Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization

Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases: firstly, this study predicts required capacity of each machine through extreme learning machine algorithm. Based on analyzing historical data, the opportunistic periods are calculated for implementing PM tasks to have less impact on production and obtain the PM interval and duration. Secondly, this study obtains the scheduling planning and the least number of maintenance personnel through an improved ant colony optimization algorithm. Finally, the feasibility and benefits of this approach are investigated based on empirical study by using historical data from real manufacturing execution system and equipment maintenance system. Experimental results demonstrate the effectiveness of proposed approach, reduce personnel number while guarantee the maintenance tasks. Therefore, the proposed approach is beneficial to improve the company's production efficiency.

Automated summary

This paper introduces a novel approach for integrating preventive maintenance into production planning of a manufacturing system, relying on prediction algorithms, opportunistic scheduling, and ant colony optimization for personnel planning. Experimental results validate the effectiveness and benefits of the proposed approach in improving production efficiency.