Location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling

Abstract: Distribution and optimum allocation of emergency resources are the most important tasks, which need to be accomplished during crisis. When a natural disaster such as earthquake, flood, etc. takes place, it is necessary to deliver rescue efforts as quickly as possible. Therefore, it is important to find optimum location and distribution of emergency relief resources. When a natural disaster occurs, it is not possible to reach some damaged areas. In this paper, location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling is investigated. In this study, there is no need to visit all the places and some demand points receive their needs from the nearest possible location. The proposed study is implemented for some randomly generated numbers in different sizes. The preliminary results indicate that the proposed method was capable of reaching desirable solutions in reasonable amount of time.

How to cite this paper

 Goli, A & Alinaghian, M. (2015). Location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling.Decision Science Letters , 4(4), 579-592.

Refrences

Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location–transportation problem for disaster response. Computers & Operations Research, 41, 83-93.

Afshar, A., & Haghani, A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences, 46(4), 327-338.

Barbaroso?lu, G., ?zdamar, L., & Cevik, A. (2002). An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Research, 140(1), 118-133.

Cao, E., & Lai, M. (2010). The open vehicle routing problem with fuzzy demands. Expert Systems with Applications, 37(3), 2405-2411.

Croes, G. A. (1958). A method for solving traveling-salesman problems. Operations Research, 6(6), 791-812.

Doerner, K., Focke, A., & Gutjahr, W. J. (2007). Multicriteria tour planning for mobile healthcare facilities in a developing country. European Journal of Operational Research, 179(3), 1078-1096.

Feo, T. A., & Resende, M. G. (1989). A probabilistic heuristic for a computationally difficult set covering problem. Operations research letters, 8(2), 67-71.

Fleszar, K., Osman, I. H., & Hindi, K. S. (2009). A variable neighbourhood search algorithm for the open vehicle routing problem. European Journal of Operational Research, 195(3), 803-809.

Ghaffari-Nasab, N., Ahari, S. G., & Ghazanfari, M. (2013). A hybrid simulated annealing based heuristic for solving the location-routing problem with fuzzy demands. Scientia Iranica, 20(3), 919-930.

Kaufmann, A. (1975). Introduction to the theory of fuzzy subsets (Vol. 1, No. 8). New York: Academic Press.

Knott, R. (1987). The logistics of bulk relief supplies. Disasters, 11(2), 113-115.

Liu, B. (2004). Uncertainty Theory. An Introduction to Its Axiomatic Foundation, vol. 154 of. Studies in Fuzziness and Soft Computing.

Luis, E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-economic planning sciences, 46(1), 88-97.

Mladenovi?, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097-1100.