期刊
MATHEMATICS
卷 10, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/math10224233
关键词
job shop scheduling; fuzzy processing times; global search; hill climbing; critical path
类别
资金
- Autonomous University of Hidalgo (UAEH)
- National Council for Science and Technology (CONACYT) [F003/320109, CB-2017-2018-A1-S-43008]
- CONACYT [713103]
This paper proposes a new algorithm for FJSSP with fuzzy processing times, which explores solutions using global neighborhood handling and hill-climbing algorithm. Experimental results show that it is competitive with state-of-the-art algorithms.
The Flexible Job Shop Scheduling Problem (FJSSP) continues to be studied extensively to test new metaheuristics and because of its closeness to current production systems. A variant of the FJSSP uses fuzzy processing times instead of fixed times. This paper proposes a new algorithm for FJSSP with fuzzy processing times called the global neighborhood with hill-climbing algorithm (GN-HC). This algorithm performs solution exploration using simple operators concurrently for global search neighborhood handling. For local search, random restart hill-climbing is applied at each solution to find the best machine for each operation. For the selection of operations in hill climbing, a record of the operations defining the fuzzy makespan is employed to use them as a critical path. Finally, an estimation of the crisp makespan with the longest processing times in hill climbing is made to improve the speed of the GN-HC. The GN-HC is compared with other recently proposed methods recognized for their excellent performance, using 6 FJSSP instances with fuzzy times. The obtained results show satisfactory competitiveness for GN-HC compared to state-of-the-art algorithms. The GN-HC implementation was performed in Matlab and can be found on GitHub (check Data Availability Statement at the end of the paper).
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