عنوان مقاله [English]
Natural disasters are highly likely to lead to severe problems, including extensive human misery and physical loss or damage. In order to primarily reduce loss of human life, it is vital to respond quickly to natural disasters. One stage of disaster relief operations is in dealing with disaster response, an aspect of which is logistics. Both the distribution of disaster relief to the affected areas and the evacuation process of injured victims to temporary
medical facilities are major activities in disaster relief logistics in the disaster response phase. The predictive analysis of natural disasters and their consequences is challenging because of uncertainties and incomplete data. The
significance of accounting for uncertainty in the context of disaster relief logistics stimulates an interest in developing appropriate decision making tools to cope with uncertain and imprecise parameters in relief logistics
This paper proposes a multi-objective, multi-mode, fuzzy mathematical programming model under the inherent uncertainty of input data in such a problem. The proposed model integrates strategic planning, such as the location
of relief distribution centers, with tactical support decisions, i.e., the quantity of flow between facilities to avoid separate decision-making processes between strategic and tactical levels. Furthermore, the model considers the
determination of the location of temporary medical facilities after natural disaster occurrences. In our approach, not only demands, but also supplies and the cost of transportation, are considered as the fuzzy parameters. According to recent studies, the performance of relief operations is measured based on total cost and demand satisfaction levels. Therefore, our multi-objective model contains: (i) minimization of the sumof the setup cost, transportation costs, vehicle assignment costs and shortage costs; (ii) maximization of serving injured people. To solve the proposed fuzzy multi-objective optimization model, an interactive fuzzy solution approach, based on the epsilon-constraint method,
is proposed, because of its capability of measuring and adjusting the satisfaction levels of each objective function explicitly. A case study is used to demonstrate the significance and applicability of the developed fuzzy optimization model, as well as the usefulness of the proposed solution approach.