A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty
Production system design has lots of restrictions and complex assumptions that cause difficulty in decision making. One of the most important of them is the complexity of the relationship between man and machine. In this regard, operator learning is recognized as an effective element in completing t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/2648936 |
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| author | Maryam Ebrahimi Parviz Dinari Mohamad Samaei Rouhollah Sohrabi Soheil Sherafatianfini |
| author_facet | Maryam Ebrahimi Parviz Dinari Mohamad Samaei Rouhollah Sohrabi Soheil Sherafatianfini |
| author_sort | Maryam Ebrahimi |
| collection | DOAJ |
| description | Production system design has lots of restrictions and complex assumptions that cause difficulty in decision making. One of the most important of them is the complexity of the relationship between man and machine. In this regard, operator learning is recognized as an effective element in completing tasks in the production system. In this research, a mathematical model for scheduling the parallel machines in terms of job degradation and operator learning is presented. As one of the most important assumptions, the sequence-dependent setup time is of concern. In other words, jobs are processed sequentially, and there is a sequence-dependent setup time. Moreover, the processing time and delivery due date are considered uncertain, and a fuzzy conversion method is used to deal with this uncertainty. The proposed mathematical model is a multiobjective one and tries to minimize speed and completion time. In order to optimize this mathematical model, the genetic algorithm (GA) and variable neighborhood search (VNS) algorithms have been used. A new hybrid algorithm has also been developed for this problem. The results show that the hybrid algorithm can provide more substantial results than classical algorithms. Moreover, it is revealed that a large percentage of Pareto solutions in the proposed algorithm have a generation time of more than 80% of the algorithm’s execution time. |
| format | Article |
| id | doaj-art-e42722370c2b4326b9e5cecbb534d475 |
| institution | OA Journals |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-e42722370c2b4326b9e5cecbb534d4752025-08-20T02:21:34ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/2648936A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under UncertaintyMaryam Ebrahimi0Parviz Dinari1Mohamad Samaei2Rouhollah Sohrabi3Soheil Sherafatianfini4Department of Information Technology ManagementDepartment of Industrial EngineeringDepartment of Industrial EngineeringFaculty of Economics and Social SciencesDepartment of Industrial EngineeringProduction system design has lots of restrictions and complex assumptions that cause difficulty in decision making. One of the most important of them is the complexity of the relationship between man and machine. In this regard, operator learning is recognized as an effective element in completing tasks in the production system. In this research, a mathematical model for scheduling the parallel machines in terms of job degradation and operator learning is presented. As one of the most important assumptions, the sequence-dependent setup time is of concern. In other words, jobs are processed sequentially, and there is a sequence-dependent setup time. Moreover, the processing time and delivery due date are considered uncertain, and a fuzzy conversion method is used to deal with this uncertainty. The proposed mathematical model is a multiobjective one and tries to minimize speed and completion time. In order to optimize this mathematical model, the genetic algorithm (GA) and variable neighborhood search (VNS) algorithms have been used. A new hybrid algorithm has also been developed for this problem. The results show that the hybrid algorithm can provide more substantial results than classical algorithms. Moreover, it is revealed that a large percentage of Pareto solutions in the proposed algorithm have a generation time of more than 80% of the algorithm’s execution time.http://dx.doi.org/10.1155/2022/2648936 |
| spellingShingle | Maryam Ebrahimi Parviz Dinari Mohamad Samaei Rouhollah Sohrabi Soheil Sherafatianfini A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty Discrete Dynamics in Nature and Society |
| title | A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty |
| title_full | A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty |
| title_fullStr | A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty |
| title_full_unstemmed | A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty |
| title_short | A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty |
| title_sort | new approach based on the learning effect for sequence dependent parallel machine scheduling problem under uncertainty |
| url | http://dx.doi.org/10.1155/2022/2648936 |
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