Collaborative scheduling of spare parts production and service workers driven by distributed maintenance demand

We analyse the joint scheduling of spare parts production and service workers driven by distributed maintenance demand. For each failure, a service worker and a necessary spare part must be assigned for on-site maintenance. In particular, the spare part is not supplied from stock, but is delivered directly from the factory to the maintenance site through just-in-time production. Spare parts production and worker allocation are integrated to optimize two objectives: customer satisfaction and total service cost. To solve the problem, we first construct a mathematical model. Subsequently, a modified non-dominated neighbor immune algorithm (MNNIA) is proposed, in which a knowledge-based initialization rule, an idle time insertion method, and two problem-specific local search operators are developed to enhance exploitation. Extensive experiments and comparisons are conducted to demonstrate the superiority of MNNIA. Furthermore, compared with that of the separate decisionmaking mode of spare parts production and worker arrangement, the effectiveness of the integrated scheduling mode is verified.

Automated summary

This study analyzes the joint scheduling of spare parts production and service workers driven by distributed maintenance demand. By integrating spare parts production and worker allocation, the study aims to optimize customer satisfaction and total service cost. A modified algorithm is proposed to solve the problem, and extensive experiments demonstrate its superiority.