Balancing setup workers load of flexible job shop scheduling using hybrid genetic algorithm with tabu search strategy

Abstract

This paper describes optimisation of a multi-objective flexible job shop scheduling problem (MO-FJSP) in small and medium-sized enterprises (SMEs) where widely various products are manufactured in make-to-order (MTO) mode. A genetic algorithm using tabu search strategy was applied to solve the MO-FJSP incorporating weighted tardiness, setup worker load balance, and work-in-process. From experiments using data based on real-world SME, the solutions obtained using the proposed method are compared with those obtained by conventional earliest due date (EDD), and GA using multi-island. The results confirmed the effectiveness of the proposed method. Results imply that the proposed approach is applicable not only for production scheduling but also for estimating the investment of resources such as machine and worker capacity.

Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement

“…As detailed in Figure 12, papers adding human factors to the objective function optimize the workers' cost most of the time. On the same time, some papers minimize ergonomic risks (Hongyu and Xiuli, 2021;Coca et al, 2019), worker's fatigue (Tan et al, 2021) or worker's workload (Luo et al, 2022;Sano et al, 2016).…”

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

“…According to our state-of-the-art [13], only 28% of the articles that incorporate operators consider them as part of the objective function, and even then, it's usually done through their cost. However, some articles aim to minimize ergonomic risks [5,6], worker fatigue [14], or operator workload [15,16]. In particular, Jaber and Neumann [17] and Sun et al [18] consider rest time for workers to ensure their safety and efficiency.…”