A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages
In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2022-01-01
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| Series: | Sustainable Operations and Computers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666412721000362 |
| _version_ | 1797976477764943872 |
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| author | Mona Jabbari Madjid Tavana Parviz Fattahi Fatemeh Daneshamooz |
| author_facet | Mona Jabbari Madjid Tavana Parviz Fattahi Fatemeh Daneshamooz |
| author_sort | Mona Jabbari |
| collection | DOAJ |
| description | In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency. |
| first_indexed | 2024-04-11T04:51:31Z |
| format | Article |
| id | doaj.art-d31f07c8458e4a2e8f5cdcf6a55944ed |
| institution | Directory Open Access Journal |
| issn | 2666-4127 |
| language | English |
| last_indexed | 2024-04-11T04:51:31Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Sustainable Operations and Computers |
| spelling | doaj.art-d31f07c8458e4a2e8f5cdcf6a55944ed2022-12-27T04:37:38ZengKeAi Communications Co. Ltd.Sustainable Operations and Computers2666-41272022-01-0132232A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stagesMona Jabbari0Madjid Tavana1Parviz Fattahi2Fatemeh Daneshamooz3Department of Finance, Providence College, Providence, Rhode IslandBusiness Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141; Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, Paderborn, Germany; Corresponding author at: Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141, United States.Department of Industrial Engineering, Alzahra University, Tehran, IranDepartment of Industrial Engineering, Bu-Ali Sina University, Hamedan, IranIn this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency.http://www.sciencedirect.com/science/article/pii/S2666412721000362Flow shopParallel assembly stagesSchedulingMetaheuristicTaguchi |
| spellingShingle | Mona Jabbari Madjid Tavana Parviz Fattahi Fatemeh Daneshamooz A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages Sustainable Operations and Computers Flow shop Parallel assembly stages Scheduling Metaheuristic Taguchi |
| title | A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages |
| title_full | A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages |
| title_fullStr | A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages |
| title_full_unstemmed | A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages |
| title_short | A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages |
| title_sort | parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages |
| topic | Flow shop Parallel assembly stages Scheduling Metaheuristic Taguchi |
| url | http://www.sciencedirect.com/science/article/pii/S2666412721000362 |
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