Joint optimization of integrated mixed maintenance and distributed two-stage hybrid flow-shop production for multi-site maintenance requirements

Similar to the synergy of machines and workers in the production process, a perfect operation and maintenance (O&M) service can be implemented only when spare parts and service workers are available at the same time. Considering the production of spare parts and the limited number of workers, a collaborative optimization problem of spare parts production and worker arrangement driven by O&M is proposed in this paper. Specif-ically, in view of the characteristics that distributed hybrid flow-shops are often encountered in the industrial environment and one O&M activity can be completed by one worker or two workers jointly, spare parts are produced in distributed two-stage hybrid flow shop, and worker service adopts a mixed mode of one worker and two workers. To obtain a satisfactory joint optimization scheme of spare parts and workers, this paper first es-tablishes a mixed integer programming model with dual objectives: the total weighted earliness/tardiness penalty and the number of lost orders. Then, an improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is proposed to solve it. In INSGA-II, an initialization rule, an initialization optimization strategy and three local search operators are designed. Next, to give full play to the best performance of the algorithm, the key pa-rameters are set by a full factor experiment. Finally, extensive experiments are carried out to verify that INSGA-II has in the studied.

Automated summary

This paper proposes a collaborative optimization problem of spare parts production and worker arrangement driven by O&M. An improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is used to solve the mixed integer programming model, which considers the production of spare parts and the limited number of workers. Extensive experiments validate the effectiveness of INSGA-II.