Two-stage distributionally robust mixed-integer optimization model for three-level location–allocation problems under uncertain environment

The problem of the optimal three-level location–allocation of the transfer center, processing factory, and distribution center for supply chain networks under uncertain transportation cost and customer demand is studied. We establish a two-stage distributionally robust 0–1 mixed-integer optimization model by considering the uncertainty of the supply chain. Given the complexity of the model, this paper proposes a distribution–separation hybrid intelligent algorithm (DS-HIA) to solve the resulting model, yielding the optimal location and maximal expected return of supply chain in the worst-case distribution. A case study of the tea supply chain in Shanghai is then presented to investigate the specific influence of uncertainties on a three-level location, i.e., transfer center, tea factory, and distribution center. Moreover, we compare the DS-HIA with distribution–separation hybrid second-order particle swarm optimization algorithm, distribution–separation hybrid particle swarm optimization, distribution–separation hybrid genetic algorithm and distributionally robust L-shaped method to validate the proposed algorithm based on the computational time and the convergence rate.