Application of simulation modeling to emergency population evacuation
Proceedings of the 29th conference on Winter simulation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Path selection in disaster response management based on Q-learning
International Journal of Automation and Computing
Evaluating emergency response capacity by fuzzy AHP and 2-tuple fuzzy linguistic approach
Expert Systems with Applications: An International Journal
In-field and inter-field path planning for agricultural transport units
Computers and Industrial Engineering
Computers and Industrial Engineering
Humanitarian/emergency logistics models: a state of the art overview
Proceedings of the 2013 Summer Computer Simulation Conference
Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling
Expert Systems with Applications: An International Journal
Computers and Operations Research
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Path selection is one of the fundamental problems in emergency logistics management. Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time of disaster. First a single-objective path selection model is presented taking into account that the travel speed on each arc will be affected by disaster extension. The objective of the model is to minimize total travel time along a path. The travel speed on each arc is modeled as a continuous decrease function with respect to time. A modified Dijkstra algorithm is designed to solve the model. Based on the first model, we further consider the chaos, panic and congestions in time of disaster. A multi-objective path selection model is presented to minimize the total travel time along a path and to minimize the path complexity. The complexity of the path is modeled as the total number of arcs included in the path. An ant colony optimization algorithm is proposed to solve the model. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.