Original Article
Decision based on Neural Networks/ Deep Learning
Mohamad Ali Khatami Firoz Abadi; 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 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-application paper
stochastic/Probabilistic/fuzzy/dynamic modeling
Mohammad-Saviz Asadi-Lari; Maryam Abbas Ghorbani; Reza Tavakkoli-Moghaddam
Abstract
Purpose: 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 ...
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Purpose: 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.Methodology: 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.Findings: 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.Originality/Value: 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 Article
supply chain management analyzing/modelling
Mohammad Mehdi Rahimian Asl; Mohammad Hassan Maleki
Abstract
Purpose: 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 ...
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Purpose: 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.Methodology: 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.Findings: 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.Originality/Value: 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
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-application paper
Strategic Planing
Narjes Bossaghzadeh; Mahmoud Moradi; Mohammad Tamimi
Abstract
Purpose: 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 ...
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Purpose: 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.Methodology: 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.Findings: 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.Originality/Value: 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-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
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 human ...
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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
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. Also, the results of a case study in Alborz Yadak company related to the problem of the research are reported in the article.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 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
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 resilience toward 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
Robust optimization
Amin Ghaseminejad; Mohammad Fallah; Hamed Kazemipoor
Abstract
Purpose: The present paper deals with modeling and solving a multi-objective problem of robust facility layout problem under 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 ...
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Purpose: The present paper deals with modeling and solving a multi-objective problem of robust facility layout problem under 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 article. The issue under consideration in this article includes several departments that are 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 equipments and arranging the departments together are among the main objectives of the article.Methodology: In this paper, GA, PSO and GWO single-objective meta-heuristic algorithms and NSGA-II, MOPSO and MOGWO multi-objective meta-heuristic algorithms have been used to solve the problem.Findings: 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; the MOPSO algorithm has the highest expansion and metric distance in achieving the average number of efficient answers and computational time, and finally the MOGWO algorithm in obtaining the average value of the third and fourth objective functions. 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.Originality/Value: In this paper, a new model of the multi-objective robust facility layout problem under uncertainty conditions is modeled with respect to health and environmental safety aspects.
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 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
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 two portfolios with 6 and 10 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.