Abstract
As consumer demand for fresh products continues to rise, the inefficiencies in cold chain logistics have emerged as a pressing issue, resulting in substantial food waste and compromised product quality. Meanwhile, logistics companies face the dual challenge of reducing costs and carbon emissions while ensuring product freshness. In response to these challenges, this paper proposes a novel target-oriented framework that leverages an underperformance riskiness index to optimize cold chain routing decisions. The primary objective is to minimize the risk of not meeting the target freshness level while accounting for costs and carbon emissions. To address the complexity that arises from stochastic arrival times, a linear decision rule is incorporated into the model. The robust counterpart of the problem is reformulated as a mixed-integer linear programming model, which is then solved efficiently using a Benders decomposition approach. Extensive computational experiments are conducted on realistic instances to evaluate the performance of our proposed approach. A comparative analysis with two benchmark models is also performed. The experimental results reveal that our target-oriented robust optimization framework generates high-quality solutions. It effectively reduces both the likelihood and magnitude of violations of the target freshness level, while maintaining relatively low costs and carbon emissions.
NETL
NSTL
Elsevier