Joint decision-making of virtual module formation and scheduling considering queuing time
Formation and scheduling are the most important decisions in the virtual modular manufacturing system; however, the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results. The joint decision of formation and scheduling is very...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2023-09-01
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| Series: | Data Science and Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764923000206 |
| _version_ | 1797669723888943104 |
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| author | Liang Mei Liu Yue Shilun Ge |
| author_facet | Liang Mei Liu Yue Shilun Ge |
| author_sort | Liang Mei |
| collection | DOAJ |
| description | Formation and scheduling are the most important decisions in the virtual modular manufacturing system; however, the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results. The joint decision of formation and scheduling is very important for system design. Complex and discrete manufacturing enterprises such as shipbuilding and aerospace often comprise multiple tasks, processes, and parallel machines, resulting in complex routes. The queuing time of parts in front of machines may account for 90% of the production cycle time. This study established a weighted allocation model of a formation-scheduling joint decision problem considering queuing time in system. To solve this nondeterministic polynomial (NP) problem, an adaptive differential evolution-simulated annealing (ADE-SA) algorithm is proposed. Compared with the standard differential evolution (DE) algorithm, the adaptive mutation factor overcomes the disadvantage that the scale of DE’s differential vector is difficult to control. The selection strategy of the SA algorithm compensates for the deficiency that DE’s greedy strategy may fall into a local optimal solution. The comparison results of four algorithms of a series of random examples demonstrate that the overall performance of ADE-SA is superior to the genetic algorithm, and average iteration, maximum completion time, and move time are 24%, 11%, and 7% lower than the average of other three algorithms, respectively. The method can generate the joint decision-making scheme with better overall performance, and effectively identify production bottlenecks through quantitative analysis of queuing time. |
| first_indexed | 2024-03-11T20:48:41Z |
| format | Article |
| id | doaj.art-adde283dfc8347aabbd7b8c4267cd9c6 |
| institution | Directory Open Access Journal |
| issn | 2666-7649 |
| language | English |
| last_indexed | 2024-03-11T20:48:41Z |
| publishDate | 2023-09-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj.art-adde283dfc8347aabbd7b8c4267cd9c62023-10-01T06:03:32ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492023-09-0163134143Joint decision-making of virtual module formation and scheduling considering queuing timeLiang Mei0Liu Yue1Shilun Ge2School of Humanities and Social Sciences, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212000, China; Corresponding author.Illinois Institute of Technology, College of Computer Science, Chicago, 60616, USASchool of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212000, ChinaFormation and scheduling are the most important decisions in the virtual modular manufacturing system; however, the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results. The joint decision of formation and scheduling is very important for system design. Complex and discrete manufacturing enterprises such as shipbuilding and aerospace often comprise multiple tasks, processes, and parallel machines, resulting in complex routes. The queuing time of parts in front of machines may account for 90% of the production cycle time. This study established a weighted allocation model of a formation-scheduling joint decision problem considering queuing time in system. To solve this nondeterministic polynomial (NP) problem, an adaptive differential evolution-simulated annealing (ADE-SA) algorithm is proposed. Compared with the standard differential evolution (DE) algorithm, the adaptive mutation factor overcomes the disadvantage that the scale of DE’s differential vector is difficult to control. The selection strategy of the SA algorithm compensates for the deficiency that DE’s greedy strategy may fall into a local optimal solution. The comparison results of four algorithms of a series of random examples demonstrate that the overall performance of ADE-SA is superior to the genetic algorithm, and average iteration, maximum completion time, and move time are 24%, 11%, and 7% lower than the average of other three algorithms, respectively. The method can generate the joint decision-making scheme with better overall performance, and effectively identify production bottlenecks through quantitative analysis of queuing time.http://www.sciencedirect.com/science/article/pii/S2666764923000206Joint decision-makingQueue timeVirtual moduleHybrid algorithm |
| spellingShingle | Liang Mei Liu Yue Shilun Ge Joint decision-making of virtual module formation and scheduling considering queuing time Data Science and Management Joint decision-making Queue time Virtual module Hybrid algorithm |
| title | Joint decision-making of virtual module formation and scheduling considering queuing time |
| title_full | Joint decision-making of virtual module formation and scheduling considering queuing time |
| title_fullStr | Joint decision-making of virtual module formation and scheduling considering queuing time |
| title_full_unstemmed | Joint decision-making of virtual module formation and scheduling considering queuing time |
| title_short | Joint decision-making of virtual module formation and scheduling considering queuing time |
| title_sort | joint decision making of virtual module formation and scheduling considering queuing time |
| topic | Joint decision-making Queue time Virtual module Hybrid algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2666764923000206 |
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