A Hybrid Meta-Heuristic Approach for Emergency Logistics Distribution Under Uncertain Demand

A Hybrid Meta-Heuristic Approach for Emergency Logistics Distribution Under Uncertain Demand

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This paper addresses the critical challenge of Hoover HBD 495D2E Integrated Washer Dryer 9/5kg - White 9kg washing capacity 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 Single Inlet 4 Way Water Valve 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.