Bi-objective Permutation Flow Shop Scheduling with Machine Availability

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Bi-objective Permutation Flow Shop Scheduling with Machine Availability

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[1]XUAN Hua,LI Haiyun,LI Bing.Bi-objective Permutation Flow Shop Scheduling with Machine Availability[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):17-23.[doi:10.13705/j.issn.1671-6833.2022.05.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 05 Page number: 17-23 Column: Public date: 2022-08-22

Title:: Bi-objective Permutation Flow Shop Scheduling with Machine Availability

Author(s):: XUAN Hua; LI Haiyun; LI Bing; School of Management, Zhengzhou University, Zhengzhou 450001, China

Keywords:: bi-objective permutation flow shop; machine availability; release time; improved genetic algorithm; generationschemes of neighborhood solutions

CLC:: TB49

DOI:: 10.13705/j.issn.1671-6833.2022.05.003

Abstract:: A permutation flow shop scheduling problem with machine availability was studied. An improved genetic algorithm was proposed by introducing CDS heuristic algorithm and local search so that the total weighted completion time and total weighted tardiness were minimized. To improve the quality of the initial job processing sequence group, the CDS heuristic algorithm is applied to generate 40% of the group and the remaining 60% of the initial job processing sequence group was yielded by random procedure. For the job processing sequence after crossover and mutation, three generation schemes of neighborhood solutions based on pair-wise exchange, single-job insertion and multiple-job insertion were designed to carry out local search in order to extend the search space. The proposed improved genetic algorithm was tested with three genetic algorithm based heuristic algorithms. The results showed that the target improvement rate of the proposed algorithm was 5.05%, 3.09% and 7.33% in the average 77.65 s, compares with other algorithms. It also showed that the proposed algorithm could obtain better target values in a shorter time. With the increase of the problem scale, the improvement effect was better.

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Last Update: 2022-08-20