Optimized vehicle exploitation period decision in cold-chain logistics companies

Abstract

Cold-chain logistics gained great attention during COVID-19 pandemic due to the demand for temperature-controlled home delivery and vaccine distribution. Refrigerated trucks used for cold-chain logistics need additional equipment such as insulated compartments and refrigeration units, resulting in high life cycle costs. As cold-chain logistics companies require an evaluation model when making vehicle replacement decisions, this study proposes a four-phase reference model including (1) Data collection and compilation, (2) Life cycle maintenance and repairment (M&R) cost, (3) Residual value prediction model, and (4) Optimal replacement model. The reference model applies methods including expert interviews, artificial neural networks, and dynamic programming. We present a case study of an international cold-chain logistics company determining the optimal replacement year for refrigerated trucks. Based on scenario analysis, findings show the optimal vehicle replacement year varies depending on operational environments and service requirements. The cold-chain logistics company can use these findings to establish its preventive M&R policy for refrigerated trucks, as well as identify the optimal year for replacement. The proposed reference model can serve as a guideline for cold-chain logistics companies' fleet management.