Dual Resource Constraints Flexible Job Shop Robust Scheduling Method

doi:10.3901/JME.2024.06.114

Abstract

Abstract: Aiming at the flexible job shop scheduling problem(FJSP) considering the difference in workers' skill level and the randomness of processing time, the dual resource constraint flexible job shop robust scheduling problem(DRC-FJRSP) model is constructed. The learning mechanism of particle swarm optimization(PSO) is introduced into the cross part of differential evolution (DE), and a PSO-DE hybrid solution algorithm is designed to realize the accelerated solution of the problem and effectively avoid the dilemma of search stagnation. DE, genetic algorithm(GA) and PSO are selected as comparison algorithms for simulation experiments. The experimental results show that the average robustness of the proposed PSO-DE hybrid solution algorithm is 5.331, which performs best in scheduling makespan solutions of three simulation examples. The rationality of DRC-FJRSP model and the robustness of PSO-DE hybrid solution algorithm are verified. Finally, taking the FJSP of a manufacturing enterprise as an example to solve the problem, the scheduling Gantt chart is given. The research solves the processing conflict caused by the shortage of equipment and worker resources to FJSP, reduces the random disturbance of processing time, and optimizes the makespan of scheduling.

Key words: dual resource constrains, flexible job shop, robust scheduling, PSO-DE hybrid solution algorithm

CLC Number:

TG156

LIANG Zhizhen, WANG Xiaojia. Dual Resource Constraints Flexible Job Shop Robust Scheduling Method[J]. Journal of Mechanical Engineering, 2024, 60(6): 114-126.

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