Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems
碩士 === 元智大學 === 工業工程與管理學系 === 97 === This research studies unrelated parallel machine scheduling problems (UPMSP) with two objectives – minimizing average tardiness and the number of tardy jobs. To make the study better fit a real world application, job processing times are defined as triangular f...
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ndltd-TW-097YZU050310322016-05-04T04:17:08Z http://ndltd.ncl.edu.tw/handle/44057636397991246854 Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems 模糊多目標非相關平行機台排程問題之解算 Ai-Lian Jhong 鍾愛蓮 碩士 元智大學 工業工程與管理學系 97 This research studies unrelated parallel machine scheduling problems (UPMSP) with two objectives – minimizing average tardiness and the number of tardy jobs. To make the study better fit a real world application, job processing times are defined as triangular fuzzy numbers whereas due dates as trapezoidal fuzzy numbers. Three state-of-art algorithms, multi-objective simulated annealing (MOSA), Pareto archived evolution strategy (PAES), and archived multi-objective simulated annealing (AMOSA), are applied to solve the bi-objective UPMSP. Each algorithm consists of three decoding schemes, one of which is the commonly used decoding scheme - machine-group assignment with fixed weighted vectors (MGA_FW) on objectives, and the other two are novel schemes: (1) machine-group matching improvement with fixed weighted vectors (MGMI_FW); (2) machine-group matching improvement with random weighted vectors (MGMI_RW). Benchmark instances with problem size 100 (jobs) x 5 (machines) and 100 x 10 using three levels for each of the two parameters, due-date tightness and due-date range, are generated according to a method in the literature. Our experimental results indicate that the decoding scheme MGMI_RW excels in eight out of nine instances. Additionally, AMOSA_MGMI_RW outperforms the others for problems with loose due-date tightness, and PAES_MGMI_RW works best for problems with moderate due-date tightness. Finally, for problems with strict due-date tightness, the MOSA_MGMI_FW surpasses the others for narrow due-date range, while PAES_MGMI_FW and PAES_MGMI_RW are better for moderate- to wide- due-date range. Chiuh-Cheng Chyu 徐旭昇 2009 學位論文 ; thesis 112 zh-TW |
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碩士 === 元智大學 === 工業工程與管理學系 === 97 === This research studies unrelated parallel machine scheduling problems (UPMSP) with two objectives – minimizing average tardiness and the number of tardy jobs. To make the study better fit a real world application, job processing times are defined as triangular fuzzy numbers whereas due dates as trapezoidal fuzzy numbers. Three state-of-art algorithms, multi-objective simulated annealing (MOSA), Pareto archived evolution strategy (PAES), and archived multi-objective simulated annealing (AMOSA), are applied to solve the bi-objective UPMSP. Each algorithm consists of three decoding schemes, one of which is the commonly used decoding scheme - machine-group assignment with fixed weighted vectors (MGA_FW) on objectives, and the other two are novel schemes: (1) machine-group matching improvement with fixed weighted vectors (MGMI_FW); (2) machine-group matching improvement with random weighted vectors (MGMI_RW).
Benchmark instances with problem size 100 (jobs) x 5 (machines) and 100 x 10 using three levels for each of the two parameters, due-date tightness and due-date range, are generated according to a method in the literature. Our experimental results indicate that the decoding scheme MGMI_RW excels in eight out of nine instances. Additionally, AMOSA_MGMI_RW outperforms the others for problems with loose due-date tightness, and PAES_MGMI_RW works best for problems with moderate due-date tightness. Finally, for problems with strict due-date tightness, the MOSA_MGMI_FW surpasses the others for narrow due-date range, while PAES_MGMI_FW and PAES_MGMI_RW are better for moderate- to wide- due-date range.
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author2 |
Chiuh-Cheng Chyu |
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Chiuh-Cheng Chyu Ai-Lian Jhong 鍾愛蓮 |
author |
Ai-Lian Jhong 鍾愛蓮 |
spellingShingle |
Ai-Lian Jhong 鍾愛蓮 Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems |
author_sort |
Ai-Lian Jhong |
title |
Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems |
title_short |
Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems |
title_full |
Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems |
title_fullStr |
Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems |
title_full_unstemmed |
Archive-Based Optimization Heuristics for Fuzzy Multi-Objective Unrelated Parallel Machine Scheduling Problems |
title_sort |
archive-based optimization heuristics for fuzzy multi-objective unrelated parallel machine scheduling problems |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/44057636397991246854 |
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