A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm

The classical distributed flexible job shop scheduling problem (DFJSP) mainly considers factory allocation, machine arrangement, job sequencing and transportation. To date, the relevant literature has not studied the DFJSPs with worker arrangement, which widely exists in practical manufacturing systems. In this paper, we investigate the DFJSP with worker arrangement (DFJSPW), where not only the factories, machines and operations, but the workers are considered simultaneously. A mixed-integer linear programming model is formulated for this problem. Correspondingly, an improved memetic algorithm (IMA) based on the structure of NSGA-II is proposed for the proposed DFJSPW whose objective is to minimize the makespan, maximum workload of machines and workload of workers simultaneously. In IMA, a simplified two-level encoding and four heuristic decoding methods are designed to encode and decode the individuals. A well-designed adaptive neighborhood search operator is developed to enhance the local search ability of IMA and speed its convergence. Fifty-eight benchmarks are constructed to evaluate the performance of our proposed IMA. Extensive experiments show that in most examples, IMA performs better than four well-known multi-objective algorithms, demonstrating the superiority of IMA in solving the DFJSPW.

Automated summary

This paper investigates the distributed flexible job shop scheduling problem with worker arrangement and proposes a mixed-integer linear programming model. An improved memetic algorithm is designed to solve this problem. Extensive experiments show that the proposed algorithm outperforms other multi-objective algorithms in most cases.