Incorporating Customer Demand Heterogeneity for Adaptive Cold Chain Routing Optimization: An Improved Ant Colony Algorithm

Chongjun Yuan; Shuwen Jia

doi:10.62051/ijgem.v10n6.09

Authors

Chongjun Yuan School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China
Shuwen Jia School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China

DOI:

https://doi.org/10.62051/ijgem.v10n6.09

Keywords:

Cold Chain Logistics, Vehicle Routing Problem, Customer Heterogeneity, Time-driven, Improved Ant Colony Optimization

Abstract

To mitigate the structural trade-offs among heterogeneous timeliness constraints in same-city fresh food distribution, an adaptive vehicle routing optimization model considering customer heterogeneity is formulated to minimize the total operating cost. To efficiently solve this complex problem, an improved ant colony algorithm featuring adaptive pheromone updates and elite ant reinforcement is developed to escape local optima traps. Numerical simulations demonstrate that the proposed algorithm converges rapidly and robustly. Driven by order-scale dynamic penalty weights, the framework successfully guides the fleet to prioritize strategic large supermarkets, substantially compressing time-window penalties with minimal marginal increases in transport expenses. This decision-making framework ultimately enables cold chain enterprises to achieve an optimal balance between transport economics and commercial credit.

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References

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