Multi-period facility location and capacity planning under $\infty$-Wasserstein joint chance constraints in humanitarian logistics
Abstract
The key of the post-disaster humanitarian logistics (PD-HL) is to build a good facility location and capacity planning (FLCP) model for delivering relief supplies to affected areas in time. To fully exploit the historical PD data, this paper adopts the data-driven distributionally robust (DR) approach and proposes a novel multi-period FLCP model under the -Wasserstein joint chance constraints (MFLCP-W). Specifically, we sequentially decide locations from a candidate set to build facilities with supply capacities, which are expanded if more economical, and use a finite number of historical demand samples in chance constraints to ensure a high probability of on-time delivery. To solve the MFLCP-W model, we equivalently reformulate it as a mixed integer second-order cone program and then solve it by designing an effective outer approximation algorithm with two tailored valid cuts. Finally, a case study under hurricane threats shows that MFLCP-W outperforms its counterparts in the terms of the cost and service quality, and that our algorithm converges significantly faster than the commercial solver CPLEX 12.8 with a better optimality gap.
Keywords
Cite
@article{arxiv.2111.15057,
title = {Multi-period facility location and capacity planning under $\infty$-Wasserstein joint chance constraints in humanitarian logistics},
author = {Zhuolin Wang and Keyou You and Zhengli Wang and Kanglin Liu},
journal= {arXiv preprint arXiv:2111.15057},
year = {2021}
}