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Solving The Flexible Job Shop Problem using Multi-Objective Optimizer with Solution Characteristic Extraction |
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PP: 1815-1830 |
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doi:10.18576/amis/100523
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Author(s) |
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Sheng-Ta Hsieh,
Shi-Jim Yen,
Chun-Ling Lin,
Shih-Yuan Chiu,
Tsan-Cheng Su,
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Abstract |
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| It is difficult to find optimal scheduling solutions for abstract scheduling problems with mass parallel tasks on multiprocessors
because they are NP-complete. In this paper, a solution searching strategy called solution characteristic extraction is proposed
as a multi-objective optimizer for solving flexible job shop problems (FJSP). These problems are concerned with finishing assigned
jobs with minimal critical machine workload, total workload, and completion times. A suitable job assignment must consider processor
performance, job complexity, and job suitability for each individual processor simultaneously. To test the efficiency and robustness
of the proposed method, the experiments will contain two groups of benchmarks; with, and without release time constraints. Each
benchmark includes numbers of heterogeneous processors and different jobs for execution. The results indicate the proposed method
can find more potential solutions, and outperform related methods. |
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