A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm. (30th November 2022)

• Record Type: Journal Article
• Authors: Luo, Qiang; Deng, Qianwang; Gong, Guiliang; Guo, Xin; Liu, Xiahui
• Is Part Of: Expert systems with applications. Volume 207(2022)
• Publication Date: 2022-11-30
• Subjects: Distributed flexible job shop scheduling -- Worker arrangement -- Memetic algorithm -- Adaptive neighborhood search; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117984
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Distributed flexible job shop scheduling problem with worker (DFJSPW) is studied. An improved memetic algorithm (IMA) is proposed to solve the DFJSPW. An effective encoding and decoding methods are presented in the IMA. An adaptive neighbourhood search operator is designed in the IMA. Elaborate experiments prove the effectiveness of the IMA in solving the DFJSPW. Abstract: 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.