A novel hybrid GA-PSO optimization technique for multi-location facility maintenance scheduling problem

Abstract

Nowadays, due to increasing the construction cost and limiting the workforce and capital of organizations, the role of maintenance activity and management to maintain the facilities and improve their performance has become more important for all the facilities. Most of the facility life cycle consists of several stages including planning, designing, operation, maintenance, and destruction which most of the time and cost have been spent on the operation and maintenance stages. In this article a new mathematical model has been proposed to optimize multi location facility maintenance scheduling problem. In proposed model combination of Genetic Algorithm and the Particle Swarm Optimization has been adopted to solve problems for organizations with multi location assets. The main innovations of this paper are: (a) considering multiple locations with specific distance and travel time between each location. (b) considering different work skills for doing maintenance plans. (c) Comparing the outsourcing work with doing it by the company's own experts and choosing the best scenario. In order to evaluate the proposed model performance, proposed model results have been compared with Golpira, Koay and Javanmard models in sixty scenarios. Scenarios are different in number of assets location, number of crews and cost coefficient value. The performance of the proposed model in most of scenarios is superior than other models.