Multi-Objective Optimization of the Dynamic and Flexible Job Shop Scheduling Problem Under Workers Fatigue Constraints

Abstract

A massive number of studies has tackled the scheduling problem, but they mainly seek to solve the classic problem by reducing the real constraints of the environment like workers’ fatigue, which may lead to defective production, and the occurrence of unexpected events that makes the initial scheduling obsolete. In this paper, we study the multi-objective dynamic flexible job shop-scheduling problem under workers’ fatigue constraints (DFJSP-WF) through three unexpected events: job insertion, machine breakdown and job cancellation. First, a multi-objective model is established with objectives to minimize makespan and total weighted tardiness, earliness and rejected parts due to workers’ errors, which depend on workers’ fatigue. Second, to deal with this model, a non-dominated sorting genetic algorithm II (NSGA-II) is adapted. Computational results are presented using three sets of well-known benchmark literature instances.