A Global Neighborhood with Hill-Climbing Algorithm for Fuzzy Flexible Job Shop Scheduling Problem

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Abstract

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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APA

Seck-Tuoh-Mora, J. C., Escamilla-Serna, N. J., Montiel-Arrieta, L. J., Barragan-Vite, I., & Medina-Marin, J. (2022). A Global Neighborhood with Hill-Climbing Algorithm for Fuzzy Flexible Job Shop Scheduling Problem. Mathematics, 10(22). https://doi.org/10.3390/math10224233

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