Configuring and optimizing the maintenance support resource based on a double layer algorithm

Abstract

Optimizing and configuring the maintenance support equipment is investigated and it is translated into RACP (resource availability cost problem) with uncertain activity durations. The problem aims to minimize the cost of the maintenance support system with the constraints of project finish time. Precedence relations among maintenance activities are coded by random key, and then a double layer optimization algorithm is used to solve the problem. The inner layer configures the amount of maintenance equipment, while the outer layer schedules the project execution procedure. Particle swarm optimization embedded with scatter search is applied to produce high quality solutions. Finally, comparative computational experiments are designed and run on benchmark datasets to test the efficiency and performance of methods. The computational results show that this optimization method has significant value to solve the resource configuration problem and the outcome has actual engineering significance.