Original Article
Scheduling Modeling
Ali Husseinzadeh Kashan; Saeed Afkhami; Parisa Maroofkhani
Abstract
Assembly lines are continuous systems, which are important not only in the mass production of high-quality products but also in low volume production of customized products. Nowadays because of increasing global competition and the rapid expansion of technology, assembly line balancing ...
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Assembly lines are continuous systems, which are important not only in the mass production of high-quality products but also in low volume production of customized products. Nowadays because of increasing global competition and the rapid expansion of technology, assembly line balancing has become more important. The assembly line balancing is the systematic grouping of operations and work elements as workstations with respect to the restrictions, work cycle time and other special limitations in order to minimize the number of workstations. In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, a bi-objective mathematical model have been developed. The objective functions are reducing the cycle time and also reducing the total cost. Then, since the research problem is shown to be NP-hard, NSGA II, which is a population-based algorithm, and also SPEA II are used to solve the problem. Finally, we use four criteria to analyse the results of the bi-objective model and evaluating the performance of the multi-objective evolutionary algorithms: the number of Pareto solutions, the mean distance from the ideal point, the variance index, and the quality index.
Original Article
Scheduling Modeling
Parham Soofi; Mehdi Yazdani; Maghsoud Amiri; Mohammad Amin Adibi
Abstract
Purpose: One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must ...
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Purpose: One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must be robust so that in the event of a malfunction, the job schedule works properly and deviate less from the optimal solution. The purpose of this paper is to study the DRCFJSP problem with possible scenarios of machine failure or workshop disruption.Methodology: In solving the under-studied problem, the assignment of jobs and the sequence of operations on each machine should be done in such a way that under any possible scenario, the maximum completion time is minimized so that the weight combination of system performance in average mode, system performance in worst mode, the penalty for violating the time window constraints of the due dates and the variance of the objective function value is optimal according to different scenarios. For this purpose, a Robust Scenario-Based Stochastic Programming (RSSP) model based on a mixed integer linear programming model has been presented for this problem and has been solved by Gams software for validation in small and medium-sized problems. Also, due to the Np-hard nature of this problem, a meta-heuristic method based on Genetic Algorithm (GA) is proposed for solving the large-sized problems.Findings: The results of the proposed RSSP model indicate that GAMS software is able to solve these problems up to medium sizes in an acceptable time and achieve a controlled and robust solution. Numerical results also show the proper performance of the proposed GA as an alternative to solve the RSSP model in the large-sized problems.Originality/Value: In this paper, DRCFJSP problem is studied with possible scenarios of machine failure or disruption in the workshop. Also, a robust scenario-based stochastic programming (RSSP) model according to the mixed integer linear programming formulation and a meta-heuristic Algorithm have been presented for mentioned problem in this article.
Original Article
supply chain management analyzing/modelling
Mohammad Mehdi Rahimian Asl; Mohammad Hassan Maleki
Abstract
The purpose of this paper to evaluate the level of antifragility in the supply chain of a Daroopakhsh company. To improve the company's competitive position and Confrontation to disruptions and breakdowns, the supply chain must move towards antifragility. Accordingly, the supply chain, in addition to ...
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The purpose of this paper to evaluate the level of antifragility in the supply chain of a Daroopakhsh company. To improve the company's competitive position and Confrontation to disruptions and breakdowns, the supply chain must move towards antifragility. Accordingly, the supply chain, in addition to being prepared to deal with and respond to disruptions, has the ability to recover pre-disruption conditions and create even better conditions. To move in this direction, it is necessary for decision makers to properly recognize the current position of their supply chain and make the right decisions to improve its dominance.To achieve this goal, the present study intends to determine the declining performance of this supply chain system in optimal, current and minimum conditions using Demetel technique, graph theory method and matrix approach. Finally, using the Importance-Performance analysis method, the components of supply chain are analyzed and prioritize the improvement of each factor.Based on the results, respectively, supply chain structure, improvement and recovery, learning, flexibility and innovation are in the first to fifth priority to improve the dominance structure of the company's supply chain.This research supports organizations in assessing the level of sufficiency of their supply chain and facilitates decision making. The following approach can simplify the dynamic nature of the environment for managing supply chain disruptions and even allow managers to compare different supply chains. Continuous assessment and monitoring of the level of chain volatility enables the creation of a competitive advantage to achieve greater market share even during a disruption or ongoing disruptions.
Original Article
Optimization in science and engineering
Nazila Nikdel
Abstract
Purpose: Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. An appropriate controller should be proposed to ensure effective industrial operations by satisfying desired performance ...
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Purpose: Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. An appropriate controller should be proposed to ensure effective industrial operations by satisfying desired performance indices such as high response speed, low operation time, control effort, and tracking error. Therefore, the first objective of this paper is controlling the robotic system based on fractional-order calculus so that it can control the system despite its complexity and non-linearity; The second objective is presenting the meta-heuristic algorithm "Improved Grey Wolf" to optimize the system response.Methodology: First, the mathematical model of the robot is presented based on Lagrange rules, and then the fractional-order calculus is used to design the controller. In addition, the efficiency of the grey wolf algorithm is increased with the introduction of an improved method.Findings: Different cost functions based on the main performance criteria of the robotic system are introduced, and an improved algorithm is applied. The comparison results of the proposed algorithm and other algorithms, indicate its satisfying performance. In addition, the efficiency of the fractional-order controller is compared with its integer-order counterpart, and the results show a significant improvement in system performance.Originality/Value: The proposed controller can confront the complexities and non-linearities of the system model and fulfill a satisfactory performance. In addition, inspired by the Grey Wolf algorithm, an improved optimization method is proposed that can increase the efficiency of the controlled system. Numerical results show the satisfying performances of the proposed controller and the improved optimization algorithm.
Original Article
stochastic/Probabilistic/fuzzy/dynamic modeling
Arezoo Khazaei; Parvaneh Samouei
Abstract
The newsboy problem is one of the high usage and important models in the field of inventory control. Due to the importance of the subject, this study examines the newsboy problem by considering the conditions of outsourcing and returned Products and limited capacity and single products, with the aim ...
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The newsboy problem is one of the high usage and important models in the field of inventory control. Due to the importance of the subject, this study examines the newsboy problem by considering the conditions of outsourcing and returned Products and limited capacity and single products, with the aim of determining the amount of domestic production and outsourcing so that maximize profits. In this paper, a non-linear mathematical model is presented and several numerical examples are solved, assuming the demand is discrete, and several sensitivity analyses are done to evaluate the proposed model.
Original Article
Decision based on Neural Networks/ Deep Learning
mohamad ali khatami; Mona Jahangir zade; Amir Mazyaki; seyed soheil fazeli
Abstract
Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have ...
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Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have more market share, and more money may return on investment. This article's main aim is to identify the factors affecting customer loyalty in insurance companies.Methodology: This research was quantitative, analytical-descriptive. In gathering information, Data was collected through the survey, and the findings are practical. In this way, two methods, Confirmatory Factor Analysis (CFA) and Artificial Neural Networks (ANN) were used. For localizing the factors extracted from other similar prior literature, first, the elements were examined by CFA with SMART PLS application due to some conflicts in the literature to evaluate whether each factor affects customer loyalty or not. Then, the elements were introduced to the ANN for training by this program.Findings: In this article, by using the MORGAN table, the sample size detected 384 people in 0.05 error. Questionnaires were distributed randomly between four Iranian insurance companies, ASIA insurance company, ALBORZ insurance company, and PARSIAN insurance company. Based on Confirmatory Factor Analysis, elements of commitment, perceived quality, trust, perceived value, empathy, brand image, the attraction of other alternatives, and customer satisfaction impact the customer loyalty of insurers in these companies. The cost of change, nevertheless, did not have a significant effect on customer loyalty. Then, the factors used as inputs for the multi-layer perceptron training also customer loyalty are indicated as output. The model was designed with eight inputs, 110 nodes in the hidden layer, and one output the error was E= 0.00992 and the regression = 0.98684.Originality/Value: the finding of this research is, expanding a model for predicting customer loyalty in Iranian insurance companies.
original-application paper
supply chain management analyzing/modelling
taher kouchaki tajani; Ali Mohtashami; Maghsoud Amiri; Reza Ehtesham Rasi
Abstract
Purpose: This paper aimed to design a Robust blood supply chain model that includes the stages of collection, processing and distribution of blood and blood products taking into account the lifespan and age of demand, which seeks to reduce supply chain costs and reduce the shortage and waste of blood ...
