Original Article
Seyyedeh Fatemeh Aghajani Mir; Fatemeh Zahra Rajabi kafshgar; Alireza Arab
Abstract
Purpose: Today, the advent of blockchain technology has changed the way businesses do business, the size and scope of different organizations. One of these areas is the supply chain, which has many stakeholders, and blockchain, with its unique features, effectively responds to the various challenges ...
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Purpose: Today, the advent of blockchain technology has changed the way businesses do business, the size and scope of different organizations. One of these areas is the supply chain, which has many stakeholders, and blockchain, with its unique features, effectively responds to the various challenges in this area. Implementing this technology, like other technologies, has many challenges. Hence, these challenges must be carefully identified and analyzed to minimize their adverse impacts to use this technology effectively. In this regard, the present study aims to identify and prioritize the challenges of implementing blockchain technology in the supply chain based on the Bayesian BWM as one of the newest multiple attribute group decision-making methods.Methodology: At first, after reviewing the research literature, the challenges were identified. Then, the Bayesian BWM method determined the importance of these challenges in the case study.Findings: The results showed that security, technical and organizational challenges are the most important challenges for the company in implementing this technology, respectively. Also, among all sub-indicators of research challenges, poor scalability, privacy/confidentiality of the information, and cyberattacks have the most importance, respectively.Originality/Value: This study studied the challenges of implementing blockchain technology as a new technology in supply chains using one of the newest multiple attribute group decision-making methods (Bayesian BWM). Based on the research results, practical and research suggestions were presented
Original Article
Fateme Yazdani; Mehdi Khashei; Seyed Reza Hejazi
Abstract
Purpose: This paper aims to propose a model for detecting the most profitable or the optimal Turning Points (TPs) existing in the history of the financial tool's time series. The profitable trading strategy, which is known as a tool for gaining profit in the Stock Exchange, is the strategy formed from ...
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Purpose: This paper aims to propose a model for detecting the most profitable or the optimal Turning Points (TPs) existing in the history of the financial tool's time series. The profitable trading strategy, which is known as a tool for gaining profit in the Stock Exchange, is the strategy formed from the profitable trading points. Trading points, in the corresponding literature, are known as TPs. TPs prediction is a tool for the achievement of a profitable trading strategy. The first step for predicting TPs is to detect TPs existing in the history of the financial tool's time series. The profitability of the detected TPs has a direct effect on the profitability of the predicted TPs. Given this, the literature has always tried to increase the profitability of the detected financial TPs. A complete review of the literature, by researchers, indicates that none of the existing methods can detect the optimal financial TPs.Methodology: This paper implements the problem of detecting TPs from the financial tool's time series, in the context of dynamic programming (DP) and then solves it optimally through a recursive procedure.Findings: Numerical results obtained from the application of the proposed model to four companies listed on the Tehran Stock Exchange indicate that the proposed model can detect the optimal financial TPs.Originality/Value: Originality in research mean what you are doing is from your own perspective although you may draw arguments from other research work to back up your arguments.
Original Article
stochastic/Probabilistic/fuzzy/dynamic modeling
Ahmad Faridanifar; Parvaneh Samouei
Abstract
Purpose: One of the topics for manufacturers today is to discuss the diversity of customer tastes, which to manage this situation with the least change in products, requires multiple lines that have the necessary flexibility to produce these products. On the other hand, many products require assembly ...
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Purpose: One of the topics for manufacturers today is to discuss the diversity of customer tastes, which to manage this situation with the least change in products, requires multiple lines that have the necessary flexibility to produce these products. On the other hand, many products require assembly operations. The main purpose of this article is to balance these issues according to the conditions of the workforce and different products.Methodology: This paper presents two mathematical models to minimize the number of workstations per given cycle time. In the first model, all parameters are definite. Since customer demand may not be constant and this factor can affect the cycle time, the second model uses a robust approach to this issue.Findings: Analysis of various issues shows that a robust modeling approach provides a more reliable design and allows decision makers to have better assembly based on a better understanding of short-term and long-term conditions under conditions of demand uncertainty.Originality/Value: In this paper, two new mathematical models for assembly line balance are presented. Multi-models in which assembly operations are performed manually by workers and for more accurate planning, the differences that workers have in terms of learning and forgetfulness effect on assembly line balance are considered.
original-application paper
Optimization in science and engineering
Leila Hasan-Beigi Dashtbayaz; Isa Nakhai Kamalabadi; Ali Husseinzadeh Kashan; Sakine Beigi
Abstract
Purpose: In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is ...
