Data Envelopment Analyses
Majid Yarahmadi; Saeedeh Sakiniya
Abstract
Purpose: This paper presents an intelligent method for applying Data Envelopment Analysis (DEA) to design a sustainable supply chain.Methodology: In the proposed method, for defuzzification of the ERM model, we used the -cutting technique. Then, to measure the productivity in the presence of environmental ...
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Purpose: This paper presents an intelligent method for applying Data Envelopment Analysis (DEA) to design a sustainable supply chain.Methodology: In the proposed method, for defuzzification of the ERM model, we used the -cutting technique. Then, to measure the productivity in the presence of environmental uncertainty via different -levels, a genetic algorithm is implemented to find an optimal -cutting. Finally, an intelligent DEA model for ranking the supplier companies via optimal value is designed.Findings: This paper presents a new fuzzy DEA model based on a Genetic Algorithm for evaluating the productivity of suppliers in a sustainable supply chain.Originality/Value: In the proposed method, since the -cut obtained from the Genetic Algorithm is optimal, there is no longer a need to calculate the efficiency for different α-cuts through trial and error. Therefore, the proposed method's advantage is that it offers a more sustainable ranking in addition to increasing productivity for each supplier. The example presented in this article demonstrates the method's superiority and advantages.
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.
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.
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.
meta-heuristic algorithms
Mohammadreza Etebari; Naser Feghhi Farahmand; Soleyman Iranzadeh
Abstract
Purpose: Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The purpose ...
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Purpose: Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The purpose of this paper is to compile a credit forecasting model for legal customers of private banks by using meta-heuristic algorithms in the branches of Pasargad Bank in the northwest of Iran.Methodology: This research is base on the purpose of developmental research and based on the method of performing descriptive work. The statistical population of this study is in two sections of banking experts and legal customers of Pasargad Bank in the northwest of the Iran. The statistical sample size for the first community of 58 banking experts including managers, credit officials and heads of branches in with credit work experience in private banks and for the second community, 427 legal clients were selected based on targeted sampling. In order to collect data in this research, a questionnaire and documents of Pasargad Bank have been used. The validity of the questionnaire was investigated as content validity and based on the indicators of content validity ratio and content validity index. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient. In order to analyze the research data, t-test, confirmatory factor analysis, multilayer neural network, genetically trained neural network, trained neural network with particle swarm optimization and trained neural network with differential evolution will be used.Findings: The research findings show that all four models are able to predict the credit predictions of the legal customers of private banks and the best way to predict the credit predictions of the legal customers of private banks is the neural network trained with differential evolution algorithm with the least amount of error compared to the other three methods.Originality/Value: In this research by using meta-heuristic algorithms, a new credit forecasting model produce for legal customers of private banks with the least amount of error.
Scheduling Modeling
Habibeh Nazif; Khadijeh Ghaziani
Abstract
The timetable is the problem of placing particular resources due to constraints in a limited number of times lots and space, in order to satisfy a set of goals that is used to a variety of problems. Among these problems, one can point out the University Examination Timetabling Problem (UETP), which is ...
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The timetable is the problem of placing particular resources due to constraints in a limited number of times lots and space, in order to satisfy a set of goals that is used to a variety of problems. Among these problems, one can point out the University Examination Timetabling Problem (UETP), which is the particular importance in educational problems. The university examination timetabling problem defined as the assignment of a certain set of exams to a fixed number of time slots and rooms, so that it meets all the hard constraints, also soft constraints are optimized as much as possible. This research presents a modified approach to optimize the incapacitated UETP. In this approach, a proposed Genetic Algorithm (GA) is modified by local search operators. These operators will make alterations to the timetable. This involves shifting or changing scheduled exams and thus greatly improve the ability of the algorithm to search. The efficiency of the proposed approach is compared with other techniques from literature using the Carter’s benchmark. The computational results show that this approach is quite effective and competitive in improving the solutions and is able to produce better solutions in most of the datasets compared with other algorithms.
Location Modeling
Ali Naimi-Sadigh; Amir Emami; Marzieh Mozafari
Abstract
Total covering problem is one of the most commonly used issues of locating facilities. In this context, the goal of determining the P service center is to cover at least the cost of deploying all demand points. These issues have a wide range of nature and scope, each of which is optimized by taking into ...
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Total covering problem is one of the most commonly used issues of locating facilities. In this context, the goal of determining the P service center is to cover at least the cost of deploying all demand points. These issues have a wide range of nature and scope, each of which is optimized by taking into account certain conditions in order to find the answer. One of these conditions can be a situation in which, in addition to full coverage of demand, the dispersion of facilities is also considered. Facility dispersion means maximizing the distance between facilities with respect to existing limits. This research seeks to provide a suitable model considering the predictable limits of the real world and the use of an appropriate method for solving the cover-dispersion model. Accordingly, the full coverage of the solution space and the choice of the optimal location of the facility with maximum dispersion, taking into account the minimum number of facilities and the lowest cost of deployment, due to the limited capacity of facilities and the minimization of transportation costs are the goals of this research. Due to the NP-HARD nature of the coating and literature models, solving these models, an algorithm is designed based on the genetic method for solving the model. In order to improve the quality of the algorithm's parameters, the parameters of the algorithm are set by the Taguchi experimental design method. The results show that the algorithm is suitable for the model.