A simulation based optimization approach for spare parts forecasting and selective maintenance. (December 2017)

• Record Type: Journal Article
• Authors: Sharma, Pankaj; Kulkarni, Makarand S; Yadav, Vikas
• Is Part Of: Reliability engineering & system safety. Volume 168(2017)
• Page Start: 274
• Page End: 289
• Publication Date: 2017-12
• Subjects: Mission reliability -- Simulation -- Genetic algorithm -- Army -- Failure simulation -- Spare parts forecasting; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2017.05.013
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: Equipment of the Army encounters various modes of exploitation depending on the scenario in which it is used. Typically, the missions are followed by intervals which can be used for maintenance. This is a suitable condition for employment of selective maintenance strategy. However, this maintenance interval is bound by the constraints of time, resources and desired reliability before the start of the next mission. This calls for optimization of maintenance activities that can be fitted into the maintenance break. There is also a requirement of having a forecasting technique for reducing the supply lead times. This paper lays out a methodology to use simulation for predicting failures in the army equipment. A Genetic Algorithm (GA) based approach is then used for optimizing the maintenance activities before the start of the maintenance break. The process of Simulation plus GA Optimization is automated using a program in MATLAB. The novelty of the work lies in modifying the process of Simulation and GA Optimization to suit the exact modus operandi employed by the Army in deploying equipment for peace, training exercise and war (mission with or without some maintenance break) separately. In addition to optimizing the maintenance activities, the methodology also helps in forecasting the requirement of spare parts both before and during the mission.