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Purpose: This paper aimed to design a Robust blood supply chain model that includes the stages of collection, processing and distribution of blood and blood products taking into account the lifespan and age of demand, which seeks to reduce supply chain costs and reduce the shortage and waste of blood products. Methodology: In this paper , MINLP method is used to model the research problem and in order to face the uncertainty in the problem parameters, the MPFRP method based on fuzzy data is proposed. The designed model was first evaluated for validation with numerical examples in small and large size and using real data in a case study in GAMS software. Findings: Using numerical examples and real data, the output indicates the performance of the proposed model. The output also had acceptable flexibility in the face of uncertainty in the parameters of the research model.Originality/Value: In this study, in order to reduce the shortage of blood products in situations where blood product is not available in the same group as the requested blood product, the ABO-RH adaptability principle has been used to replace the received demand with a compatible inventory. and also, to deal with Uncertainty in uncertain parameters in the supply chain A solution based onMixed possibilistic-flexible robust programming is proposed.
Original Article
Data Envelopment Analyses
Somayye Karimi Omshi; Sohrab Kordrostami; Alireza Amirteimoori; Armin Ghane Kanafi
Abstract
Purpose: In the most investigations of sustainability, including environmental, social and economic issues, in addition to the desirable outputs, undesirable outputs are also presented, which is an obstacle to sustainable development. In this regard, the purpose of this paper is providing an approach ...
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Purpose: In the most investigations of sustainability, including environmental, social and economic issues, in addition to the desirable outputs, undesirable outputs are also presented, which is an obstacle to sustainable development. In this regard, the purpose of this paper is providing an approach based on data envelopment analysis (DEA) with different forms of weak disposability of undesirable outputs to move towards sustainability. Methodology: Presenting a DEA-based model, the sustainability and performance of each dimension of sustainability are calculated simultaneously, while undesirable outputs are present with different forms of weak disposability. The sustainability performance of provincial gas companies is examined using the proposed technique.Findings: The results show that the proposed method in the performance analysis of sustainability and its dimensions is efficient when undesirable outputs are presented.Originality/Value: DEA provides a variety of disposability to minimize undesirable outputs and moves to optimize. In this study, an integrated approach with different forms of weak disposability is presented to analyze sustainability.
Original Article
Robust optimization
Amin Ghaseminejad; mohammad Fallah; Hamed Kazemipoor
Abstract
The present paper deals with modeling and solving a multi-objective problem of robust facility layout in conditions of uncertainty with NSGA II, MOPSO and MOGWO algorithms. Since the problem of facility layout is NP-Hard, the need to use meta-algorithms by providing a suitable chromosome to achieve near-optimal ...
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The present paper deals with modeling and solving a multi-objective problem of robust facility layout in conditions of uncertainty with NSGA II, MOPSO and MOGWO algorithms. Since the problem of facility layout is NP-Hard, the need to use meta-algorithms by providing a suitable chromosome to achieve near-optimal solutions has been investigated in this paper. The problem studied in this paper includes several departments based on 5 different aspects (minimizing the flow time between departments, maximizing the number of equipment and facilities, minimizing the distance traveled to access firefighting equipment, minimizing the distance to access Optimal climatic conditions and maximization of noisy departments from each other) should be arranged in different parts of the hall. In order to achieve the above objective functions at the same time, assigning departments to each section, equipping each section with different equipment and arranging the departments together is one of the main goals of the paper. The computational results show that GA, PSO and GWO single-objective algorithms have high efficiency in achieving the optimal value of the objective function in a much shorter time, and their multi-objective methods show the high efficiency of the NSGA II algorithm in achieving the average value of the objective function. First, second and fifth; also the efficiency of MOPSO algorithm in achieving the average number of efficient answers, and computational time and finally the efficiency of MOGWO algorithm in obtaining the average value of the third, fourth, objective function has the most expansion and metric distance. Statistical comparisons also showed a significant difference between the means of computational time. To evaluate and rank the algorithms, the TOPSIS method is used and the results show the high efficiency of the MOGWO algorithm in solving the model.
original-application paper
Strategic Planing
narjes bossaghzadeh; mahmoud moradi; mohammad tamimi
Abstract
Despite the growing importance of the role of knowledge in promoting innovation performance and maintaining competitive advantage in a highly competitive global environment, organizations face many difficulties in utilizing and developing the external knowledge flow infrastructure. Promoting organizational ...
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Despite the growing importance of the role of knowledge in promoting innovation performance and maintaining competitive advantage in a highly competitive global environment, organizations face many difficulties in utilizing and developing the external knowledge flow infrastructure. Promoting organizational learning based on absorptive capacity theory and creating exploration and exploitation structures based on organizational ambidexterity theory can be an explanation to help organizations to solve these problems. This study aims to explain the effect of knowledge absorptive capacity in achieving competitive advantage and the mediating role of organizational ambidexterity in a study in export companies. The initial model was extracted from the literature and in the qualitative stage through in-depth interviews with experts, the final conceptual model was drawn. The research questionnaire, after confirming its reliability and validity, was distributed among managers and experts of Iranian export companies by random sampling method. The statistical population of the research were 570 top Iranian export companies. In the qualitative section, sampling was performed by theoretical sampling method and 11 managers of companies with more than 5 and 10 years of experience were selected. The field of activity of selected companies included: gas and petrochemical, steel, auto parts, pipes and fittings and food. In the quantitative section, a sample of 78 companies was selected with the help of G*Power software. Qualitative data analysis was performed by ATLAS.ti and quantitative data with structural equation modeling (SEM) based on partial least squares (PLS). The results show that the absorptive capacity does not have a significant effect on the competitive advantage. Nevertheless the effect of this variable on organizational ambidexterity and the effect of organizational ambidexterity on competitive advantage is significant. Therefore, it can be said that organizational ambidexterity has been a perfect mediator in the relationship between absorptive capacity and competitive advantage. The findings of this study provide a path for export companies in order to gain a competitive advantage. Companies can facilitate the flow of external knowledge into the organization by strengthening the ambidexterious organizational structures, creating learning environments and strengthening the capacity to absorb knowledge.
Original Article
Decision based on Neural Networks/ Deep Learning
Yousef Ebrahimi; Yagoub Alavi Matin; Sahar Khoshfetrat; Hasan Refaghat
Abstract
Purpose: Banks as a service and financial economic enterprise, while accompanying the economic programs of countries, seek to benefit their stakeholders. In order to achieve this goal, they must be able to equip and allocate their resources optimally. One of the important issues is to identify the factors ...
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Purpose: Banks as a service and financial economic enterprise, while accompanying the economic programs of countries, seek to benefit their stakeholders. In order to achieve this goal, they must be able to equip and allocate their resources optimally. One of the important issues is to identify the factors affecting the absorption of resources that the purpose of this study is to provide a suitable model to identify the factors affecting the supply of resources. Methodology: To achieve the purpose of the research, by reviewing the research background, mission of the bank and the opinions of banking experts, 62 factors were presented in the form of a questionnaire. After approval by banking experts, the questionnaire was distributed to a sample of 30 employees of Tejarat Bank in Zanjan province for pre-testing. Then its reliability was tested and confirmed by Cronbach's alpha. After field collection of research data, the effective components were divided into two main groups of external and internal organizational factors. Then the factors within the organization into four subgroups; Financial, physical, service and communication and human factors were separated. Finally, the main research model was extracted using the model of unattended neural networks (self-organized maps) and the research data were analyzed.Findings: Research findings show that, From the set of factors affecting the provision of banking resources, communication and human factors had the most impact and external factors had the least impact. Also, due to the lack of similarity between the models of research input vectors, the correlation between each of the factors affecting resource equipping was not confirmed.Originality/Value: In this study, using a new approach of neural network model (self-organized mapping) to identify and weigh the factors affecting the equipping of bank resources, the findings of which help to develop the literature in the field of resource equipping.
Original Article
Game Theory
Mohamad Tabrizian; Hosein Seifi; MohamadKazem Sheikh-al-Eslami; Hamidreza Shahmirzad
Abstract
Congestion risk management is one of the most important issues in a restructured power system. The financial transmission right (FTR) is an accepted tool for transmission congestion risk management in local pricing based electricity markets. This paper uses the game theory to analyze the short-term (weekly) ...
