Optimising hierarchical emergency logistics network under ambiguous demand and transportation cost

This article studies the response problem of an emergency logistics network with a decision hierarchy relationship under uncertainty. To account for the partial distribution information about uncertain demand and transportation costs, we construct a moment-based ambiguity set based on limited historical data, where the pivot variable method is employed to determine the confidence interval of the mean value. Based on the constructed ambiguity set, we develop a novel distributionally robust bi-level post-disaster emergency logistics location-routeing model. By exploiting the structural characteristic, chance-constrained models under box-ellipsoid and budget perturbation sets are reformulated as bi-level mixed-integer conic programming models. To accelerate the solution procedure, the bi-level models are further converted into single-level ones via Karush-Kuhn-Tucker condition, which can be directly solved to optimality using CPLEX software. Supply risk value for each supplier is obtained by applying analytic hierarchy process. We conduct extensive experiments using the Iranian flood as the case study to address the computational performance of our proposed optimisation method.