An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers

The traditional distributed flexible job shop scheduling problem (DFJSP) assumes that operations of a job cannot be transferred between different factories. However, in real-world production settings, the operations of a job may need to be processed in different factories owing to requirements of economic globalization or complexity of the job. Hence, in this paper, we propose a distributed flexible job shop scheduling problem with transfers (DFJSPT), in which operations of a job can be processed in different factories. An efficient memetic algorithm (EMA) is proposed to solve the DFJSPT with the objectives of minimizing the makespan, maximum workload, and total energy consumption of factories. In the proposed EMA, a well-designed chromosome presentation and initialization methods are presented to obtain a high-quality initial population. Several crossover and mutation operators and three effective neighborhood structures are designed to expand the search space and accelerate the convergence speed of the solution. Forty benchmark instances of the DFJSPT are constructed to evaluate the EMA and facilitate further studies. The Taguchi method of design of experiments is used to obtain the best combination of key EMA parameters. Extensive computational experiments are carried out to compare the EMA with three well-known algorithms from the literature. The computational results show that the EMA can obtain better solutions for approximately 90% of the tested benchmark instances compared to the three wellknown algorithms, thereby demonstrating the DFJSPT's competitive performance and efficiency. (c) 2020 Elsevier Ltd. All rights reserved.