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Congestion risk management is one of the most important issues in a restructured power system. The financial transmission right (FTR) is an accepted tool for transmission congestion risk management in local pricing based electricity markets. This paper uses the game theory to analyze the short-term (weekly) secondary FTR markets, in which the players manage the transmission risk through optimal biddings. The proposed algorithm seeks for finding the Nash equilibrium point for the FTR secondary markets. The objective function of the game is the profits of the market players; to be maximized, subject to various constraints of the secondary auction. The proposed method is applied to a test system, and satisfactory results are reported.
Original Article
Scheduling Modeling
Roja Ruhbakhsh; ٍEsmaeil Mehdizadeh; Mohammad Amin Adibi
Abstract
Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment are ...
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Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment are always ignored. Hence, in this paper, these assumptions are discussed and the results are reviewed. In this paper, the aim is presenting and solving a multi objective mathematical model for solving hybrid flow shop scheduling problem with lot-streaming, setup time and transportation time. At first, a multi objective mathematical programming model is presented for solving the problem. Then, by weighting method, the multi objective model convert to single objective model and GAMS software is used to solve the small size problems to show the performance of the mathematical model. Inspired by previous studies, two multi objective meta heuristic algorithms based on the genetic algorithm are used to solve the large-scale problems. To validate the proposed model, a sample example is solved by using GAMS software. To illustrate the performance of the proposed meta heuristic algorithms the obtained results of the algorithms compared with GAMS outputs in single mode. The obtained results show the performance of the mathematical model. Then, two proposed algorithms are used to solve the large-scale problems. For this purpose, 30 instance problems are randomly generated and six indicators are used to compare the algorithms. After performing the experiments and comparing the algorithms with each other, the results show NRGA algorithm performs better than NSGAII at least in three indicators. In this paper, The sequence-dependent setup time and the transportation time constraints between consecutive stages are considered for solving a multi objective mathematical model with the aim of minimizing the makespan and total tardiness.
original-application paper
Multi-Attribute Decision Making
Mojtaba Movahedi; Mahdi Homayounfar; Mehdi Fadaei; Mansour Soufi
Abstract
Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm ...
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Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm for clustering companies listed on the Tehran Stock Exchange in the field of finance from It has used different clustering algorithms and evaluated the validity of these algorithms and selected the best algorithm.Methodology: This research is applied in terms of purpose and descriptive in terms of implementation method and is of quantitative type (mathematical modeling). The statistical population of the research to test the proposed model includes 403 companies listed on the Tehran Stock Exchange in 2019, whose performance has been evaluated based on four financial criteria.Findings: After clustering the surveyed companies by five clustering algorithms K-means, EM, COBWEB, Density-based algorithm and Ward method, from seven indicators RS, DB, Dun, SD, Purity, Entropy and Time to evaluate clustering algorithms were used. Finally, the final performance of the algorithms used was analyzed based on TOPSIS, VICOR and DEA methods. Based on the results, the K-means method has a better performance in clustering companies based on financial data sets than other methods used.Originality/Value: Since no clustering algorithm can have the best performance in all measurements for each data set, this study uses a combination of multiple criteria to analyze data clustering algorithms related to the field of financial performance appraisal. Companies have provided suggestions and the results of this study have been used effectively for investors in the field of finance, which leads to the optimal choice of investment portfolio.
Original Article
stochastic/Probabilistic/fuzzy/dynamic modeling
Akbar Dehghan Nezhad; Nasim Daryani
Abstract
In Islamic Architecture, one of the most popular ways for building a dome was using arches. So, if we want to investigate the construction of a dome of a shrine or a masque, we should first investigate the generating arch of it. In architectural manner, a dome is built by rotating the generating arc ...
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In Islamic Architecture, one of the most popular ways for building a dome was using arches. So, if we want to investigate the construction of a dome of a shrine or a masque, we should first investigate the generating arch of it. In architectural manner, a dome is built by rotating the generating arc around the vertical axis, which reminds us of the surface of revolution and the generating curve in differential geometry, as well. In this case, the generating curve will be derived from the generating arch. For this purpose, keeping the way of its drawing in mind, we will parameterize half of the arch according to the clear span of it in xoz plane, and then by rotating this curve around the z axis, we will have the whole surface of revolution. In this article we are going to investigate domes in a differential geometrical manner and as a surface of revolution. We can show the beautiful relation between the architectural and mathematical point of view in studying domes, in a way that is interesting for both architects and mathematicians. The methodological approach of this study is quantitative research which includes calculations corresponding to some kind of domes and it is based on a descriptive research strategy. As we know, there are a lot of kinds of arches but we are going to study just four of the most important ones. Like Shabdari, Shakhbozi, Panj ohaft and Paay to paay arches. We will define some of the differential geometry tools, which we will use in our article and then bring some tables, which have the information we need. At last, we will apply our calculations to the dome of Jome mosque in Ardabil.
Original Article
Data mining and related topics
Saman Haratizadeh; Fatemeh Rezaee
Abstract
Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in stock market investment. In many studies, advanced machine learning methods have been used to select stocks for the portfolio, but machine ...
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Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in stock market investment. In many studies, advanced machine learning methods have been used to select stocks for the portfolio, but machine learning capacities have received less attention in the process of optimal capital allocation among portfolio assets. In most of the existing techniques, equal weights are assigned to portfolio stocks or traditional risk assessment methods are used to divide capital among portfolio stocks. The purpose of this paper is to show how machine learning can be used to create a more effective mechanism for estimating performance, which leads to a more efficient allocation of capital to portfolio stocks.
Methodology: Our proposed framework, called Per-Learner, uses two predictive models based on machine learning. In the first step stocks historical information is used in a return forecasting model, then based on the predicted returns, the appropriate stocks for the portfolio are selected. In the second step, a separate forecasting model predicts portfolio returns by taking into account both the forecasted returns in the first model and the expected risk of the stocks. At the end, appropriate weight for each asset is set based on the predicted returns for numerous random portfolios.
Findings: The comparison of the cumulative returns of the portfolios suggested by this model and the state of the art baselines shows the superiority of the proposed model.
Originality/Value: In this paper we introduce a novel machine learning approach to select the appropriate stocks for the portfolio and allocate the capital among the candidate stocks, This algorithm can be used for automatic trading in the market or applied by investors as a portfolio recommendation technique,
original-application paper
stochastic/Probabilistic/fuzzy/dynamic modeling
morteza abdolhosseini
Abstract
Purpose: Coronavirus (Covid-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry ...
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Purpose: Coronavirus (Covid-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry and exit, increasing the capacity of hospital beds, setting up round-the-clock vaccination centers, requiring the use of masks in public places, and observing social distances. Therefore, predicting such cases will reduce the number of corona cases and therefore reduce the mortality rate
Methodology: In this paper, using the singular spectrum analysis (SSA) algorithm, the sixth peak of coronavirus in Iran is predicted by considering the current situation. To improve the grouping process of the SSA algorithm, Eigenvalues have been selected in the optimization process, so that the predicted time series of which has been significantly improved according to the error-index.
Findings: Comparing the proposed method with other forecasting methods include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), TBATS, and Neural Network Autoregression (NNAR), it is observed that the forecasting error is acceptable and the SSA method can be used for forecasting.
Originality/Value: This article predicts a new case of Covid-19 using efficient method SSA and the presented results confirm the effectiveness of the proposed method.
Original Article
Location Modeling
Alireza Roshani; Mohammad Reza Gholamian; Mahsa Arabi
Abstract
Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting ...
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Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resiliencetoward any disruptive events seem to be necessary.
Methodology In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a two-stage stochastic programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one.
Findings: In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model.
Originality/Value: This paper presents a multi-objective linear mathematical model for designing the pharmaceutical supply chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.
Original Article
Multi-Attribute Decision Making
Sahar Esmaeili; Kiamars Fathi Hafshejani; Changiz Valmohammadi; Ali Akbar Haddadi Harandi
Abstract
Purpose: This article seeks to provide a flexible structure for the learning organization tailored to the conditions of Iranian schools. Using this structure, schools, as an educational organization, facilitate innovation and effectiveness in the face of an ever-changing environment. Also, teaching ...
