Pub Date : 2020-07-01DOI: 10.22094/JOIE.2020.579974.1605
Moein Asadi-Zonouz, M. Khalili, Hamed Tayebi
Due to the variety of products, simultaneous production of different models has an important role in production systems. Moreover, considering the realistic constraints in designing production lines attracted a lot of attentions in recent researches. Since the assembly line balancing problem is NP-hard, efficient methods are needed to solve this kind of problems. In this study, a new hybrid method based on unconscious search algorithm (USGA) is proposed to solve mixed-model assembly line balancing problem considering some realistic conditions such as parallel workstation, zoning constraints, sequence dependent setup times and learning effect. This method is a modified version of the unconscious search algorithm which applies the operators of genetic algorithm as the local search step. Performance of the proposed algorithm is tested on a set of test problems and compared with GA and ACOGA. The experimental results indicate that USGA outperforms GA and ACOGA.
{"title":"A Hybrid Unconscious Search Algorithm for Mixed-model Assembly Line Balancing Problem with SDST, Parallel Workstation and Learning Effect","authors":"Moein Asadi-Zonouz, M. Khalili, Hamed Tayebi","doi":"10.22094/JOIE.2020.579974.1605","DOIUrl":"https://doi.org/10.22094/JOIE.2020.579974.1605","url":null,"abstract":"Due to the variety of products, simultaneous production of different models has an important role in production systems. Moreover, considering the realistic constraints in designing production lines attracted a lot of attentions in recent researches. Since the assembly line balancing problem is NP-hard, efficient methods are needed to solve this kind of problems. In this study, a new hybrid method based on unconscious search algorithm (USGA) is proposed to solve mixed-model assembly line balancing problem considering some realistic conditions such as parallel workstation, zoning constraints, sequence dependent setup times and learning effect. This method is a modified version of the unconscious search algorithm which applies the operators of genetic algorithm as the local search step. Performance of the proposed algorithm is tested on a set of test problems and compared with GA and ACOGA. The experimental results indicate that USGA outperforms GA and ACOGA.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"123-140"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41448863","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 : 2020-07-01DOI: 10.22094/JOIE.2020.1883892.1700
Tooraj Karimi, M. Fathi, Yalda Yahyazade
Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed to design a risk management model that seeks to manage the risk of software development projects based on the key criteria of project time, cost, quality and scope. This is presented after making an extensive review of the literature and asking questions from experts in the field. In this regard, after identifying the risks and defining them based on the dimensions and indicators of software development projects, 22 features were identified to evaluate banking software projects. The data were collected for three consecutive years in the country's largest software development eco-system. According to Rough modelling, the most important variables affecting the cost, time, quality and scope of projects were identified and the amount of risk that a project may have in each of these dimensions was shown. Since traditional scales cannot provide the accurate estimation of project risk assessment under uncertainty, the indexes were fuzzy. Finally, the fuzzy expert system was designed by MATLAB software that showed the total risk of each project. To create a graphical user interface, the MATLAB software GUIDE was used. The system can predict the risks of each project before each project begins and helps project managers be prepared to deal with these risks and consider ways to prevent the project from failing. The results showed that quality and time risks were more important than cost and scope risks and had a greater impact on total project deviation.
