Elucidating two-stage flowshop multiprocessor scheduling problems using a hybrid genetic algorithm

Shun-chi Yu

International Journal of Mathematics in Operational Research, 2023, vol. 25, issue 2, 242-288

Abstract: In recent decades, genetic algorithms (GAs) have often been applied as heuristic techniques at various settings entailing production scheduling. However, early convergence is one of the problems associated with this approach. This study develops an efficient local search rule for the target-oriented rule in traditional GAs. It also addresses the problem of two-stage multiprocessor flowshop scheduling (FSP) by viewing the due window and sequence-dependent setup times as constraints faced by common flowshops with multiprocessor scheduling suites in the actual production scenario. Using the simulated data, this study verifies the effectiveness and robustness of the proposed algorithm. The results of data testing demonstrate that the proposed method may outperform other algorithms, including a significant hybrid algorithm, in addressing the problems considered.

Keywords: target-oriented; genetic algorithm; two-stage MFSS; due window; setup time. (search for similar items in EconPapers)
Date: 2023
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