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Purpose: This article seeks to provide a flexible structure for the learning organization tailored to the conditions of Iranian schools. Using this structure, schools, as an educational organization, facilitate innovation and effectiveness in the face of an ever-changing environment. Also, teaching human values and principles of education help students become people who live in a healthy and civilized way in a world rich in future technologies.
Methodology: This paper uses multi-criteria decision-making methods and fuzzy techniques such as fuzzy Delphi and DEMATEL and ANP techniques to provide an executive and operational framework in school learning organizations.
Findings: The results show the structures of the learning organization, the skills of the learning organization, and the technologies of the learning organization as the main criteria of the learning organization in Iranian schools, respectively. Also, reinforcing leadership sub-criteria, knowledge management technology, personal abilities, and subjective models with the nature of cause play a crucial role in forming learning organizations in schools.
Originality/Value: Researchers have identified the way for Iran to achieve the economic goals envisaged in the Iran Vision 2025 document, the transformation of Iranian organizations into learning organizations. However, the study of databases such as Irandak, scientific information of Jihad Daneshgahi, and the citation database of sciences of the Islamic world shows that limited efforts have been made in this direction, especially in schools in Iran. Without focusing on why and what the learning organization is, this article, using the dimensions and criteria introduced for the learning organization, points out how to provide a flexible structure for the learning organization appropriate to the conditions of Iranian schools.
Original Article
Forecasting Models/ Time Series
Shirin Shoaee; Mohammad Mehdi Gholi Keshmarzi
Abstract
Purpose: Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore, the accuracy of mathematical models in predicting ...
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Purpose: Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore, the accuracy of mathematical models in predicting mortality rates is an important challenge. The purpose of this study is to generalize static stochastic mortality models to dynamic stochastic mortality models and to predict mortality rates based on the generalization of stochastic mortality models by the Cox-Ingersoll-Ross (CIR) process and to compare the results with each other.
Methodology: In this research, two suggestions are presented: The first idea is to provide a dynamic correction method to increase the prediction accuracy using the CIR process and the second idea is to examine the out-of-sample validation method.
Findings: In this study, using the out-of-sample validation method, the force of mortality from the best models selected from the two famous mortality model families (Lee-Carter and Cairns, Blake and Dowd) is compared with the results of the generalized model. After estimating the parameters of the studied models and calculating the prediction of the mortality rates, by calculating the mean absolute error and root mean squares error of prediction, it is determined that the generalization of stochastic mortality models by the CIR process performs much better than static mortality models. The Bayesian information criterion also indicates that the use of generalized stochastic mortality models is justified.
Originality/Value: In this study, stochastic mortality index models, which include Lee-Carter and Cairns-Blake-Dowd family models, are used and generalized by the CIR process. In this regard, human mortality database data is used. But there is no information about our country in this database. Because the French mortality pattern is very close to the Iranian pattern and the life tables of this country (TD 88-90) are used in Iranian insurance applications, the crude death rate of French men in the years 1900-2018 on the ages of 18, 40 and 65 years is used. Using these data and the backtesting method, static mortality models and generalized models with the CIR process are compared.
original-application paper
Fuzzy Optimization
Malihe Niksirat
Abstract
Purpose: During the Corona virus epidemic and in order to comply with the rules of social distancing, public transport operators have to operate with less capacity. Because demand may be overcapacity in different areas at different times of the day, drivers are forced to refrain from serving passengers ...
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Purpose: During the Corona virus epidemic and in order to comply with the rules of social distancing, public transport operators have to operate with less capacity. Because demand may be overcapacity in different areas at different times of the day, drivers are forced to refrain from serving passengers at certain stations to avoid overcrowding.
Methodology: The purpose of this paper is to develop decision support tools to prevent congestion of vehicles. Also, in order to consider the real conditions, two types of fuzzy and scenario-based uncertainty are considered. A dynamic nonlinear integer programming model is introduced to obtain the optimal service pattern for vehicles that are ready to be dispatched. To overcome the combined uncertainty of the problem, possibility theory has been proposed as a new fuzzy stochastic programming approach that has significant advantages.
Findings: The model is clearly strikes a balance between observing social distancing by reducing the capacity of vehicles and reducing the waiting time of passengers who lose services. Numerical examples are provided to illustrate the proposed concepts and model and to compare the results.
Originality/Value: The proposed decision support model can suggest service patterns for different lines service and can assess public transport operators to evaluate the advantages and disadvantages of implementing epidemic-based service patterns due to operational advances and demand level of travelers.
original-application paper
Linear Optimization
Younes nozarpour; sayyed mohammad reza davoodi; Mahdi fadaee
Abstract
Purpose: The multi-period portfolio after closing, can be reviewed and modified at regular intervals. The philosophy behind using multi-period stock portfolio models is that investors often have a multi-period view of future asset changes that can be derived from technical, fundamental, or statistical ...
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Purpose: The multi-period portfolio after closing, can be reviewed and modified at regular intervals. The philosophy behind using multi-period stock portfolio models is that investors often have a multi-period view of future asset changes that can be derived from technical, fundamental, or statistical models. In conventional multi-period portfolio models, it is assumed that the forecast and correction horizons are the same for all assets. However, one asset may be predicted for the one-month horizon and another for the two-month horizon, and may be revised in the future in these periods. The purpose of this study is to present a multi-period stock portfolio model in which assets have different time horizons for correction or an asset can not be traded for the first few periods and then enter the correction cycle.Methodology: In this model, uncertainty variables defined on an uncertainty space are used to describe the returns. The objective function of the model is to maximize the ultimate wealth of the portfolio, and to limit portfolio risk, a constraint is used in which the uncertainty of the ultimate wealth below a threshold is controlled at a confidence level. To find the optimal solution, the model is converted into a form of linear programming by a change of variable method.Findings: After explaining how to model the research portfolio, using a numerical example the model is implemented on a portfolio with 6 stocks and 4 monthly time steps on the Tehran Stock Exchange.Originality/Value: The present study extends the uncertain multi-period portfolio to a multi-period portfolio with different time horizons and offers an optimal solution through linear programming. In the research stock portfolio, transaction costs are also considered to be more in line with the real conditions.
Original Article
Forecasting Models/ Time Series
Sepideh Etemadi; Mehdi Khashei
Abstract
Purpose: The purpose of this paper is to present a new methodology for statistical modeling, which, unlike all commonly developed models and algorithms, maximizes the reliability of the results instead of the resulting accuracy. Accordingly, a new class of statistical modeling approaches has been developed ...
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Purpose: The purpose of this paper is to present a new methodology for statistical modeling, which, unlike all commonly developed models and algorithms, maximizes the reliability of the results instead of the resulting accuracy. Accordingly, a new class of statistical modeling approaches has been developed by replacing conventional processes with the proposed process.
Methodology: The multiple linear regression method has been selected to implement the proposed methodology in this paper. To comprehensively evaluate the performance of the proposed regression model, 10 standard datasets from the literature on statistical modeling have been considered.
Findings: Overall, the results show that in 65% of the studied data sets, the proposed model can generalize more than the usual multiple linear regression. The proposed regression model, on average, has been able to improve the accuracy of the modeling by 5.571% and 6.466% in mean absolute error and mean square error, respectively, compared to its classic version. These results clearly show the significant effect of reliability of the results on the degree of generalizability, which is basically not considered in the usual statistical modeling processes.
Originality/Value: Statistical modeling is one of the most important tools for simulating real-world systems and data sets that are often used to make decisions in a wide range of applications. Several different approaches have been developed in the literature with different features to cover real-world issues with the desired accuracy. However such methods follow a similar concept and idea in the modeling process. The performance basis in all conventional statistical modeling approaches is based on the assumption that maximum accuracy in experimental and inaccessible data will be obtained from models with minimization of error in training data. Although this is a logical and standard procedure in traditional statistical modeling spaces, it is not the unique way to achieve maximum generalizability. In other words, the generalizability of the model simultaneously depends on the model's accuracy and the level of results' reliability. In this paper, a new methodology for statistical modeling is presented, which, unlike all commonly developed models and algorithms, maximizes the reliability of the results instead of the resulting accuracy.
Original Article
Robust optimization
shima Roosta; Seyed Milad Mirnajafi Zadeh; Hamid Bazargan Harandi
Abstract
Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental ...