{"title":"Developing a Risk Management Model for Banking Software Development Projects Based on Fuzzy Inference System","authors":"Tooraj Karimi, M. Fathi, Yalda Yahyazade","doi":"10.22094/JOIE.2020.1883892.1700","DOIUrl":"https://doi.org/10.22094/JOIE.2020.1883892.1700","url":null,"abstract":"Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed to design a risk management model that seeks to manage the risk of software development projects based on the key criteria of project time, cost, quality and scope. This is presented after making an extensive review of the literature and asking questions from experts in the field. In this regard, after identifying the risks and defining them based on the dimensions and indicators of software development projects, 22 features were identified to evaluate banking software projects. The data were collected for three consecutive years in the country's largest software development eco-system. According to Rough modelling, the most important variables affecting the cost, time, quality and scope of projects were identified and the amount of risk that a project may have in each of these dimensions was shown. Since traditional scales cannot provide the accurate estimation of project risk assessment under uncertainty, the indexes were fuzzy. Finally, the fuzzy expert system was designed by MATLAB software that showed the total risk of each project. To create a graphical user interface, the MATLAB software GUIDE was used. The system can predict the risks of each project before each project begins and helps project managers be prepared to deal with these risks and consider ways to prevent the project from failing. The results showed that quality and time risks were more important than cost and scope risks and had a greater impact on total project deviation.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"267-278"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49506946","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 : 2020-07-01DOI: 10.22094/JOIE.2020.576631.1593
Masoud Mohsendokht, M. Hashemi-Tilehnoee
Probabilistic safety assessment (PSA) which plays a crucial role in risk evaluation is a quantitative approach intended to demonstrate how a nuclear reactor meets the safety margins as part of the licensing process. Despite PSA merits, some shortcomings associated with the final results exist. Conventional PSA uses crisp values to represent the failure probabilities of basic events. This causes a high level of uncertainty due to the inherent imprecision and vagueness of failure input data. In this paper, to tackle this imperfection, a fuzzy approach is employed with fault tree analysis and event tree analysis. Thus, instead of using the crisp values, a set of fuzzy numbers is applied as failure probabilities of basic events. Hence, in the fault tree and event tree analysis, the top events and the end-states frequencies are treated as fuzzy numbers. By introducing some fuzzy importance measures the critical components which contribute maximum to the system failure and total uncertainty are identified. As a practical example, under redesign Iranian heavy water research reactor loss of coolant accident is studied. The results show that the reactor protection system has the largest index in sequences lead to a core meltdown. In addition, the emergency core cooling system has a main role in preventing abnormal conditions.
{"title":"Overcoming the uncertainty in a research reactor LOCA in level-1 PSA; Fuzzy based fault-tree/event-tree analysis","authors":"Masoud Mohsendokht, M. Hashemi-Tilehnoee","doi":"10.22094/JOIE.2020.576631.1593","DOIUrl":"https://doi.org/10.22094/JOIE.2020.576631.1593","url":null,"abstract":"Probabilistic safety assessment (PSA) which plays a crucial role in risk evaluation is a quantitative approach intended to demonstrate how a nuclear reactor meets the safety margins as part of the licensing process. Despite PSA merits, some shortcomings associated with the final results exist. Conventional PSA uses crisp values to represent the failure probabilities of basic events. This causes a high level of uncertainty due to the inherent imprecision and vagueness of failure input data. In this paper, to tackle this imperfection, a fuzzy approach is employed with fault tree analysis and event tree analysis. Thus, instead of using the crisp values, a set of fuzzy numbers is applied as failure probabilities of basic events. Hence, in the fault tree and event tree analysis, the top events and the end-states frequencies are treated as fuzzy numbers. By introducing some fuzzy importance measures the critical components which contribute maximum to the system failure and total uncertainty are identified. As a practical example, under redesign Iranian heavy water research reactor loss of coolant accident is studied. The results show that the reactor protection system has the largest index in sequences lead to a core meltdown. In addition, the emergency core cooling system has a main role in preventing abnormal conditions.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"249-266"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43373006","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 : 2020-03-01DOI: 10.22094/JOIE.2019.580054.1607
Maryam Fazelimoghadam, M. Ershadi, S. T. A. Niaki
Statistically constrained economic design for profiles usually refers to the selection of some parameters such as the sample size, sampling interval, smoothing constant, and control limit for minimizing the total implementation cost while the designed profiles demonstrate a proper statistical performance. In this paper, the Lorenzen-Vance function is first used to model the implementation costs. Then, this function is extended by the Taguchi loss function to involve intangible costs. Next, a multi-objective particle swarm optimization (MOPSO) method is employed to optimize the extended model. The parameters of the MOPSO are tuned using response surface methodology (RSM). In addition, data envelopment analysis (DEA) is employed to find efficient solutions among all near-optimum solutions found by MOPSO. Finally, a sensitivity analysis based on the principal parameters of the cost function is applied to evaluate the impacts of changes on the main parameters. The results show that the proposed model is robust on some parameters such as the cost of detecting and repairing an assignable cause, variable cost of sampling, and fixed cost of sampling.
