Robust Optimization for Perishable Product Distribution under Uncertainty of Multimodal Transportation Services

Abstract

Unusual logistics service uncertainties resulting from recent mega-disruptions, such as COVID-19 outbreak or the Russia-Ukraine conflict, are placing increasing pressure on practitioners and researchers to develop robust solutions to mitigate these uncertainties. Perishable products, such as fresh produce, medicine, and vaccines, which deteriorate quickly and have short shelf-lives, are particularly vulnerable to the lead-time uncertainty from the global logistics services, which causes huge waste or even fatal outcomes. Moreover, modeling such time uncertainties using probability distributions is challenging due to the lack of historical data on unprecedented and complex disruptions. The literature on distribution and transportation service planning for perishable products under unknown probability distributions is also scarce. This paper aims to provide shippers with robust solutions for distribution planning of perishable products considering available transportation services within a multimodal transportation network under uncertainty of transit time including waiting time at sites and transportation time between sites. A robust model is proposed to minimize the total distribution costs composed of transportation, transshipment, and deterioration costs. Our model applies the budget of uncertainty to adjust the degree of conservatism in the solutions. A variable neighborhood search (VNS) algorithm is devised to solve the robust model. Finally, computational experiments are carried out to evaluate the performance and applicability of the proposed model and algorithm.