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Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, the use of green logistics to mitigate these impacts has become increasingly important. To copmpensative a gap in the literature, this paper presents a robust bi-objective mixed-integer linear programming (MILP) model for the green capacitated location-routing problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes. The final result of this Robust Multi-Objective Model is to set up the depots and select the routes that offer the highest reliability (Maximizing network service) while imposing the lowest cost and environmental pollution.
The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model. from your own perspective although you may draw arguments from other research work to back up your arguments.
Original Article
Game Theory
Hamed Jafari
Abstract
Purpose: Consider products such as disposable tableware manufacturerd from plastic waste. To produce these products, plastic waste is collected and recycled, and then is reused. In this research, a supply chain including waste depot, recycler, and manufacturer is considered in which plastic waste is ...
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Purpose: Consider products such as disposable tableware manufacturerd from plastic waste. To produce these products, plastic waste is collected and recycled, and then is reused. In this research, a supply chain including waste depot, recycler, and manufacturer is considered in which plastic waste is reused in order to manufacture a product. Under this supply chain, the waste depot collects non-recycled plastic waste and the recycler recycles it. Then, the manufacturer uses recycled plastic waste to produce final product and to meet customer demand. In this structure, the waste depot, recycler, and manufacturer set the price of non-recycled plastic waste, the price of recycled plastic waste, and the price of final product, respectively.
Methodology: The game theory is used to make the decisions under the considered supply chain. It is assumed that the decision power of the manufacturer is more than of the waste depot and recycler. In this setting, to specify the prices adopted by the members, Stackelberg game-theoretic model is established in which the manufacturer is the leader and the waste depot and recycler are the followers.
Findings: Results indicate that decision power of the waste depot and recycler has no effect on the price and demand of final product. The profits allocated to the manufacturer are the same when decision powers of the waste depot and recycler are different. Moreover, more the price elasticity of the demand for final product leads to lower profits for the members.
Originality/Value: Using plastic waste is an effective approach to sustain environment and reduce pollution. In this research, plastic waste is used to manufacture products such as disposable tableware in a supply chain including manufacturer, recycler, and waste depot. The game theory approach is also applied to make decisions. To our knowledge, the idea of applying the game theory under the considered supply chain to use plastic waste for manufacturing disposable tableware has been raised for the first time in the literature.
Original Article
simulation techniques and expert systems
Seyedeh Raahil Mousavi; Seyed Esmaeil Najafi; Mohammad Mehdi Sepehri
Abstract
Purpose: High efficiency in the operating room may significantly improve the overall performance of the hospital and the service quality provided to patients. The operating room is the de facto financial hub of the hospital and maximizing its efficiency may lead to considerable improvements. Studies ...
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Purpose: High efficiency in the operating room may significantly improve the overall performance of the hospital and the service quality provided to patients. The operating room is the de facto financial hub of the hospital and maximizing its efficiency may lead to considerable improvements. Studies have shown that operating room costs are high and, conversely, revenue is very low, which doubles the need for the present study.The purpose of this article is to increase the efficiency of the operating room and reduce the lost time by optimizing the patient flow.
Methodology: In this study, we use agent-based simulation to simulate patient care in the operating room. To reduce the length of the patient's stay in the operating room, it defines five new indicators and carefully selects the optimal amount of agents at the same time. After performing the required validations, a number of improvement scenarios were developed and evaluated.
Findings: A hybrid scenario including modifications to the referral time of the patient by the surgeon, transfer time of the surgical set and supplies to the operating room, and the timing of anesthesia proved to have the most positive impact on the criteria i.e. activities, reducing the average length of stay (LOS) by 9.69 minutes. The second-most effective scenario involved modifying the referral time of the patient by the surgeon, reduced the LOS by 7.31 minutes.
Originality/Value: Through this research, it became apparent that minimizing the patients' LOS improves the efficiency of the operating room as it helps reduce the overall idle time and increases the number of operations carried out in each shift. Making time even for one additional operation per day significantly increases the operating room income. Moreover, a shorter LOS means less fatigue for the medical staff and reduces the total cost of running the operating room by reducing the staff's overtime hours.
Original Article
Fuzzy Optimization
Gohar shakouri; Seyed Hadi Nassery; Mohammad Mahdi Paydar
Abstract
Purpose: The transportation problem, as one of the most important and most practical models related to linear programming, has always been of interest to researchers. Due to the lack of accurate information, variable economic conditions, uncontrollable factors and especially variable conditions of available ...
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Purpose: The transportation problem, as one of the most important and most practical models related to linear programming, has always been of interest to researchers. Due to the lack of accurate information, variable economic conditions, uncontrollable factors and especially variable conditions of available resources, to adapt to the real conditions, we are faced with a kind of uncertainty, both flexibility in constraints and fuzzy nature of the parameters. Hence, one method to express the conditions of this modeling is to use flexible fuzzy numbers that make it more adaptable to real conditions.
Methodology: In this research, after reviewing the research literature, the transportation problem is modeled by considering the flexible-interval fuzzy supply constraint. Then, for the solution process, a flexible fuzzy approach to the proposed model is studied.
Findings: Numerical example analysis indicates that parametric linear programming approach offers a reliable design so that the decision maker can obtain a better selection of resources with the most satisfaction.
Originality/Value: In this research, parametric approach with flexible relationship is discussed and based on the research results, the solution is obtained with the most satisfaction in constraints.
original-application paper
Multi-Attribute Decision Making
Ahmad Negravi; Omid Titidezh
Abstract
Purpose: This study seeks to assess the significant effects of blockchain, as a new decentralized technology, in waste reduction and performance improvement of freight transportation management systems (FTMS).
Methodology: The network analysis technique is used as one of the most valid multi-criteria ...
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Purpose: This study seeks to assess the significant effects of blockchain, as a new decentralized technology, in waste reduction and performance improvement of freight transportation management systems (FTMS).
Methodology: The network analysis technique is used as one of the most valid multi-criteria decision-making models. We developed this model in four levels with four main criteria of performance wastes (including cost, time, trust and security and safety), and fourteen sub-criteria, considering the blockchain technologies in four areas of digital infrastructure, visibility, transparency, and smart contracts.
Findings: Findings indicate that each of these technologies, individually and collectively, has reduced the waste of road freight management systems. The digital infrastructure technology reduced time and cost waste by 46.44% and 53.56%, respectively. Visibility has reduced 23.99%, 22.30%, and 53.71% of the wastes of cost, time, and safety, respectively. The transparency technology has influenced time and trust and security by 22.63% and 77.37%, respectively, and smart contracts affected the two above waste categories by 88.81% and 11.19%, respectively. The most effective wastes were identified as transportation costs, control costs, transparency in providing information, control of product conditions, and driver monitoring.
Originality/Value: This study identified the most crucial road freight transport wastes and made it possible to determine priorities in various areas of blockchain technologies for future investment to improve the FTMS performance, and the findings of this study might be a starting point for future studies in this context.
original-application paper
Abbas Nosouhi; Akbar Etebarian; Mehraban Hadi Peykani
Abstract
Abstract
Purpose: This study is conducted to design a model of mutual expectations in the relationship between the individual and the organization (Person as a client) in Isfahan's government organizations and service providers based on the phenomenological approach.
Methodology: The purpose of ...
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Abstract
Purpose: This study is conducted to design a model of mutual expectations in the relationship between the individual and the organization (Person as a client) in Isfahan's government organizations and service providers based on the phenomenological approach.
Methodology: The purpose of this study is practical, and information collection is qualitative. The trio approach of Rao and Perry is used for the reliability of the results and the validity of the research. In addition, to determine the validity of the questionnaire, the content validity ratio of Lawshe is used. The modified aesthetic-Klaizi method was also used to analyze the information. Seventeen experts and 19 managers of government and service organizations and 35 clients of these organizations were the statistical populations of the study. They were selected by purposive sampling method, and data were collected through semi-structured interviews. In quantitative research, the DEMATEL technique investigated the relationship between affecting and being affected between components.One hundred managers and experts of government and service organizations and 100 of their clients were purposefully selected using the pairwise comparison test method and identifying in the quantitative section.
