Robust optimization for relief logistics planning under uncertainties in demand and transportation time

Abstract

• An emergency logistics model for relief logistics planning in post-disaster stage is proposed. • A robust optimization approach is adopted to cope with uncertainties in demand and transportation time. • A numerical example is utilized to investigate the application of the proposed model. • Sensitivity analyses explore the trade-off between optimization and robustness. • The robustness of the solutions generated by the robust model is assessed by comparing with the deterministic model. Emergency logistics is an essential component of post-disaster relief campaigns. However, there are always various uncertainties when making decisions related to planning and implementing post-disaster relief logistics. Considering the particular environmental conditions during post-disaster relief after a catastrophic earthquake in a mountainous area, this paper proposes a stochastic model for post-disaster relief logistics to guide the tactical design for mobilizing relief supply levels, planning initial helicopter deployments, and creating transportation plans within the disaster region, given the uncertainties in demand and transportation time. We then introduce a robust optimization approach to cope with these uncertainties and deduce the robust counterpart of the proposed stochastic model. A numerical example based on disaster logistics during the Great Sichuan Earthquake demonstrates that the model can help post-disaster managers to determine the initial deployments of emergency resources. Sensitivity analyses explore the trade-off between optimization and robustness by varying the robust optimization parameter values.