Conventional research on flexible job shop scheduling (FJSP) often overlooks critical factors such as workpiece handling, machine preventive maintenance, and variable machining speeds, resulting in scheduling schemes with limited practicality and suboptimal performance. To tackle these issues, this study establishes a Flexible Job Shop Scheduling Problem with Workpiece Handling and Machine Preventive Maintenance (WHMPM-FJSP) model, aiming to minimize both makespan and total energy consumption. An Improved Multi-Objective Discrete Grey Wolf Optimization (IMOD-GWO) algorithm is proposed to solve this model. The algorithm incorporates three key innovations: (1) A tri-level encoding structure that integrates machine assignments, operation sequences, and machining speed selection, tailored to the problem’s characteristics. (2) Multiple effective population initialization strategies combined with novel individual update mechanisms. (3) Implementation of distributed computing methods to enhance search efficiency within limited timeframes. To verify the rationality and efficacy of the model and the algorithm, comparative experiments were conducted using benchmark instances of varying scales against existing multi-objective optimization algorithms. The experimental results show that in medium- to large-scale cases, IMOD-GWO outperforms other methods, demonstrating significant advantages and highlighting its enhanced global search capability in solving WHMPM-FJSP problems. The proposed model and algorithm effectively solve the scheduling problem in flexible workshops with integrated processing and maintenance, demonstrating strong performance and practicality.
Xu et al. (Sat,) studied this question.