Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.1891023.1722
A. R. Komijan, P. Ghasemi, K. Khalili-Damghani, Fakhrosadat HashemiYazdi
In developing countries, whereas the urban bus network is a major part of public transportation system, it is necessary to try to find the best design and routing for bus network. Optimum design of school bus routes is very important. Non-optimal solutions for this problem may increase traveling time, fuel consumption, and depreciation rate of the fleet. A new bus routing problem is presented in this study. A multi-objective mixed integer model is proposed to handle the associated problem. Minimization of transportation cost as well as traveling time is the main objectives. The main contributions of this paper are considering gender separation as well as mixed-loading properties in the school bus routing problem. Moreover, special and handicapped students are considered in this problem. The proposed model is applied in a real case study including 4 schools in Tehran. The results indicate the efficiency of the proposed model in comparison with the existing system. This comparison shows that the students’ travelling time is reduced by 28% for Peyvand middle smart school, 24% for Tehran international school, 13% for Hemmat School and 21% for Nikan High school. A customized Genetic Algorithm (GA) is proposed to solve the model. Penalty functions are used to handle the several constraints of the problem in Genetic Algorithm. The results justify the applicability and efficacy of the both proposed model and solution approach.
{"title":"A New School Bus Routing Problem Considering Gender Separation, Special Students and Mix Loading: A Genetic Algorithm Approach","authors":"A. R. Komijan, P. Ghasemi, K. Khalili-Damghani, Fakhrosadat HashemiYazdi","doi":"10.22094/JOIE.2020.1891023.1722","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1891023.1722","url":null,"abstract":"In developing countries, whereas the urban bus network is a major part of public transportation system, it is necessary to try to find the best design and routing for bus network. Optimum design of school bus routes is very important. Non-optimal solutions for this problem may increase traveling time, fuel consumption, and depreciation rate of the fleet. A new bus routing problem is presented in this study. A multi-objective mixed integer model is proposed to handle the associated problem. Minimization of transportation cost as well as traveling time is the main objectives. The main contributions of this paper are considering gender separation as well as mixed-loading properties in the school bus routing problem. Moreover, special and handicapped students are considered in this problem. The proposed model is applied in a real case study including 4 schools in Tehran. The results indicate the efficiency of the proposed model in comparison with the existing system. This comparison shows that the students’ travelling time is reduced by 28% for Peyvand middle smart school, 24% for Tehran international school, 13% for Hemmat School and 21% for Nikan High school. A customized Genetic Algorithm (GA) is proposed to solve the model. Penalty functions are used to handle the several constraints of the problem in Genetic Algorithm. The results justify the applicability and efficacy of the both proposed model and solution approach.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"39-55"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44352218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.1866273.1648
A. F. Kivi, E. Mehdizadeh, R. Tavakkoli-Moghaddam, S. Najafi
The supply chain network design not only assists organizations production process (e.g.,plan, control and execute a product’s flow) but also ensure what is the growing need for companies in a longterm. This paper develops a three-echelon supply chain network problem including multiple plants, multiple distributors, and multiple retailers with amulti-mode demand satisfaction policy inside of production planning and maintenance. The problem is formulated as a mixed-integer linear programming model. Because of its NP-hardness, three meta-heuristic algorithms(i.e., tabu search, harmony search and genetic algorithm) are used to solve the given problem. Also, theTaguchi method is used to choose the best levels of the parameters of the proposedmeta-heuristic algorithms. The results show that HS has abetter solution quality than two other algorithms.
{"title":"Solving a Multi-Item Supply Chain Network Problem by Three Meta-heuristic Algorithms","authors":"A. F. Kivi, E. Mehdizadeh, R. Tavakkoli-Moghaddam, S. Najafi","doi":"10.22094/JOIE.2020.1866273.1648","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1866273.1648","url":null,"abstract":"The supply chain network design not only assists organizations production process (e.g.,plan, control and execute a product’s flow) but also ensure what is the growing need for companies in a longterm. This paper develops a three-echelon supply chain network problem including multiple plants, multiple distributors, and multiple retailers with amulti-mode demand satisfaction policy inside of production planning and maintenance. The problem is formulated as a mixed-integer linear programming model. Because of its NP-hardness, three meta-heuristic algorithms(i.e., tabu search, harmony search and genetic algorithm) are used to solve the given problem. Also, theTaguchi method is used to choose the best levels of the parameters of the proposedmeta-heuristic algorithms. The results show that HS has abetter solution quality than two other algorithms.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"145-151"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45637550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2019.1864217.1635
Ehsan Vaezi
The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model simulates the internal structure of a diagnostic lab (the pre-test, the test and the post-test). In this study, the criteria for evaluation are obtained by using the Fuzzy Delphi method. Due to the social, economic and environmental problems of health care organizations, the importance of sustainability criteria is evident in the case study indicators. We utilized the multiplicative DEA approach to measure the efficiency of a general system and a heuristic technique was used to convert non-linear models into linear models. Ultimately, this paper concentrates on the interval efficiency to rank the units.