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Purpose: In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is generally determined by assuming a certain lifetime for CNC machine tools to minimize production costs. However, minimizing costs usually results in increased machining time and lower output rates.Methodology: In this research, the optimal machining time is determined using a bi-objective model including minimizing the cost and total production time of a robotic cell with a CNC machine and a material handling robot. It has assumed that identical productions are produced in this robotic cell. Using the Epsilon constraint method, the proposed model is coded in GAMS software and its results are reported.Findings: In this research, the lifespan of the CNC machine tools can be considered as a determined or probable value. The results showed that decreasing the operation time at different speeds does not necessarily impose the same cost on the system. Therefore, it is necessary to be more careful in choosing the appropriate machining time for different tools and parts. Paying attention to the rate of suddenly tool breakdowns is also important in choosing the appropriate time for machining. Using a set of non-dominated solutions, it is possible to determine the appropriate machining time in different parts to achieve a suitable level of problem objectives.Originality/Value: In this research, for the first time, the failure rate of the tool as one of the cost factors in the robotic cell has been added to the cost function of a production cycle and its effect on determining the appropriate machining time has been investigated.
Original Article
Location Modeling
Mona Alizadeh Firozi; Vahid Kiani; Hossein Karimi
Abstract
Purpose: The purpose of this paper is to propose an improved genetic algorithm to solve the problem of Uncapacitated Single-allocation Hub Location. Previous methods have paid less attention to the diversity of population, and due to insufficient vairation in mutation operators, they perform well only ...
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Purpose: The purpose of this paper is to propose an improved genetic algorithm to solve the problem of Uncapacitated Single-allocation Hub Location. Previous methods have paid less attention to the diversity of population, and due to insufficient vairation in mutation operators, they perform well only in a few runs, and in other runs they are caught in the local optimum.Methodology: The proposed method uses appropriate genetic operators to increase diversity of the population and performs local search around the best answer to exploit promising areas of the solution space. The use of hub mutation operators along with allocation mutation operators in the proposed algorithm has increased its exploration ability and effectiveness, which has led to discovery of the optimal answer in most runs for large size problems. Also, searching for the local neighborhood of the best answer made convergence faster and reduced the total running time for large instances.Findings: Evaluation of the proposed method and base algorithm on the Australian Post (AP) dataset showed that the improvements increased efficiency of the genetic algorithm in achieving optimal solutions for problems as large as 200 nodes from 2% to more than 85%.Originality/Value: This study showed that meta-heuristic algorithms and their improved versions are suitable methods for solving hub location problems in a short and limited time.
original-application paper
Data Envelopment Analyses
Mohammad Izadikhah; Mohadeseh Shamsi; Abbas Sheikhsn; Fariba Ghafouri
Abstract
Purpose: Implementing a credit rating system considering banks' deferred claims is one of the most important means of controlling credit risk in banks and financial institutions. In the case of banking facilities, the possibility of non-repayment of facilities is one of the most important issues. By ...
