Journal
JOURNAL OF CLEANER PRODUCTION
Volume 143, Issue -, Pages 882-893Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.12.033
Keywords
Economic impact; Environmental impact; Maintenance; Modeling; Scheduling
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This paper presents a mathematical model to determine the optimal schedule of preventive replacement of a component such that the economic and environmental impacts of the component are minimized. For the economic dimension, the model minimizes the operation, failure and replacement costs of the component. From the environmental perspective, the model aims to minimize the environmental impact associated with the use phase and action taken to replace the component. The model is made general and can accommodate any environmental impact category. Due to the complexity of the objective functions of the model, genetic algorithm (GA) is proposed to find the optimal solutions. To reduce GA search space, upper and lower bounds of the solutions are determined based on the numerical analysis of the first derivatives of the objective functions of the model. To show the applicability of the model, a case study aiming to minimize total expected cost and global warming potential (GWP) of a bus tire is presented. The results of the case study show that the optimal preventive replacement schedule minimizing total expected cost per km is when the tire reaches 17,700 km and the schedule minimizing total expected GWP per km is when the tire reaches 19,500 km. The schedules result in US$23 and 0.2 kg CO2-eg savings in the total expected cost and GWP per tire. The solutions of the multi-objective optimization problem indicate that a 1000 km increase in the optimal schedule minimizing total cost will result in a 0.4% increase in the total expected cost and a 0.002% reduction in the total expected GWP of the tire. The sensitivity analysis presents that 1% reduction in the operation cost and fuel consumption contributes to a 0.91% reduction in the total expected cost and 0.99% reduction in the total expected GWP, respectively. (C) 2016 Elsevier Ltd. All rights reserved.
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