Multi-Objective Low Carbon Vehicle Routing for Cold Chain Distribution with Customer Time Loss Aversion

The elevated refrigeration expenses linked to cold chain distribution contribute to increased overall logistics costs and carbon emissions.Concurrently,the sensitivity of consumers to delivery delays also impacts the design of cold chain distribution operations.This paper considers the logistics costs of cold chain,consumer time loss aversion,and the efficiency of low-carbon distribution to construct a multi-objective cold chain vehicle routing problem.It combines a decomposition-based multi-objective solution algorithm and fruit fly optimization algorithm to solve the proposed model,and validates the algorithm and model through a large number of numerical experiments.Firstly,our computations of the C-metric,IGD value,Hypervolumn,and CPU time demonstrate that the algorithm employed in this study has yielded notable advantages in terms of convergence and the overall performance of the non-dominated solutions.Secondly,we find that increasing logistics satisfaction requires a significant investment in logistics costs,thus requiring a delicate balance between logistics expenditure and service advantages.Finally,we used a typical example to analyze the size of different cost modules in cold chain distribution and find that vehicles can optimize their routes without needing to make extensive diversions to reach distant customers,ultimately leading to reduced fuel consumption and carbon emissions.Besides,the traditional assumption that a higher utilization of logistics vehicles results in increased carbon emissions and fuel usage is not universally valid.Our research contributes to the current balance between cold chain costs and consumer satisfaction in cold chain distribution.Additionally,leveraging multi-objective algorithm design,we provide feasible solutions for current cold chain delivery operations.Further,by incorporating consumer time loss aversion model,we aid in understanding the impact of consumer behavior on the design of cold chain delivery solutions.