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Purpose: Implementing a credit rating system considering banks' deferred claims is one of the most important means of controlling credit risk in banks and financial institutions. In the case of banking facilities, the possibility of non-repayment of facilities is one of the most important issues. By identifying various factors that affect the non-repayment of bank facilities, it is possible to provide a framework for reducing and controlling the credit risk of banks and improving the crediting process. The purpose of this paper is to examine the relationship between efficiency and risk in the banking system.Methodology: In this research, a sample of 24 companies from the most important legal customers of 11 branches of the Melli Bank of the city of Arak has been studied. 18 variables affecting credit risk were identified in this paper. Among the variables available, 6 variables were selected using the Factor Analysis Technique and Expert judgment (Delphi method), of which 3 were inputs and 3 were formed the outputsFindings: The efficiency and rank of legal firms were obtained with the help of Data Envelopment Analysis models, and then using the Fitch Institute's data and the efficiency of legal clients, the credit rating of each of the legal firms and their qualitative analysis was expressed.Originality/Value: Accordingly, using the method of data envelopment analysis and data provided by the Fitch Ratings Institute, the credit risk of Arak's Melli Bank's legal customers is assessed and ranked
Original Article
Numerical Optimization
Nooshin Hakamipour
Abstract
Purpose: In this paper k- level constant stress accelerated life test under Type-I progressive censoring for Lomax distribution with non-constant shape and scale parameters is investigated. The purpose of this paper is to estimate the model parameters using the EM algorithm and optimize the test design.Methodology: ...
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Purpose: In this paper k- level constant stress accelerated life test under Type-I progressive censoring for Lomax distribution with non-constant shape and scale parameters is investigated. The purpose of this paper is to estimate the model parameters using the EM algorithm and optimize the test design.Methodology: Life testing often consumes a very long time for testing and this is a fundamental problem in test design. This problem is solved by accelerated life tests. There is a recommended method for reducing the time of failure, such that the stress level of the test units will increase and then they will fail earlier than normal operating conditions. Therefore, these approaches reduced the running time. In this paper, the k-level constant stress accelerated life test under progressive Type-I censoring used. The Expectation-Maximization (EM) algorithm is used to determine the maximum likelihood estimates of the unknown parameters. This algorithm is a very powerful tool in handling the incomplete data problem. Two different criteria used to optimize the test plan. And the effect of the sample size, number of stress levels and inspection and the intermediate censoring proportion are assessed on the design efficiency.Findings: based on the simulation study and a real data set, it is demonstrated that the EM estimator is good. Also, under the optimization criterion II, a more efficient test was obtained than the optimization criterion I. In addition, the small sample size, the small number of stress levels, the small number of inspections and the large intermediate censoring proportion lead to a more efficient test.Originality/Value: In this paper, the periodic inspection is used to collect lifetime data. Although continuous is an ideal mode. But sometimes due to technical limitations and/or budgetary constraints, the continuous inspection is not possible in practice and the experimenter has to use the periodic inspection. In this case, the exact times of test units may not be available and only the failure counts are collected at certain time points during the test. Also, in this paper, we assumed that both scale and shape parameters to be log linear model by operating stress
original-application paper
Financial Marketing Strategies
Shahram Eshragh; Narges Delafrooz; Kambiz Shahroodi; Yalda Rahmati
Abstract
Purpose: By increasing knowledge about the role of the brand in the success of industrial markets, the attention of buyers and suppliers in the industrial market, understanding the major factors affecting the performance of the industrial brand becomes important. Today, the petrochemical industry has ...
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Purpose: By increasing knowledge about the role of the brand in the success of industrial markets, the attention of buyers and suppliers in the industrial market, understanding the major factors affecting the performance of the industrial brand becomes important. Today, the petrochemical industry has played an important role in non-oil exports. Therefore, companies related to the petrochemical industry can play an effective role in the economic development of the country by creating an industrial brand. The purpose of this research is to analyze the factors affecting brand performance in the petrochemical industry.Methodology: In this study, through literature study, interviews with industry-related experts and analysis of data obtained through data analysis of the foundation, the main criteria related to brand performance were identified and classified, and in the next step using Dimatel decision method, the causal network structure of the criteria and sub-criteria were examined.Findings: In the qualitative phase of the paradigm model of the research, 10 main criteria were identified, including brand equity, sales efficiency, innovation in response, company reputation, modeling, brand-based organizational capital, organizational competitive strategies, distribution, product quality, production and volume and finally using Dimatel decision making method, organizational competitive strategies were identified as the most influential factor.Originality/Value: In this article, while using the appropriate features of qualitative and quantitative methods, provided a deeper understanding of the key factors affecting the success of the petrochemical industry brand performance and by providing a new model provides better competitiveness in this industry