{"title":"An Efficient Economic-Statistical Design of Simple Linear Profiles Using a Hybrid Approach of Data Envelopment Analysis, Taguchi Loss Function, and MOPSO","authors":"Maryam Fazelimoghadam, M. Ershadi, S. T. A. Niaki","doi":"10.22094/JOIE.2019.580054.1607","DOIUrl":"https://doi.org/10.22094/JOIE.2019.580054.1607","url":null,"abstract":"Statistically constrained economic design for profiles usually refers to the selection of some parameters such as the sample size, sampling interval, smoothing constant, and control limit for minimizing the total implementation cost while the designed profiles demonstrate a proper statistical performance. In this paper, the Lorenzen-Vance function is first used to model the implementation costs. Then, this function is extended by the Taguchi loss function to involve intangible costs. Next, a multi-objective particle swarm optimization (MOPSO) method is employed to optimize the extended model. The parameters of the MOPSO are tuned using response surface methodology (RSM). In addition, data envelopment analysis (DEA) is employed to find efficient solutions among all near-optimum solutions found by MOPSO. Finally, a sensitivity analysis based on the principal parameters of the cost function is applied to evaluate the impacts of changes on the main parameters. The results show that the proposed model is robust on some parameters such as the cost of detecting and repairing an assignable cause, variable cost of sampling, and fixed cost of sampling.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"99-112"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45367582","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 : 2020-03-01DOI: 10.22094/JOIE.2019.563130.1551
Teshome Bekele Dagne, Jeyraju Jayaprkash, S. G. Gebeyehu
Supply chain network design in perishable product has become a challenging task due to its short life time, spoilage of product in degradation nature and stochastic market demand. This paper focused on designing and optimizing model for perishable product in stochastic demand, which comprises multiple levels from producer, local collector, wholesaler and retailers. The ultimate goal is to optimize availability and net profit of all members in supply chain network model for avocado fruit under stochastic demand. The network model has considered the quality deterioration rate of the product with increased order of transportation time. The validity of developed model was tested with data collected from avocado supply chain network in Ethiopian market.
{"title":"Design of Supply Chain Network Model for Perishable Products with Stochastic Demand: An Optimized Model","authors":"Teshome Bekele Dagne, Jeyraju Jayaprkash, S. G. Gebeyehu","doi":"10.22094/JOIE.2019.563130.1551","DOIUrl":"https://doi.org/10.22094/JOIE.2019.563130.1551","url":null,"abstract":"Supply chain network design in perishable product has become a challenging task due to its short life time, spoilage of product in degradation nature and stochastic market demand. This paper focused on designing and optimizing model for perishable product in stochastic demand, which comprises multiple levels from producer, local collector, wholesaler and retailers. The ultimate goal is to optimize availability and net profit of all members in supply chain network model for avocado fruit under stochastic demand. The network model has considered the quality deterioration rate of the product with increased order of transportation time. The validity of developed model was tested with data collected from avocado supply chain network in Ethiopian market.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"29-37"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46049466","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 : 2020-03-01DOI: 10.22094/JOIE.2019.387.0
H. Halleh, Azam Sadati, N. Hajisharifi
Computer-aided process planning (CAPP) is an essential component in linking computer-aided design (CAD) and computer-aided manufacturing (CAM). Operation sequencing in CAPP is an essential activity. Each sequence of production operations which is produced in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number of feasible sequences increase exponentially, consequently the best sequence is to be chosen. This paper aims at presenting the application of a newly developed meta-heuristic called the hybrid teaching–learning-based optimization (HTLBO) as a global search technique for the quick identification of the optimal sequence of operations with consideration of various feasibility constraints. To do so, three case studies have been conducted to evaluate the performance of the proposed algorithm and a comparison between the proposed algorithm and the previous searches from the literature has been made. The results show that HTLBO performs well in operation sequencing problem.
