Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

• Published in: Socio-economic planning sciences 2020-09, Vol.71, p.100745, Article 100745
• Main Authors: Ghasemi, Peiman, Khalili-Damghani, Kaveh, Hafezalkotob, Ashkan, Raissi, Sadigh
• Format: Article
• Language: English
• Subjects: Algorithms; Case studies; Chance constraint programming; Commodities; Disaster logistic planning; Earthquakes; Emergency preparedness; Evacuation; Evacuation planning; Injured people; Integer programming; Logistics; Mathematical programming; Optimization; Simulation; Stochastic optimization model; Uncertainty
• ISSN: 0038-0121; 1873-6041
• DOI: 10.1016/j.seps.2019.100745

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model. The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way. •Stochastic multi-objective optimization model is proposed for logistic distribution and evacuation planning.•Demands are assumed to be probabilistic and estimated using a simulation approach through several scenarios.•The stochastic optimization model is transformed to its deterministic equivalent using chance constraint programming.•The model is solved using an efficient epsilon-constraint approach and non-dominated sorting genetic algorithm (NSGA-II).•The proposed optimization model and solution approach are used to handle the case study of