A bi-objective robust optimization model for disaster response planning under uncertainties. (May 2021) - Sun, Huali; Wang, Yang; Xue, Yaofeng
Highlights: ·An emergency model of Location-Transportation-Allocation Problem is proposed. ·Robust method is used to deal with the uncertainties in demand and transport time. ·The casualty triage and the deterioration of injury with time is described. ·Minimizing the total cost and the sum of injury severity scores are achieved. ·Sensitivity analyses discuss the trade-off between optimization and robustness. Abstract: There are various uncertainties post-disaster relief logistics, it is essential to optimize emergency logistics to provide timely and effective medical service in such emergency situations. This paper proposes a bi-objective robust optimization model for strategic and operational response to decide the facility location, emergency resource allocation, and casualty transportation plans in a three-level rescue chain composed of casualty clusters, temporary facilities, and general hospitals. The Injury Severity Score (ISS) is adopted to divide the casualties into two categories and give the dynamic injury deterioration of casualties over time. Also, the model considers various uncertainties in demand including the number of casualties and the number of rescue supplies and transportation time. The objectives are to minimize the sum of ISS for all casualties and minimize the total costs of the system. Meanwhile, the penalty coefficients for untransported casualties are applied to maximize the transport number of casualties, and the penalty costs for unmet relief supplies are used to maximize the satisfaction of supplies demand at temporary facilities. We employ the robust optimization method to derive the robust corresponding model of the proposed stochastic model. The bi-objective model is solved with the ε-constraint method. Additionally, case studies based on Yushu Earthquake are utilized to demonstrate the feasibility and validity of the proposed model. Sensitivity analyses discuss the impact of uncertainties on the proposed model results by changing the settings of the uncertain parameters in the robust optimization, to make a tradeoff between optimization and robustness.