Findings: The findings in Demetel technique showed that the most impact was related to the factor of capable and knowledgeable client and the most impact was related to the factor of respect for human dignity of employees.Also the findings show the most gaps are in the component of the rule of law and avoidance of illegal methods, and the minor gaps are in the component of participation in client-related organizational systems by using the questionnaire to collect the required information. In the organization questionnaire, the respondents claim that the client's supply to the organization is much less than the organization's demand from the client.
Originality/Value: The results of this research can be effective in the theoretical development of the relationship between governmental organizations and service providers and clients, and policy makers and managers can create intelligent structures to reduce the expectations of clients and employees of governmental organizations and provide more favorable conditions for service and receiving services.
Original Article
Robust optimization
Mohamad A. Movafaghpour
Abstract
In many Real-world optimization problems, we are facing uncertainties in parameters describing the problem. In general, as a simplifying assumption, uncertainty is ignored. In the school bus routing problem there are uncertain parameters that are assumed to have deterministic values. As a result of this ...
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In many Real-world optimization problems, we are facing uncertainties in parameters describing the problem. In general, as a simplifying assumption, uncertainty is ignored. In the school bus routing problem there are uncertain parameters that are assumed to have deterministic values. As a result of this simplifying assumption, the obtained solutions may be mismatched with the real world. This issue arose by violating some hard constraints. In this research, a mixed linear integer programming for school bus routing with mixed loading by using a heterogeneous fleet is presented. The uncertainty of travel times is modeled as interval numbers. We propose a heuristic algorithm to generate extreme scenarios. Each scenario is generated in order to make the last found optimal solution into an infeasible one as much as possible. Experimental results show that deploying this novel algorithm for generating extreme scenarios, efficiently produces diverse scenarios. After the scenario generation algorithm is converged, the intersection of the feasible optimal solutions under diverse scenarios is extracted as robust sub-tours or robust trips. It is the first time to apply the notions of robust optimization using the extreme scenarios generation scheme. At each iteration of the extreme scenarios generation, the most conflicting scenario against a given optimum solution is generated. The main advantage of this method over other present robust optimization methods is its emphasis on maintaining the feasibility of the optimal solution when dealing with the most diverse set of uncertainty scenarios while keeping the computational effort needed as low as desired.
Original Article
stochastic/Probabilistic/fuzzy/dynamic modeling
Alireza Hamidieh; maryam besharat meymandi
Abstract
Purpose: The main challenge in devastating events such as the Kermanshah earthquake is the optimal location of humanitarian distribution centers, which plays an effective role in allocating relief shipments to demand centers. Therefore, balancing the complexity of the issue and the uncertainty with the ...
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Purpose: The main challenge in devastating events such as the Kermanshah earthquake is the optimal location of humanitarian distribution centers, which plays an effective role in allocating relief shipments to demand centers. Therefore, balancing the complexity of the issue and the uncertainty with the constraints on aid scheduling and resource management is critical. In this regard, the location-allocation model has been developed by considering the reliability of the distribution hub set, which provides the possibility of dealing with impending disruptions after the crisis. The proposed model divides the affected area into several layers and simultaneously considers the capacity of the relief fleet. And offers a combination of fuzzy programming with chance constraints and robust programming to deal with parametric uncertainty.
Methodology: First, the effective factors and parameters of crisis management in locating relief centers under the crisis were identified. Subsequently mathematical modeling was distributed by considering the reliability of the earthquake crisis distribution hub and relief according to the topography of the study area. Next, the Epsilon constraint method was applied to cover the multi-objective optimization problem and to determine non-dominant Pareto optimal solutions, and the mathematical combination of Possibilistic-Robust programming was used to deal with uncertainty.
Findings: The results indicate that the approach of using temporary distribution centers (TDC) based on the stratification of the geographical area of the affected area along with combined transport is effective in reducing logistics costs and delivery time of relief goods. The reliability policy used in the distribution hub set has improved the confidence capability of the humanitarian distribution network. Then, by studying the Kermanshah earthquake disaster and determining the critical parameters of the crisis, was confirmed the performance and efficiency of the proposed model.
Originality/Value: The present study, as a decision support system, facilitates relief in the regions of the country in the event of a crisis. Predicting a reliable distribution hub set with a combined transportation approach appropriate to the topography of the region ensures the optimal implementation of relief operations. Also, the developed model is operational in the areas at risk of the country.
original-application paper
Data Envelopment Analyses
Maasoumeh Mirsadeghpour; masoud sanei; Ghasem Tohidi; Shokoofeh Banihashemi; navideh Modarresi
Abstract
Purpose: Portfolio optimization is a selection of assets with the lowest risk and highest return. Asset performance evaluation is a useful way to choose assets and construct a profitable portfolio. For this purpose, the non-parametric Data Envelopment Analysis (DEA) method is used which is a suitable ...
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Purpose: Portfolio optimization is a selection of assets with the lowest risk and highest return. Asset performance evaluation is a useful way to choose assets and construct a profitable portfolio. For this purpose, the non-parametric Data Envelopment Analysis (DEA) method is used which is a suitable tool for measuring performance. By the fact that stock returns are not normally distributed and usually exhibits skewness, kurtosis and heavy-tails, which definitely affects the assets performance, we have to consider the characteristics of the returns distribution. In the proposed model, we apply the Variance Gamma process which covers the skewness and kurtosis of returns. As a result, we construct a portfolio by selecting assets which their performance is more realistic.
Methodology: In the introduced model, the only input of the model is Conditional Value at Risk, and the mean return and Sharpe index are as the model’s outputs. Since the outputs can be negative, the model is inspired by VRM in the output-oriented DEA model which deals with negative values. As the returns on stock are Variance Gamma distributed, its parameters are simulated by moments estimation method, and then the process factors are simulated by Monte Carlo technique. Finally, the scenarios of returns are obtained, and the assets performance are evaluated.
Findings: The correctness of the model is investigated on evaluating the relative efficiency of 7 companies from different industries in Iran Stock market. The results show that by considering the returns distribution characteristics, the input and outputs values of the model are estimated more realistically and more reliable results can be obtained, thus a profitable portfolio can be constructed.
Originality/Value: Evaluation the assets performance by taking into account the returns distribution characteristics leads realistic results.
Original Article
Optimization in science and engineering
Zeynab Rashidi; Zahra Rashidi
Abstract
Purpose: The issue of allocating space to academic needs is one of the complex optimization issues that distributes a limited set of educational and research needs to a set of resources with a set of constraints. Due to the complexity of this problem, several techniques based on innovative methods have ...
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Purpose: The issue of allocating space to academic needs is one of the complex optimization issues that distributes a limited set of educational and research needs to a set of resources with a set of constraints. Due to the complexity of this problem, several techniques based on innovative methods have been proposed. In this paper, a mathematical model of integer programming is presented to formulate this problem.
Methodology: To solve the model, the gradient descent method is used and its parameters are adjusted. To evaluate the proposed model and solution, the data and facilities of one of the fledgling faculties at Allameh Tabatabai University in Tehran are tested. There are 11 requirements and 18 allocable spaces in this faculty and therefore there are 198 binary decision variables, in the model. In experiments, several scenarios are created and the results of each scenario are compared.
Findings: The proposed model and solution is a general method and can be used for other faculties and universities that face space constraints.
original-application paper
Decision based on Soft Computing
Hamzeh Amin-Tahmasbi; Mahdi, Alireza
Abstract
Purpose: Deciding to choose stocks has always been one of the concerns of investors. The main purpose of this study is to identify the factors affecting the decision-making and ranking of stocks in the three metal, chemical and pharmaceutical industries of the stock exchange according to the importance ...
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Purpose: Deciding to choose stocks has always been one of the concerns of investors. The main purpose of this study is to identify the factors affecting the decision-making and ranking of stocks in the three metal, chemical and pharmaceutical industries of the stock exchange according to the importance of these industries.
Methodology: The statistical sample of this research includes the shares of 84 companies in these three industries, which have been examined based on the data of 2021. First, stock rating factors were extracted by reviewing the research background. For screening these factors were used by experts in this field and after screening, the final factors were selected. Weighting and prioritization of these factors were done using fuzzy Savara method. According to the weight of the factors obtained from the fuzzy ride method and the use of companies' financial information, the Cocoso method was used to rank the target stocks.