{"title":"Measuring the performances of Medical Diagnostic Laboratories based on interval efficiencies","authors":"Ehsan Vaezi","doi":"10.22094/JOIE.2019.1864217.1635","DOIUrl":"https://doi.org/10.22094/JOIE.2019.1864217.1635","url":null,"abstract":"The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model simulates the internal structure of a diagnostic lab (the pre-test, the test and the post-test). In this study, the criteria for evaluation are obtained by using the Fuzzy Delphi method. Due to the social, economic and environmental problems of health care organizations, the importance of sustainability criteria is evident in the case study indicators. We utilized the multiplicative DEA approach to measure the efficiency of a general system and a heuristic technique was used to convert non-linear models into linear models. Ultimately, this paper concentrates on the interval efficiency to rank the units.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"153-170"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43148784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2021.578550.1599
AmirHossein Zahedi-Anaraki, G. Esmaeilian
This paper addresses a bi-objective two-stage stochastic mixed-integer linear programming model for a stochastic reliable capacitated facility location in which the optimum numbers, locations and as well as shipment quantity of the product between the network nodes for all scenarios should be determined. Unlike most of previous relevant works, multiple levels of capacities available to the manufacturers in different scenarios are permitted in this study. The proposed objectives of the model include: the minimization of expected sum of installation, production, transportation under uncertainty of parameters, such as transportation and production and disruption of facilities, as well as minimizing expected standard deviation of network costs for whole scenarios. Since one of the most important reasons for researchers' reluctance to apply Benders-decomposition algorithm in facility-location concept is the time-consuming nature of its feasibility-cut stage, one of the most outstanding innovation in this paper is to add a strengthening redundant constraint to the proposed model in order to eliminate the mechanism related to feasibility cuts in master problem. to the best of our knowledge, it is the first time that this technique, not being involved in keeping master-problem feasibility, is used to solve a reliable capacitated facility location problem. In this approach, in terms of time-consuming the Benders algorithm is able to powerfully compete with metaheuristic algorithms, but with an exact solution. To prove advantage of this algorithm satisfying both ultimate solution optimality and appropriate running time compared to metaheuristic algorithms at the same time, one metaheuristic algorithm, namely Imperialist Competitive Algorithm (ICA), is presented. Usefulness and practicality of the proposed model and solution method demonstrated through a case example in different class with variable size.
{"title":"A Benders-Decomposition and Meta-Heuristic Algorithm for a Bi- Objective Stochastic Reliable Capacitated Facility Location Problem Not Dealing with Benders Feasibility-Cut Stage","authors":"AmirHossein Zahedi-Anaraki, G. Esmaeilian","doi":"10.22094/JOIE.2021.578550.1599","DOIUrl":"https://doi.org/10.22094/JOIE.2021.578550.1599","url":null,"abstract":"This paper addresses a bi-objective two-stage stochastic mixed-integer linear programming model for a stochastic reliable capacitated facility location in which the optimum numbers, locations and as well as shipment quantity of the product between the network nodes for all scenarios should be determined. Unlike most of previous relevant works, multiple levels of capacities available to the manufacturers in different scenarios are permitted in this study. The proposed objectives of the model include: the minimization of expected sum of installation, production, transportation under uncertainty of parameters, such as transportation and production and disruption of facilities, as well as minimizing expected standard deviation of network costs for whole scenarios. Since one of the most important reasons for researchers' reluctance to apply Benders-decomposition algorithm in facility-location concept is the time-consuming nature of its feasibility-cut stage, one of the most outstanding innovation in this paper is to add a strengthening redundant constraint to the proposed model in order to eliminate the mechanism related to feasibility cuts in master problem. to the best of our knowledge, it is the first time that this technique, not being involved in keeping master-problem feasibility, is used to solve a reliable capacitated facility location problem. In this approach, in terms of time-consuming the Benders algorithm is able to powerfully compete with metaheuristic algorithms, but with an exact solution. To prove advantage of this algorithm satisfying both ultimate solution optimality and appropriate running time compared to metaheuristic algorithms at the same time, one metaheuristic algorithm, namely Imperialist Competitive Algorithm (ICA), is presented. Usefulness and practicality of the proposed model and solution method demonstrated through a case example in different class with variable size.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"307-319"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47238773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.677869
M. Bil, I. Irtyshcheva, N. Popadynets, D. Voit
