A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times
In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, mach...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/3530701 |
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| _version_ | 1832566149019074560 |
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| author | Chia-Lun Hsu Win-Chin Lin Lini Duan Jan-Ray Liao Chin-Chia Wu Juin-Han Chen |
| author_facet | Chia-Lun Hsu Win-Chin Lin Lini Duan Jan-Ray Liao Chin-Chia Wu Juin-Han Chen |
| author_sort | Chia-Lun Hsu |
| collection | DOAJ |
| description | In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm. |
| format | Article |
| id | doaj-art-97dd684d1f8b415fa2cacb0851214071 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-97dd684d1f8b415fa2cacb08512140712025-02-03T01:05:04ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/35307013530701A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing TimesChia-Lun Hsu0Win-Chin Lin1Lini Duan2Jan-Ray Liao3Chin-Chia Wu4Juin-Han Chen5Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Statistics, Feng Chia University, Taichung 40724, TaiwanSchool of Economics & Management, Shenyang University of Chemical Technology, Shenyang 110142, ChinaDepartment of Electrical Engineering, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Statistics, Feng Chia University, Taichung 40724, TaiwanDepartment of Industrial Engineering & Management, Cheng Shiu University, Kaohsiung 83347, TaiwanIn many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm.http://dx.doi.org/10.1155/2020/3530701 |
| spellingShingle | Chia-Lun Hsu Win-Chin Lin Lini Duan Jan-Ray Liao Chin-Chia Wu Juin-Han Chen A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times Discrete Dynamics in Nature and Society |
| title | A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times |
| title_full | A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times |
| title_fullStr | A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times |
| title_full_unstemmed | A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times |
| title_short | A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times |
| title_sort | robust two machine flow shop scheduling model with scenario dependent processing times |
| url | http://dx.doi.org/10.1155/2020/3530701 |
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