Development of an optimized maintenance scheduling for emergency rescue railway wagons using a genetic algorithm: a case study of Iran railways company

Abstract

PurposeThis paper aims to optimize the maintenance scheduling of emergency rescue wagons in railway companies using a genetic algorithm (GA). It offers an integrated model for simultaneously solving maintenance planning, preventive maintenance, prognostic information and resource planning from which optimal levels of the system performance in terms of cost and repair time could be determined.Design/methodology/approachThis study initially evaluates the previous types of research in presenting the optimal model of the rescue train wagon for maintenance and repair planning and lists the identified criteria based on experts' opinions using fuzzy analytic hierarchy process (FAHP) and stepwise weight assessment ratio analysis (SWARA) techniques. Then, the final weight of the desired criteria is calculated. Later, the final decision matrix is evaluated by the experts. The final normal decision matrix is formed to select the optimal maintenance and repair planning plan based on the GA. Finally, two strategies including joint optimization strategy of preventive maintenance planning are compared with the independent preventive maintenance planning strategy.FindingsAccording to the primary results, three primary parameters, including technology, human damage caused by negligence and the average failure rate, should be considered for launching the GA model. Based on the results of the second part; by comparing the preventive maintenance planning strategy independently, the joint optimization strategy reduces the total cost of production up to 8.25%. Comparison results show that the total cost of joint optimization production is less than independent preventive maintenance and repair planning. Moreover, the value of total process time in joint optimization strategy was reduced by 1.2% compared to independent preventive maintenance (PM) planning (from 137.90 to 136.20 h).Originality/valueThe novelty of this paper lies on the application of the GAs to develop an optimized PM scheduling to localize the maintenance planning for maximizing productivity, avoid train accidents, reduce costs and increase efficiency and capacity.