Spatial mobility is a topical concept of analytical migration science, which makes it possible to assess the desires, readiness and capabilities of the population to move over certain distances and time. In the management of spatial mobility assessment requires the organization of systematic monitoring, which includes identifying the mobility potential in spatial and temporal interpretation, the status of its implementation in migration and tourism directions, the causes of displacement with an assessment of the deprivation level, as well as the consequences of displacement, in particular in the context of achieving human development goals and capitalizing on human potential. The quality system of monitoring of population spatial mobility should be the basis for mobility regulation. The formation of such systems should be carried out in several stages: development of unified approach to the formation of accounting and mobility statistics; development of methodology and conducting selective sociological survey on the population spatial mobility with the participation of state statistics authorities and the International Organization for Migration (internal and external mobility); development of a indicators system for the monitoring of population spatial mobility in context of achieving human development goals. One of the main results of the monitoring of population spatial mobility is to find out the main groups of the potentially active population in the coordinates of space and time: internal mobility, including intra-settlement, intra-regional, inter-regional (the main purpose of regulation is the development of transport infrastructure); middle-distance mobility, including cross-border, intra-state (the main purpose of regulation is the effective use of migration capital and tourism costs); long-distance mobility, including continental, remote (the main purpose of regulation is to ensure circulating migration; keeping in touch through the Diaspora Institute). Formation of high-quality system of information support for migration regulation through the monitoring of population spatial mobility will allow to depart from the practice of biased accounting of migration processes with limited and non-systematic presentation of statistics.
{"title":"Population Spatial Mobility: Monitoring, Methodology of Formation, Features of Regulation","authors":"M. Bil, I. Irtyshcheva, N. Popadynets, D. Voit","doi":"10.22094/JOIE.2020.677869","DOIUrl":"https://doi.org/10.22094/JOIE.2020.677869","url":null,"abstract":"Spatial mobility is a topical concept of analytical migration science, which makes it possible to assess the desires, readiness and capabilities of the population to move over certain distances and time. In the management of spatial mobility assessment requires the organization of systematic monitoring, which includes identifying the mobility potential in spatial and temporal interpretation, the status of its implementation in migration and tourism directions, the causes of displacement with an assessment of the deprivation level, as well as the consequences of displacement, in particular in the context of achieving human development goals and capitalizing on human potential. The quality system of monitoring of population spatial mobility should be the basis for mobility regulation. The formation of such systems should be carried out in several stages: development of unified approach to the formation of accounting and mobility statistics; development of methodology and conducting selective sociological survey on the population spatial mobility with the participation of state statistics authorities and the International Organization for Migration (internal and external mobility); development of a indicators system for the monitoring of population spatial mobility in context of achieving human development goals. One of the main results of the monitoring of population spatial mobility is to find out the main groups of the potentially active population in the coordinates of space and time: internal mobility, including intra-settlement, intra-regional, inter-regional (the main purpose of regulation is the development of transport infrastructure); middle-distance mobility, including cross-border, intra-state (the main purpose of regulation is the effective use of migration capital and tourism costs); long-distance mobility, including continental, remote (the main purpose of regulation is to ensure circulating migration; keeping in touch through the Diaspora Institute). Formation of high-quality system of information support for migration regulation through the monitoring of population spatial mobility will allow to depart from the practice of biased accounting of migration processes with limited and non-systematic presentation of statistics.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"31-37"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48248916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2021.678825
M. Abadi, H. Saeednia, A. Khorshidi
The current research has been conducted to provide a model for customer experience management in the mobile banking industry for customers of commercial banks in Dubai. An explorative mixed methods research (qualitative and quantitative) was used in the research. Data were gathered in both qualitative phase (based on grounded theory) and quantitative phase (based on cross-sectional survey method). In the qualitative phase, population consisted of academic specialists and experts (university professors in the field of management) selected by judgmental sampling method of snowball sampling type. Data were gathered using a semi-structured interview. Data gathering reached theoretical data saturation in the twenty-fifth interview, so interviews were stopped at this point. The results of coding based on grounded theory led to the identification of 170 open codes, 24 axial codes, and 7 selective codes including value, cognitive, motivational, sensory, physical, behavioral, and communicative ones. In the quantitative phase, population consisted of 100,000 users (equal numbers of men and women) of mobile banking services. Given that the community variance was not available, Morgan and Krejcie table were used to determine the sample size that was calculated at 384 individuals. Data analysis in the quantitative phase confirmed the findings of qualitative research according to chi-square (x2), goodness of fit (GFI), adjusted goodness of fit (AGFI), and root mean squared error of approximation (RMSEA) indices.