Findings: The results showed that price-income ratio, operating profit margin and percentage of return on capital are the most important criteria for experts. Also, Fasabezvar, Fasmin and Vetoka from the metal group, Vepakhsh, Desobha and Depars from the pharmaceutical and shoyande, Shepdis and Shefan from the chemical group won the first and third places.
Originality/Value: In the last four years, various works have been done in this field, but less work has paid attention to the uncertainty in the opinions of experts. Regarding other innovations of this article, it can be pointed out that the three industries of metal, chemical and pharmaceutical, due to the importance of these industries, have not been specifically studied. Regarding the method of prioritizing criteria and stocks, less attention has been paid to new decision-making methods and uncertainty in decisions. Therefore, using new methods, the importance of criteria can be determined with higher accuracy and better return on investment can be obtained.
original-application paper
Multi-Attribute Decision Making
Maryam Musavi-Nogholi; Mohammad Taghi Rezvan
Abstract
Purpose: The purpose is to identify and analyze the upcoming scenarios of the aluminum industry and finally, its market analysis, to draw a correct vision of the future and choose the appropriate strategy in this industry.
Methodology: Firstly, the key variables affecting the development of the aluminum ...
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Purpose: The purpose is to identify and analyze the upcoming scenarios of the aluminum industry and finally, its market analysis, to draw a correct vision of the future and choose the appropriate strategy in this industry.
Methodology: Firstly, the key variables affecting the development of the aluminum industry are identified through interviews with experts and specialists in the industry, and the probability of variables is determined by completing a questionnaire, and then based on the extracted variables, specific scenarios are developed. The interactions of the variables will be determined using the fuzzy DEMATEL method and the matrix obtained from this method will be part of the supramatrix of the Analysis Network Process. To analyze the aluminum industry market, game theory will be used in two forms, considering competition between players and considering the possibility of cooperation and alliance between players, to find balancing points between existing situations, strategic options of players and scenarios of this industry.
Findings: The results indicate that the possibility of "increasing inflation" is the most likely and "increasing investment in the country" is the least likely variable, and "lifting sanctions" is the most influential variable and "production and export" is the most impressive variable. Five scenarios as "Iran's difficult scenario in the ordinary world", "Iran's catastrophic scenario in the ordinary world space", "Disaster scenario for Iran in the difficult global space", "Iran's developing scenario in the ordinary world space", and "Iran's favorable space scenario in the difficult world space "were ranked. Three main players, namely policymakers, smalters and downstream, were identified and 13 strategic options in the form of investment, pricing, exports, energy rates in different directions and shapes were extracted. The preferences of the players in different scenarios are explained based on the 12 modes and for each scenario based on different equilibrium points, selected modes are extracted.
Originality/Value: Futurology has been the main subject of this paper, which, by analyzing the aluminum industry and the activities of the main players active in this industry, paints a picture of the future of this industry, including the entire value chain in a five-year horizon.
Original Article
Kasra Ghafori
Abstract
In general, efficiency is a criterion for evaluating the performance of a decision unit from different dimensions. Data envelopment analysis is a mathematical programming method for evaluating the performance of decision-making units. The purpose of this study is to measure the financial efficiency of ...
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In general, efficiency is a criterion for evaluating the performance of a decision unit from different dimensions. Data envelopment analysis is a mathematical programming method for evaluating the performance of decision-making units. The purpose of this study is to measure the financial efficiency of firms by considering both incoming assets and financing. In this research, a new method called the three-dimensional model of data envelopment analysis was introduced and performance analysis was done on 10 active firms in the field of the steel industry in Iran during 5 years, from 2016 to 2021. The results showed that there are several firms that have good performance in managing incoming assets but are inefficient in terms of financing. At the same time, there are firms that have poor management performance compared to inputs but they are efficient in terms of financing. Therefore, when analyzing a firm's performance, an indicator is needed that considers both inputs and financing at the same time. According to this, we proposed a new measurement method and had analyzed the current financial situation of each decision-making unit through the method of return to scale and a path has been determined for financial improvement. Attention to the effect of negative and destructive factors such as borrowings and debts of the decision-making unit in data envelopment analysis has been the key and different aspect of this study, compared to other previous studies. According to the literature review and using the redesigned DEA model has not been considered by Iranian researchers and due to a new approach to data envelopment analysis, our approach has distinguished itself from the previous works.
original-application paper
Multi-Attribute Decision Making
Seyed Fakhreddin, Fakhrhosseini; Meysam Kaviani
Abstract
Purpose: The main objective of this study is to rank methods of improving debt-asset management at branches of Bank Sepah in Tehran. Methodology: Questionnaires were the tool to collect data, and statistical sample is 146 managers and experts of Bank Sepah in Tehran that have been selected by the simple ...
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Purpose: The main objective of this study is to rank methods of improving debt-asset management at branches of Bank Sepah in Tehran. Methodology: Questionnaires were the tool to collect data, and statistical sample is 146 managers and experts of Bank Sepah in Tehran that have been selected by the simple random sampling method. In this study, by using multiple criteria decision-making techniques (MCDM) of fuzzy TOPSIS, we have ranked the goals of debt asset management in Bank Sepah. Findings: Based on the results, among the main criteria for Asset and liability management (ALM) goals, “the risk management of interest rate” with a weight of 3.83 is at the first priority, then the “maintenance of adequate capital” with a weight of 3.67 is in the second place and then “liquidity risk management” with a weight of 3.41 is in the third priority. Also, according to Friedman test results; there are differences between the achievements for each of the major debt-asset management in Bank Sepah in Tehran.Originality/Value: This study is a mixed method ((Delphi (qualitative) and survey (quantitative)) in terms of performance and in terms of data collection. By using multiple criteria decision-making techniques, we have ranked the major objectives of asset-debt management in Bank Sepah. In addition, the results could be used in the planning process of banks and financial institutions.
Original Article
Game Theory
Hamed Jafari
Abstract
Purpose: In this research, a recyclable waste is used to manufacture a specific product. For this reason, a supply chain is considered containing manufacturer, recycler, and waste warehouse. First, the customers’ demand for the considered product is determined based on its price. Then, the manufacturer ...
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Purpose: In this research, a recyclable waste is used to manufacture a specific product. For this reason, a supply chain is considered containing manufacturer, recycler, and waste warehouse. First, the customers’ demand for the considered product is determined based on its price. Then, the manufacturer produces it from a waste with specific recyclability rate. The waste warehouse collects the waste and the recycler recycles it. The manufacturer meets his requirements through two different channels. He can procure the non-recycled waste from the waste warehouse and then recycles it himself, or can buy the recycled waste from the recycler. The manufacturer selects these channels based on the established situations.
Methodology: The game theory is applied to make the decisions under two considered channels. In this setting, a Stackelberg game is developed based on the competitive situation established among the members, where the manufacturer has higher decision power than the waste warehouse and recycler.
Findings: Eventually, the given strategies are discussed and the obtained results are presented. Results indicate that the manufacturer selects each channel as a threshold is met. Moreover, more recyclability rate of the considered waste leads to higher profits for the members.
Originality/Value: In this research, to provide the waste materials required for producing a product, the game-theoretic approach as well as the concept of the channel-selection are used. It can be stated that this issue has been proposed for the first time in the literature.
original-application paper
stochastic/Probabilistic/fuzzy/dynamic modeling
Mohammad-Saviz Asadi-Lari,; Maryam Abbasghorbani; reza Tavakkoli-Moghaddam
Abstract
The hotel industry has become a competitive industry at the international level in recent decades, and countries have tended to use developed models and new techniques, and provide innovations to maximize income from it. As a result, it is critical to pay attention to how we can manage hotel income while ...