{"title":"Operation Sequencing Optimization in CAPP Using Hybrid Teaching-Learning Based Optimization (HTLBO)","authors":"H. Halleh, Azam Sadati, N. Hajisharifi","doi":"10.22094/JOIE.2019.387.0","DOIUrl":"https://doi.org/10.22094/JOIE.2019.387.0","url":null,"abstract":"Computer-aided process planning (CAPP) is an essential component in linking computer-aided design (CAD) and computer-aided manufacturing (CAM). Operation sequencing in CAPP is an essential activity. Each sequence of production operations which is produced in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number of feasible sequences increase exponentially, consequently the best sequence is to be chosen. This paper aims at presenting the application of a newly developed meta-heuristic called the hybrid teaching–learning-based optimization (HTLBO) as a global search technique for the quick identification of the optimal sequence of operations with consideration of various feasibility constraints. To do so, three case studies have been conducted to evaluate the performance of the proposed algorithm and a comparison between the proposed algorithm and the previous searches from the literature has been made. The results show that HTLBO performs well in operation sequencing problem.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"123-130"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45093521","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 : 2020-03-01DOI: 10.22094/JOIE.2018.555578.1529
M. Billal, Md. Mer Mosharraf Hossain
The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to customers. The decision variables are the number and the locations of reliable DCs and retailers, the optimum number of items produced by plants, the optimum quantity of transported products, the optimum inventory of products at DCs, retailers and plants, and the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained multi-objective mixed integer linear programming model. After that, the problem is solved by using meta-heuristic algorithms that are Multi-objective Genetic Algorithm (MOGA), Fast Non-dominated Sorting Genetic Algorithms (NSGA-II) and Epsilon Constraint Methods via the MATLAB software to select the best in terms of the total supply chain cost and the total expected number of products dispatched to customers simultaneously. At the end, the performance of the proposed multi-objective optimization model of multi-product multi-period four-echelon supply chain network design is validated through three realizations and an innumerable of various analyses in a real world case study of Bangladesh. The obtained outcomes and their analyses recognize the efficiency and applicability of the proposed model under uncertainty.
{"title":"Multi-Objective Optimization for Multi-Product Multi-Period Four Echelon Supply Chain Problems Under Uncertainty","authors":"M. Billal, Md. Mer Mosharraf Hossain","doi":"10.22094/JOIE.2018.555578.1529","DOIUrl":"https://doi.org/10.22094/JOIE.2018.555578.1529","url":null,"abstract":"The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to customers. The decision variables are the number and the locations of reliable DCs and retailers, the optimum number of items produced by plants, the optimum quantity of transported products, the optimum inventory of products at DCs, retailers and plants, and the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained multi-objective mixed integer linear programming model. After that, the problem is solved by using meta-heuristic algorithms that are Multi-objective Genetic Algorithm (MOGA), Fast Non-dominated Sorting Genetic Algorithms (NSGA-II) and Epsilon Constraint Methods via the MATLAB software to select the best in terms of the total supply chain cost and the total expected number of products dispatched to customers simultaneously. At the end, the performance of the proposed multi-objective optimization model of multi-product multi-period four-echelon supply chain network design is validated through three realizations and an innumerable of various analyses in a real world case study of Bangladesh. The obtained outcomes and their analyses recognize the efficiency and applicability of the proposed model under uncertainty.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49267562","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 : 2020-03-01DOI: 10.22094/JOIE.2017.728.1463
N. Sahebjamnia, F. Goodarzian, M. Hajiaghaei-Keshteli
In this paper, a new multi-objective integer non-linear programming model is developed for designing citrus three-echelon supply chain network. Short harvest period, product specifications, high perished rate, and special storing and distributing conditions make the modeling of citrus supply chain more complicated than other ones. The proposed model aims to minimize network costs including waste cost, transportation cost, and inventory holding cost, and to maximize network’s profits. To solve the model, firstly the model is converted to a linear programming model. Then three multi-objective meta-heuristic algorithms are used including MOPSO, MOICA, and NSGA-II for finding efficient solutions. The strengths and weaknesses of MOPSO, MOICA, and NSGA-II for solving the proposed model are discussed. The results of the algorithms have been compared by several criteria consisting of number of Pareto solution, maximum spread, mean ideal distance, and diversification metric.Computational results show that MOPSO algorithm finds competitive solutions in compare with NSGA-II and MOICA.
