Parallel machine scheduling with splitting jobs by a hybrid differential evolution algorithm

Authors:
Wan-Liang Wang;Hai-Yan Wang;Yan-Wei Zhao;Li-Ping Zhang;Xin-Li Xu
Affiliations:
College of computer science and technology, Zhejiang University of Technology, Hangzhou 310014, PR China;Mechanical & Electrical Engineering College, Jiaxing University, Jiaxing 314000, PR China;Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of Ministry of Education, Zhejiang University of Technology, Hangzhou 310014, PR China;Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of Ministry of Education, Zhejiang University of Technology, Hangzhou 310014, PR China;Key Laboratory of Special Purpose Equipment and Advanced Processing Technology of Ministry of Education, Zhejiang University of Technology, Hangzhou 310014, PR China
Venue:
Computers and Operations Research
Year:
2013

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Abstract

The problem of parallel machine scheduling for minimizing the makespan is an open scheduling problem with extensive practical relevance. It has been proved to be non-deterministic polynomial hard. Considering a job's batch size greater than one in the real manufacturing environment, this paper investigates into the parallel machine scheduling with splitting jobs. Differential evolution is employed as a solution approach due to its distinctive feature, and a new crossover method and a new mutation method are brought forward in the global search procedure, according to the job splitting constraint. A specific local search method is further designed to gain a better performance, based on the analytical result from the single product problem. Numerical experiments on the performance of the proposed hybrid DE on parallel machine scheduling problems with splitting jobs covering identical and unrelated machine kinds and a realistic problem are performed, and the results indicate that the algorithm is feasible and efficient.