{"title":"Presenting a Model of Customer Experience Management in Mobile Banking Industry for Commercial Banks Customers in Dubai","authors":"M. Abadi, H. Saeednia, A. Khorshidi","doi":"10.22094/JOIE.2021.678825","DOIUrl":"https://doi.org/10.22094/JOIE.2021.678825","url":null,"abstract":"The current research has been conducted to provide a model for customer experience management in the mobile banking industry for customers of commercial banks in Dubai. An explorative mixed methods research (qualitative and quantitative) was used in the research. Data were gathered in both qualitative phase (based on grounded theory) and quantitative phase (based on cross-sectional survey method). In the qualitative phase, population consisted of academic specialists and experts (university professors in the field of management) selected by judgmental sampling method of snowball sampling type. Data were gathered using a semi-structured interview. Data gathering reached theoretical data saturation in the twenty-fifth interview, so interviews were stopped at this point. The results of coding based on grounded theory led to the identification of 170 open codes, 24 axial codes, and 7 selective codes including value, cognitive, motivational, sensory, physical, behavioral, and communicative ones. In the quantitative phase, population consisted of 100,000 users (equal numbers of men and women) of mobile banking services. Given that the community variance was not available, Morgan and Krejcie table were used to determine the sample size that was calculated at 384 individuals. Data analysis in the quantitative phase confirmed the findings of qualitative research according to chi-square (x2), goodness of fit (GFI), adjusted goodness of fit (AGFI), and root mean squared error of approximation (RMSEA) indices.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"215-223"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45819575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.677889
A. Khazaei, B. Karimi, M. Mozaffari
The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used was a multilayer perceptron network using the error propagation learning algorithm.After predicting the stock price with the neural network, the forecast price data in the second phase has been used to optimize the stock portfolio.In this phase, a multi-objective genetic algorithm is used to optimize the portfolio, and the optimal weights are assigned to the stock and the optimal stock portfolio is created.Having a regression model, the design of the relevant genetic algorithm has been done using MATLAB software.The results show that the stock portfolio created by MOPSO algorithm has a better performance compared to the algorithms used in the article under comparison under all four risk criteria except the criterion of conditional risk exposure. In all models, except the conditional risk-averaged value model, the stock portfolios created by the MOPSO algorithm used in the research have more and more appropriate performance.
{"title":"Optimizing the Prediction Model of Stock Price in Pharmaceutical Companies Using Multiple Objective Particle Swarm Optimization Algorithm (MOPSO)","authors":"A. Khazaei, B. Karimi, M. Mozaffari","doi":"10.22094/JOIE.2020.677889","DOIUrl":"https://doi.org/10.22094/JOIE.2020.677889","url":null,"abstract":"The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used was a multilayer perceptron network using the error propagation learning algorithm.After predicting the stock price with the neural network, the forecast price data in the second phase has been used to optimize the stock portfolio.In this phase, a multi-objective genetic algorithm is used to optimize the portfolio, and the optimal weights are assigned to the stock and the optimal stock portfolio is created.Having a regression model, the design of the relevant genetic algorithm has been done using MATLAB software.The results show that the stock portfolio created by MOPSO algorithm has a better performance compared to the algorithms used in the article under comparison under all four risk criteria except the criterion of conditional risk exposure. In all models, except the conditional risk-averaged value model, the stock portfolios created by the MOPSO algorithm used in the research have more and more appropriate performance.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"89-97"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46881614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.1893849.1730
Fariba Safari, F. Etebari, Adel Pourghader Chobar
In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method.
