A hybrid Genetic Algorithm and LP-Metric method for a multi-objective optimization model for relief logistics problem

The logistics planning includes transporting injured people from affected areas to medical centers and dispatching relief commodities to the affected areas and medical centers are the most important activities in an emergency situation. An efficient relief logistics planning is needed to reduce loss of human life in a disaster. In this study, a multi-objective, multi-commodity mathematical model is developed to manage the relief commodities and injured people logistics. During the response phase, it is necessary to set up temporary medical centers as soon as possible to speed up medical care for injured people. Hence, the proposed model considers the locating of the temporary medical centers in appropriate sites as well. This model encompasses two objective functions, including minimization of total cost and minimization of sum of unserved injured people. The total cost consists of the procurement cost of the relief commodities, transportation cost, set up and shortage cost. LP-Metric method and a hybrid LP-Metric Genetic Algorithm are applied to solve the multi-objective model. Finally, the performance of the proposed model and solution algorithm are tested in Tehran, Iran.