Multi-objective multi-compartment vehicle routing problem of fresh products with the promised latest delivery time

In light of the growing consumer emphasis on delivery speed and corporate environmental responsibility, it becomes paramount to simultaneously address the alignment of customer expectations with corporate objectives. We undertake a comprehensive examination of delivery delays and carbon emissions stemming from e-commerce logistics, leading us to formulate a delivery delay penalty function informed by customer behavior traits such as loss aversion. Concurrently, we analyze various factors influencing customer satisfaction and integrate them into our model. Similarly, we incorporate multiple determinants impacting vehicle emissions, devising a logistics cost-minimization model encompassing carbon emissions and cooling expenses. By amalgamating considerations of customer satisfaction, logistics expenses, and environmental concerns, we devise a dual-objective optimization model. To tackle this complex challenge, we introduce a multi-objective Artificial Bee Colony algorithm based on MOEA/D principles, substantiating its efficacy through extensive numerical experiments. Our findings demonstrate the algorithm's ability to intelligently optimize logistics routes, thus reducing vehicle utilization. Finally, we present a Pareto front, illustrating how mitigating customer satisfaction can alleviate logistics and carbon emission costs.