A bi-objective model of preventive maintenance planning in distributed systems considering vehicle routing problem. (June 2018)

• Record Type: Journal Article
• Authors: Rashidnejad, Mehdi; Ebrahimnejad, Sadoullah; Safari, Jalal
• Is Part Of: Computers & industrial engineering. Volume 120(2018)
• Page Start: 360
• Page End: 381
• Publication Date: 2018-06
• Subjects: Maintenance scheduling -- Geographically distributed assets -- Vehicle routing problem -- Preventive maintenance -- NSGA-II; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.05.001
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A bi-objective model is proposed to optimize the preventive maintenance planning with VRP's constraints for routing the maintenance teams in distributed systems. Maintenance scheduling and routing of the maintenance teams are considered in the model, simultaneously. An adapted NSGA-II with new crossover and mutation operators is proposed to solve problems in large scale. The model is applied in a real case study (ATMs of a bank in Tehran, Iran). The performance of the model is investigated by sensitivity analysis. Abstract: Preventive maintenance scheduling has a significant influence on reliability and availability of the assets by reason of avoiding inadvertent failures. Performing the maintenance activities at the right time is a crucial issue in maintenance scheduling. Geographically distributed assets make the maintenance scheduling problem more complicated because of considering routing constraints and necessitous travel time to reach the assets. In this paper, the problem of maintenance planning of geographically distributed assets is investigated. This is first time in the literature that a research observes bi-objective model of preventive maintenance planning in geographically dispersed systems through prognostic information and Remaining Useful Life (RUL). In this paper, state-of-the-art bi-objective model for maintenance planning of geographically distributed assets and a solution method are introduced. In this research a multi-objective metaheuristic method, termed Non-dominated Sorting Genetic Algorithm II (NSGA-II) is advanced to solve this NP-hard problem. At the end, the proposed approach is utilized in a real case study and with applying sensitivity analysis the validation and performance of the proposed model is assessed. The results of analysis demonstrate the capability of the newly introduced model.