{"title":"Optimization of Multi-period Three-echelon Citrus Supply Chain Problem","authors":"N. Sahebjamnia, F. Goodarzian, M. Hajiaghaei-Keshteli","doi":"10.22094/JOIE.2017.728.1463","DOIUrl":"https://doi.org/10.22094/JOIE.2017.728.1463","url":null,"abstract":"In this paper, a new multi-objective integer non-linear programming model is developed for designing citrus three-echelon supply chain network. Short harvest period, product specifications, high perished rate, and special storing and distributing conditions make the modeling of citrus supply chain more complicated than other ones. The proposed model aims to minimize network costs including waste cost, transportation cost, and inventory holding cost, and to maximize network’s profits. To solve the model, firstly the model is converted to a linear programming model. Then three multi-objective meta-heuristic algorithms are used including MOPSO, MOICA, and NSGA-II for finding efficient solutions. The strengths and weaknesses of MOPSO, MOICA, and NSGA-II for solving the proposed model are discussed. The results of the algorithms have been compared by several criteria consisting of number of Pareto solution, maximum spread, mean ideal distance, and diversification metric.Computational results show that MOPSO algorithm finds competitive solutions in compare with NSGA-II and MOICA.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"39-53"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41680456","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 : 2020-03-01DOI: 10.22094/JOIE.2018.558585.1539
Mohammad Ramyar, E. Mehdizadeh, S. M. H. Molana
In this research, a bi-objective model is developed to deal with a supply chain including multiple suppliers, multiple manufacturers, and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of suppliers' and producers' reliability by the considering probabilistic lead times, to improve the performance of the system and achieve a more reliable production plan. To solve the model in small sizes, a e-constraint method is used. A numerical example utilizing the real data from a paper and wood industry is designed and the model performance is assessed. With regard to the fact that the proposed bi-objective model is NP-Hard, for large-scale problems one multi-objective harmony search algorithm is used and its results are compared with the NSGA-II algorithm. The results demonstrate the capability and efficiency of the proposed algorithm in finding Pareto solutions.
