| 总结: | This paper addresses the critical challenge of effective resource allocation in emergency logistics distribution, particularly during disaster scenarios where demand is uncertain and traditional deterministic optimization methods are inadequate. We propose a novel multi-objective chance-constrained model that minimizes transportation deviation, creating a computationally feasible deterministic equivalent model using uncertainty theory. Furthermore, this paper introduces a hybrid of the Estimation of Distribution Algorithm and the Multi-Objective Snow Goose Algorithm (EDA-MOSGA), designed to efficiently navigate the solution space and identify near-optimal solutions. The EDA-MOSGA enhances Pareto front diversity with its adaptive population adjustment and specialized operator model, marking a significant advancement over existing methods. Validated through the Zitzler-Deb-Thiele (ZDT) and Deb-Thiele-Laumanns-Zitzler (DTLZ) multi-objective test suites, and a case study during the Coronavirus Disease 2020 (COVID) pandemic in Chengdu, the EDA-MOSGA demonstrates exceptional performance in real-world applications. The results of this study lay the foundation for the future integration of artificial intelligence technology to improve the scalability of logistics distribution in different emergency situations.
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