Robust optimal disaster relief logistics planning using a bi-objective robust scenario-based stochastic programming model and augmented epsilon constraint method

Disaster relief logistics planning decisions can be divided into two categories: strategic decisions before the crisis and operational decisions during and after the crisis. This paper studies the strategic issues and some operational issues related to disaster relief logistics, including locating relief depots and determining the capacity/size, storage rates for relief supplies in warehouses, locating relief distribution centers after occurrence of crises, the allocation of warehouses to them, and finally, how to cover the damaged areas which demand the relief goods and simultaneously modeling and solve problems. Modeling and problem solving is done using the bi-objective robust scenario-based stochastic programming approach in which both the humanitarian objective of minimizing the maximum shortage and the economic goal of minimizing the average cost of relief is considered under possible scenarios. Thus, the best balance between economic and humanitarian objectives is achieved using the perfect epsilon-constraint method. The GAMS software has been used to implement the proposed model. Findings: The numerical results of the proposed model of this study indicates that the approach can be used to solve the problem of disaster Relief logistic planning, since, it shows acceptable performance in terms of criteria such as average costs and deficiencies, and also the maximum deficit under various scenarios. The proposed bi-objective robust model controls the uncertainty of parameters such as relief goods’ demand more efficiently than other approaches. Using proposed decision models, crisis managers in a country or provinces can make optimal strategic decisions regarding location and inventory of relief warehouses, distribution of relief goods and etc… before the disaster. . As a result, it will save not only the cost of relief, but also accelerates the relief efforts and minimizes the lack of casualties