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The hotel industry has become a competitive industry at the international level in recent decades, and countries have tended to use developed models and new techniques, and provide innovations to maximize income from it. As a result, it is critical to pay attention to how we can manage hotel income while noticing travel and passenger transportation costs and use modeling compatible with this field to optimize goal achievement. The problems of optimizing hotel revenue management, passenger cost management, and analyzing how to expand the transportation used by them have been studied in this research. One of the key issues studied is to predict how to transport a passenger and choose its type based on different modes of travel such as air, rail, water, and road based on the amount of the passenger’s budget. Also, many effective factors and criteria have been considered in the modeling done, and the amount of hotel reception capacity in the selected cities of travelers and the provision of various types of rooms with different pricing, and the examination of elements related to the services provided to travelers by the hotel and different accesses of the hotel, which is based on the hotel’s revenue model, affect on. It is useful to estimate the state of competitive factors of hotels. Noteworthy, the transfer and mode of transportation have been determined to predict the level of demand for hotel reservations for all types of travelers during different periods in different tourism seasons. This subject is based on the traveler’s budget allocated for paying expenses during the travel pattern and the related results extracted from the estimated income model, as well as the influencing factors in choosing the hotel and transportation. In the current study, the design of NP-Hard problems led to the use of exact methods in small-sized problems and two multi-objective meta-heuristic algorithms, namely NSGA-II and MOPSO, in medium- and large-sized problems. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.
original-application paper
Data Envelopment Analyses
Hossein Azizi
Abstract
Purpose: The analytic hierarchy process (AHP) is a multiple criteria decision-making method extensively used in various fields. Prioritization of decision criteria or alternatives from pairwise comparison matrices in AHP has been studied extensively. This article proposed the “Double-Frontier DEA” ...
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Purpose: The analytic hierarchy process (AHP) is a multiple criteria decision-making method extensively used in various fields. Prioritization of decision criteria or alternatives from pairwise comparison matrices in AHP has been studied extensively. This article proposed the “Double-Frontier DEA” approach for prioritization in AHP. This new approach uses two optimistic and pessimistic DEA models to obtain the best local priorities from a pairwise comparison matrix, regardless of whether it is fully consistent or not.
Methodology: One of these methods is data envelopment analysis (DEA). The combination of DEA and AHP (DEAHP) is used to obtain and aggregate weights in AHP. Studies show that DEAHP fails in obtaining and aggregating weights in AHP and sometimes produces priority vectors contrary to evidence for inconsistent pairwise comparison matrices that limits its application.
Findings: This new approach uses two optimistic and pessimistic DEA models to obtain the best local priorities from a pairwise comparison matrix, regardless of whether it is fully consistent or not. Some numerical examples, including a real application of AHP for selecting an innovation team for a university, are provided to specify the advantages of the proposed approach and its potential applications.
Originality/Value: The double-frontier DEA approach generates true weights for fully consistent pairwise comparison matrices and best local priorities for inconsistent pairwise comparison matrices, that are logical and fit subjective judgments of decision-makers.
original-application paper
Strategic Planing
Sajad Moradi
Abstract
This article studies an issue in the fish farming industry in which the goal is to find the best multi-period planning for handling various chains, including ordering, breeding, and selling of trout over a time horizon.
In this study, a new formulation is presented as a mixed integer linear programming ...
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This article studies an issue in the fish farming industry in which the goal is to find the best multi-period planning for handling various chains, including ordering, breeding, and selling of trout over a time horizon.
In this study, a new formulation is presented as a mixed integer linear programming model that could find the optimum solution quickly. In the new proposed formulation, some intermediate stages of the breeding chain that have no effect on decisions are ignored, and therefore, the size and complexity of the proposed model reduce without compromising the optimality of the answers.
After implementing the proposed model, using different data samples, it can be seen that this model achieves the optimal solution in a short time, including volume and time of spawning in each breeding chain and different periods, harvesting time, and accepting or rejecting the main demands.
In this paper, the issue of scheduling of fish farming chains and sales management, which there are a few studies in this field, has been studied and a new mixed integer linear programming model is presented. Compared to the previous model, this model has more real assumptions and less complexity and execution time.
Original Article
Data Envelopment Analyses
azam pourhabibyekta; Mahnaz Maghbouli
Abstract
Data Envelopment Analysis (DEA) has been proven as a useful non-parametric method for assessing a set of Decision Making Units (DMUs). Standard Models of DEA assume an optimal set of input and output weights to represent the assessed DMU in the best light in comparison to all other DMUs. Unfortunately, ...
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Data Envelopment Analysis (DEA) has been proven as a useful non-parametric method for assessing a set of Decision Making Units (DMUs). Standard Models of DEA assume an optimal set of input and output weights to represent the assessed DMU in the best light in comparison to all other DMUs. Unfortunately, the optimal weights generated may not be strictly positive which has reduced the usefulness of this powerful method. The subject of zero weights or equivalently non-zero slacks has been attracted considerable attention among researchers. In addition, the flexibility of input and output weights in DEA models causes the dispersion. To avoid zero weights, weights restrictions are frequently used in multiplier CCR- DEA model. In this manner, this paper proposes an approach by imposing weight restriction in the absence of expert information, cost or prices in BCC model. To ensure non-zero weights and avoiding dissimilarity between the weights an ancillary criterion for choosing the bounds of weights is employed. The contribution of the proposed model is four folded: (1) Pareto-efficient units are appeared in the reference points of inefficient units. (2) The proposed method based on BCC model produces strictly positive weights and at the same time avoid dissimilar weights. (3) The model does not require any prior information on the weights and especially unit’s classification. (4) The computational effort in the proposed model is substantially less than the other approaches without any infeasibility issues. To elucidate the details of the proposed method, a comparison is made between the existing models in the literature and the proposed method to measure the efficiency of some real cases. The results demonstrate the practicality and superiority of the proposed method in comparison with the existing models.
Original Article
Mathematical Optimization Models
Najme esmaeil Darjani; ahmad assadzadeh; mohamad mehdi barghi oskoei
Abstract
Purpose: Most of the tax policies are based on the way taxpayers make decisions according to classical economic models. However, studies show that the conventional decision-making models, which are designed without the social-psychology foundations and only based on economic components, cannot express ...
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Purpose: Most of the tax policies are based on the way taxpayers make decisions according to classical economic models. However, studies show that the conventional decision-making models, which are designed without the social-psychology foundations and only based on economic components, cannot express the developments and the exact way of decision-makers' performance. Today, a wide range of literature has been formed about tax behaviors based on behavioral economics. Due to the fact that the issue of tax evasion prevention by taxpayers is very significant and necessary, in this research, tax crimes have been compared in behavioral economics theory and expected utility theory by use of mathematical modeling and scenario building.
Methodology: In order to carry out simulations and investigate different scenarios, it is essential to calculate the necessary values for the two parts of external tax ethics and internal tax ethics, which have been calculated through surveys and using questionnaires. In this research, all guilds of Tehran province are considered, and due to the unavailability of all guilds, 455 samples have been selected using Morgan table and considering 5% error.
Findings: The obtained results indicate that the amount of crimes calculated in the theory of behavioral economics is closer to the crimes in the real world. Therefore, the obtained results are a good justification for choosing the perspective theory instead of the expected utility theory, and with the addition of the tax ethics parameter, the amount of tax penalty is reduced in both theories.
Originality/Value: Considering that a healthy economy is an economy that is mostly based on taxes and in which government expenses are financed through tax collection, the country's tax system should be reformed to achieve this goal.
Original Article
supply chain management analyzing/modelling
hamid saffari; morteza abbasi; Jafar Gheidar-Kheljani
Abstract
Purpose This research proposes a multi-objective and robust model considering both cost and risks related to the environment (water consumption and environmental pollution), social responsibility (working conditions and employee health), operations (change in demand and return rates), and disruption ...
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Purpose This research proposes a multi-objective and robust model considering both cost and risks related to the environment (water consumption and environmental pollution), social responsibility (working conditions and employee health), operations (change in demand and return rates), and disruption (accidents and diseases such as COVID-19) in the supply chain, using horizontal collaboration to deal with it.Methodology: In this research, mixed-integer linear programming and robust optimization technique have been used for closed-loop supply chain network design and a multi-objective method has been developed to solve the problem and create Pareto spaces.Findings: The results of the calculations show the effect of failure probability on the capacity of the facility, the total cost of the network and the degree of collaboration between members of the supply chain to deal with the risk. Also, the amount of cost required for allocation to reliable and unreliable facilities and also creating a suitable Pareto space for deciding on the optimal choice of facilities, capacity and flow between them and iron and steel production technology, according to sustainability and social responsibility indicators, are other research findings.Originality/Value: In this study, for the first time, the design of a robust, sustainable, and resilient network of iron and steel under different risks has been studied. Horizontal collaboration has been used as a new approach to deal with risk and solution method for multi-objective problems has been developed. Using the results of this study, the decision-maker can make informed decisions about the supply chain under risk conditions by considering suitability for each of the objectives.
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