Globalized Robust Optimization for Cold Chain Logistics: Integrated Routing and Storage Decisions

Abstract

Cold-chain vehicle routing is highly vulnerable to demand uncertainty, which may lead to infeasible and unreliable delivery schedules. This paper proposes a globalized robust optimization (GRO) model for the cold-chain vehicle routing problem. Unlike traditional robust optimization, which tends to be overly conservative and costly by prioritizing worst-case scenario, the GRO method introduces adjustable parameters to establish a continuous trade-off decision space between robustness and economic efficiency, better aligning with the operational flexibility requirements for practical logistics management. The uncertain capacity constraints are reformulated based on global robustness theory, resulting in a tractable mixed-integer linear programming model with an adjustable robustness control parameter. Numerical experiments conducted on three urban logistics networks demonstrate that the globalized robust optimization model achieves balanced vehicle utilization and robust routing solutions under multidimensional demand fluctuations, confirming the effectiveness and practical value of the proposed method for cold-chain logistics planning under severe demand uncertainty.

Keywords

Perishable products / globalized robust optimization / mixed-integer programming

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Systems Engineering Society of China and Springer-Verlag GmbH Germany