{"title":"Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II","authors":"Fariba Safari, F. Etebari, Adel Pourghader Chobar","doi":"10.22094/JOIE.2020.1893849.1730","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1893849.1730","url":null,"abstract":"In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"99-114"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43704174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.1873983.1674
Mojtaba Enayati, E. Asadi-Gangraj, M. Paydar
This study considers outsourcing decisions in a flexible flow shop scheduling problem, in which each job can be processed by either an in-house production line or outsourced. The selected objective function aims to minimize the weighted sum of tardiness costs, in-house production costs, and outsourcing costs with respect to the jobs due date. The purpose of the problem is to select the jobs that must be processed in-house, schedule processing of the jobs in-house, and finally select and assign other jobs to the subcontractors. We develop a mixed-integer linear programming (MILP) model for the research problem. Regarding the complexity of the research problem, the MILP model cannot be used for large-scale problems. Therefore, four metaheuristic algorithms, including SA, GA, PSO, hybrid PSO-SA, are proposed to solve the problem. Furthermore, some random test problems with different sizes are generated to evaluate the effectiveness of the proposed MILP model and solution approaches. The obtained results demonstrate that the GA can obtain better solutions in comparison to the other algorithms.
{"title":"Scheduling on flexible flow shop with cost-related objective function considering outsourcing options","authors":"Mojtaba Enayati, E. Asadi-Gangraj, M. Paydar","doi":"10.22094/JOIE.2020.1873983.1674","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1873983.1674","url":null,"abstract":"This study considers outsourcing decisions in a flexible flow shop scheduling problem, in which each job can be processed by either an in-house production line or outsourced. The selected objective function aims to minimize the weighted sum of tardiness costs, in-house production costs, and outsourcing costs with respect to the jobs due date. The purpose of the problem is to select the jobs that must be processed in-house, schedule processing of the jobs in-house, and finally select and assign other jobs to the subcontractors. We develop a mixed-integer linear programming (MILP) model for the research problem. Regarding the complexity of the research problem, the MILP model cannot be used for large-scale problems. Therefore, four metaheuristic algorithms, including SA, GA, PSO, hybrid PSO-SA, are proposed to solve the problem. Furthermore, some random test problems with different sizes are generated to evaluate the effectiveness of the proposed MILP model and solution approaches. The obtained results demonstrate that the GA can obtain better solutions in comparison to the other algorithms.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"69-88"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46089326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.22094/JOIE.2020.1892184.1725
Samir Settoul, M. Zellagui, M. Zellagui, R. Chenni
The wind turbine has grown out to be one of the most common Renewable Energy Sources (RES) around the world in recent years. This study was intended to position the Wind Turbine (WT) on a wind farm to achieve the highest performance possible in Electric Distribution Network (EDN). In this paper a new optimization algorithm namely Salp Swarm Algorithm (SSA) is applied to solve the problem of optimal integration of Distributed Generation (DG) based WT (location and sizing) in EDN. The proposed algorithm is applied on practical Algerian EDN in Constantine city 73-bus in presence single and multiple WT-DGs for reducing the total active power loss. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization algorithms. A numerical simulation including comparative studies was presented to demonstrate the performance and applicability of the proposed algorithm.
近年来，风力发电机已成为世界上最常用的可再生能源之一。本研究旨在将风力涡轮机(WT)放置在风电场上，以实现配电网络(EDN)的最高性能。本文提出了一种新的优化算法Salp Swarm algorithm (SSA)来解决EDN中基于WT (location and sizing)的分布式发电(DG)的最优集成问题。将该算法应用于君士坦丁市实际的阿尔及利亚EDN中，在存在单、多wt - dg的情况下降低了总有功损耗。通过将所得结果与文献中其他优化算法的结果进行比较，证明了所提算法的有效性。通过数值模拟和对比研究，验证了该算法的性能和适用性。
{"title":"A New Optimization Algorithm for Optimal Wind Turbine Location Problem in Constantine City Electric Distribution Network Based Active Power Loss Reduction","authors":"Samir Settoul, M. Zellagui, M. Zellagui, R. Chenni","doi":"10.22094/JOIE.2020.1892184.1725","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1892184.1725","url":null,"abstract":"The wind turbine has grown out to be one of the most common Renewable Energy Sources (RES) around the world in recent years. This study was intended to position the Wind Turbine (WT) on a wind farm to achieve the highest performance possible in Electric Distribution Network (EDN). In this paper a new optimization algorithm namely Salp Swarm Algorithm (SSA) is applied to solve the problem of optimal integration of Distributed Generation (DG) based WT (location and sizing) in EDN. The proposed algorithm is applied on practical Algerian EDN in Constantine city 73-bus in presence single and multiple WT-DGs for reducing the total active power loss. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization algorithms. A numerical simulation including comparative studies was presented to demonstrate the performance and applicability of the proposed algorithm.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"14 1","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41745597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：