Cold Chain Routing for Product Freshness and Low Carbon Emissions: A Target-Oriented Robust Optimization Approach

Abstract

While customers’ demands for fresh products continue to rise, inefficient logistics in cold chains has led to significant food waste and compromised product quality. In addition, logistics companies face the challenge of reducing costs and carbon emissions while ensuring product freshness. Our paper proposes a target-oriented framework utilizing an underperformance riskiness index to optimize cold chain routing decisions. The objective of the problem is to minimize the risk of failing to meet the target freshness level while accounting for cost and carbon emission limits. To address the complexity of stochastic arrival times, a linear decision rule is adopted. The problem’s robust counterpart is reformulated as a mixed-integer linear programming model. A Benders decomposition approach is developed to solve the problem efficiently. Computational experiments
are conducted on realistic instances to assess the performance of our approach, with a comparative analysis with two benchmark models. The experimental results demonstrate that our target-oriented robust optimization framework produces high-quality solutions, effectively reducing the chance and magnitude of violating the target freshness level while maintaining relatively low costs and carbon emissions.

Keywords: Cold chain routing, Freshness, Underperformance riskiness index, distributionally robust optimization, Benders decomposition