Research on Optimization of Maintenance Task Scheduling for Metro Systems Based on Resource Constraints

Abstract

Optimizing the maintenance scheduling of metro systems is a crucial task that necessitates meticulous coordination of labor, equipment, and workspaces to ensure optimal system performance and safety. A mathematical model and a two-stage teaching-learning-based optimization (TLBO)-resource operators crossover (ROC) algorithm are proposed aiming at optimizing the scheduling of maintenance tasks for metro systems. The mathematical model focuses on minimizing the makespan, which represents the total duration or time required to complete a set of tasks or activities within a project. In addition, it takes into account the need to balance the load on labor and workspaces, considering environmental constraints, limited resources, and strict scheduling requirements. A two-stage TLBO-ROC algorithm is specifically designed to enhance the scheduling process. It achieves this by iteratively updating the local best individual matrix, dividing it into groups, and adjusting the resource allocation. This algorithm effectively reduces the makespan while also achieving improved balance in the workspace load. The model and algorithm are tested on the Shenzhen metro system. Experimental results demonstrate that our proposed approach significantly reduces the makespan. In comparison to manual scheduling plans, the algorithm achieved a remarkable 28.06% reduction in the makespan. Moreover, when compared to benchmark algorithms, our proposed algorithm not only improves the makespan but also ensures more equitable occupation of workspaces by maintaining a similar balance in labor load.