Multi-objective optimization for distributed flexible job shop scheduling problem with job priority

For the distributed flexible job shop scheduling problem (DFJSP), the existing researches have predominantly focused on operation sequence, machine selection and factory assignment, and assuming that the jobs have no priority. However, in real-world manufacturing systems, production scheduling with job priority is very common and is of concern to production managers. The paper presents a DFJSP with job priority (DFJSPJP) for the first time, aiming at minimizing the makespan, total energy consumption and the weighted average time of jobs with priority. A new memetic algorithm (NMA) is designed to solve the proposed DFJSPJP. In the proposed NMA, a well-designed chromosome encoding method (CEM) is constructed to obtain a high-quality initial population. An effective local search approach (LSO) is proposed to improve the NMA’s convergence speed and fully exploit its solution space. Computational experiments conducted confirm the effectiveness of the CEM and LSO, and show that the NMA is able to easily obtain better solutions for about 90 % of the tested 60 challenging problem instances compared to other three well-known algorithms, demonstrating its superior performance on both solution quality and computational efficiency. This research will provide a theoretical basis for considering job priority issues in distributed production environments and assist manufacturers in conducting accurate production scheduling, thereby reducing resource waste and time loss caused by unreasonable production plans.