Unpredictability and the uncertain nature of disasters are the key challenges of designing HRCs. In addition, the need to find a balance between main performance criteria such as response time, demand satisfaction level and cost efficiency complicates further the design of HRCs. In this paper, a novel SBPSP model is proposed for humanitarian logistics network design problem, which is capable of coping with the uncertainty and multiple objectives of the decision problem, simultaneously. The uncertainties are treated by taking into account inherent fuzziness and randomness in the available data. The model deals with preparedness and response planning and takes into account distribution planning of relief items during stock prepositioning.
The proposed SBPSP model is converted into an equivalent crisp model by using a mixed possibilistic-stochastic approach. A tailored interactive DE algorithm is proposed in which, the decision maker is able to input her/his preferences like confidence level to find alternative solutions. The proposed model is applied to the design of relief network in Tehran and the outcome is compared with the existing pre-planned network. The results indicate robustness of the SBPSP model and superiority of the resulting solution compared to the existing network.
The current research can be extended in a number of directions. A hierarchical planning framework could be considered in HRC design problem to decrease computational complexity by solving the stock pre-positioning and relief distribution sub-problems sequentially. In addition, the proposed model could be extended, for instance by considering capacity constraints on transportation routes, determining suitable inventory control policies for perishable relief items and allowing possible lateral transshipments between various LDCs to decrease the unmet demands. Obtaining optimal solutions even for medium-sized problem instances needs huge computational storage capacities and times while it is almost impossible in large-sized problems. Therefore, developing other meta-heuristic methods to improve time-efficiency of the proposed solution approach could be considered as a suitable avenue for further research.