{"title":"A New Bi-objective Mathematical Model to Optimize Reliability and Cost of Aggregate Production Planning System in a Paper and Wood Company","authors":"Mohammad Ramyar, E. Mehdizadeh, S. M. H. Molana","doi":"10.22094/JOIE.2018.558585.1539","DOIUrl":"https://doi.org/10.22094/JOIE.2018.558585.1539","url":null,"abstract":"In this research, a bi-objective model is developed to deal with a supply chain including multiple suppliers, multiple manufacturers, and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of suppliers' and producers' reliability by the considering probabilistic lead times, to improve the performance of the system and achieve a more reliable production plan. To solve the model in small sizes, a e-constraint method is used. A numerical example utilizing the real data from a paper and wood industry is designed and the model performance is assessed. With regard to the fact that the proposed bi-objective model is NP-Hard, for large-scale problems one multi-objective harmony search algorithm is used and its results are compared with the NSGA-II algorithm. The results demonstrate the capability and efficiency of the proposed algorithm in finding Pareto solutions.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"81-98"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49418800","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 : 2020-03-01DOI: 10.22094/JOIE.2019.567816.1565
A. Yemane, Gebremedhin Gebremicheal, Teklewold Meraha, Misgna Hailemicheal
The typical problems facing garment manufacturers are long production lead time, bottlenecking, and low productivity. The most critical phase of garment manufacturing is the sewing phase, as it generally involves a number of operations or for the simple reason that it’s labor intensive. In assembly line balancing, allocation of jobs to machines is based on the objective of minimizing the workflow among the operators, reducing the throughput time as well as the work in progress and thus increasing the productivity. Sharing a job of work between several people is called division of labor. Division of labor should be balanced equally by ensuring the time spent at each station approximately the same. Each individual step in the assembly of product has to be analyzed carefully, and allocated to stations in a balanced way over the available workstations. Each operator then carries out operations properly and the work flow is synchronized. In a detailed work flow, synchronized line includes short distances between stations, low volume of work in process, precise of planning of production times, and predictable production quantity. This study deals with modeling of assembly line balancing by combining both manual line balancing techniques with computer simulation to find the optimal solution in the sewing line of Almeda textile plc so as to improve productivity. In this research arena software, is employed to model and measure the performance of the existing and proposed sewing line of the federal police trousers sewing line model. For each operation, the researchers have taken 15 sampling observations using stopwatch and recorded the result. All the collected data are statistically analyzed with arena input analyzer for statistical significance and determination of expressions to be used to the simulation modeling; SAM is also calculated for these operations to be used to the manual line balancing. An existing systems simulation model is developed and run for 160 replications by the researchers to measure the current performance of the system in terms of resource utilization, WIP, and waiting time. The existing systems average utilization is 0.53 with a line efficiency of 42%. This study has developed a new Sewing assembly line model which has increased the system utilization to 0.69 at a line efficiency of 58.42% without incurring additional cost.
{"title":"Productivity Improvement through Line Balancing by Using Simulation Modeling","authors":"A. Yemane, Gebremedhin Gebremicheal, Teklewold Meraha, Misgna Hailemicheal","doi":"10.22094/JOIE.2019.567816.1565","DOIUrl":"https://doi.org/10.22094/JOIE.2019.567816.1565","url":null,"abstract":"The typical problems facing garment manufacturers are long production lead time, bottlenecking, and low productivity. The most critical phase of garment manufacturing is the sewing phase, as it generally involves a number of operations or for the simple reason that it’s labor intensive. In assembly line balancing, allocation of jobs to machines is based on the objective of minimizing the workflow among the operators, reducing the throughput time as well as the work in progress and thus increasing the productivity. Sharing a job of work between several people is called division of labor. Division of labor should be balanced equally by ensuring the time spent at each station approximately the same. Each individual step in the assembly of product has to be analyzed carefully, and allocated to stations in a balanced way over the available workstations. Each operator then carries out operations properly and the work flow is synchronized. In a detailed work flow, synchronized line includes short distances between stations, low volume of work in process, precise of planning of production times, and predictable production quantity. This study deals with modeling of assembly line balancing by combining both manual line balancing techniques with computer simulation to find the optimal solution in the sewing line of Almeda textile plc so as to improve productivity. In this research arena software, is employed to model and measure the performance of the existing and proposed sewing line of the federal police trousers sewing line model. For each operation, the researchers have taken 15 sampling observations using stopwatch and recorded the result. All the collected data are statistically analyzed with arena input analyzer for statistical significance and determination of expressions to be used to the simulation modeling; SAM is also calculated for these operations to be used to the manual line balancing. An existing systems simulation model is developed and run for 160 replications by the researchers to measure the current performance of the system in terms of resource utilization, WIP, and waiting time. The existing systems average utilization is 0.53 with a line efficiency of 42%. This study has developed a new Sewing assembly line model which has increased the system utilization to 0.69 at a line efficiency of 58.42% without incurring additional cost.","PeriodicalId":36956,"journal":{"name":"Journal of Optimization in Industrial Engineering","volume":"13 1","pages":"153-165"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48180740","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}
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