Abstract
A dynamic vehicle routing problem that models the relief distribution operations in a post-disaster environment is addressed. As an approximate solution method, a multi-agent system with two hierarchical levels is proposed. Within the proposed framework, the vehicles have the ability to dynamically re-route, bid for new tasks and de-commit to previously undertaken tasks to take advantage of the continuous flow of incoming information. In order to evaluate the proposed architecture, a discrete-event simulator was built in an object-oriented language. A series of simulation cases were identified and the behavior of the proposed approach was compared to that of a centralized, on-line heuristic solution approach.
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Adler JL, Satapathy G, Manikonda V, Bowles B, Blue VJ. A multi-agent approach to cooperative traffic management and route guidance. Transport Res B. 2005;39:297–318.
Arora H, Raghu TS, Vinze A. Resource allocation for demand surge mitigation during disaster response. Decis Support Syst. 2010;50:304–15.
Balcik B, Beamon BM, Krejci CC, Muramatsu KM, Ramirez M. Coordination in humanitarian relief chains: practices, challenges and opportunities. Int J Prod Econ. 2010;126:22–34.
Barbucha D. Search modes for the cooperative multi-agent system solving the vehicle routing problem. Neurocomputing. 2012;88:13–23.
Bayakasoglu A, Kaplanoglu V. A multi-agent approach to load consolidation in transportation. Adv Eng Softw. 2011;42:477–90.
Bohnlein D, Schweiger K, Tuma A. Multi-agent-based transport planning in the newspaper industry. Int J Prod Econ. 2011;131:146–57.
Branchini RM, Armentano VA, Lokketangen A. Adaptive granular local search heuristic for a dynamic vehicle routing problem. Comput Oper Res. 2009;36:2955–68.
Bürckert H-J, Fischer K, Vierke G. Holonic transport scheduling with teletruck. Appl Artif Intell. 2000;14(7):697–725.
Claes R, Holvoet T, Weyns D. A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Intell Transport Syst. 2011;12(2):364–73.
Dorer K, Calisti M. An adaptive solution to dynamic transport optimization. In: Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005). Utrecht: ACM Press; 2005. p. 45–51.
Fischer K, Muller JP, Pischel M, Schier D. A model for cooperative transportation scheduling. In: Proceedings of the First International Conference on Multiagent Systems. Menlo park: AAAI Press/MIT Press; 1995. p. 109–16.
Hu ZH. A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Syst Appl. 2011;38:2632–9.
Kohout R, Kutluhan E. In-time agent-based vehicle routing with a stochastic improvement heuristic. In: Proceedings of the 16th National Conference on Artificial Intelligence and the 11th on Innovative Applications of Artificial Intelligence (AAAI/IAAI 1999). Menlo Park: AAAI Press; 1999. p. 864–9.
Laporte G, Gendreau M, Potvin J-Y, Semet F. Classical and modern heuristics for the vehicle routing problem. Int Trans Oper Res. 2000;7(4–5):285–300.
Leong HW, Liu M. A multi-agent algorithm for vehicle routing problem with time window. In: Proceedings of the ACM Symposium on Applied Computing (SAC 2006). New York: ACM Press; 2006. p. 106–11.
Liao TY, Hu TY. An objected-oriented evaluation framework for dynamic vehicle routing problems under real-time information. Expert Syst Appl. 2011;38:12548–58.
Lorini S, Potvin JY, Zufferey N. Online vehicle routing and scheduling with dynamic travel times. Comput Oper Res. 2011;38:1086–90.
Mahr T, Srour J, de Weerdt M, Zuidwijk R. Can agents measure up? A comparative study of an agent-based and an on-line optimization approach for a drayage problem with uncertainty. Transport Res C. 2010;18:99–119.
Martinez AJP, Stapleton O, Wassenhove LNV. Field vehicle fleet management in humanitarian operations: a case-based approach. J Oper Manag. 2011;29:404–21.
Mes M, van der Heijden M, van Harten A. Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems. Eur J Oper Res. 2007;181(1):59–75.
Persson JA, Davidsson P, Johansson SJ, Wernstedt F. Combining agent-based approaches and classical optimization techniques. In: Proceedings of the European Workshop on Multi-Agent Systems (EUMAS 2005). 2005. p. 260–269.
Teo JSE, Taniguchi E, Qureshi AG. Evaluating city logistics measure in e-commerce with multi-agent systems. Procedia Soc Behav Sci. 2012;39:349–59.
Thompson P, Psaraftis H. Cyclic transfer algorithms for multivehicle routing and scheduling problems. Oper Res. 1993;41(5):935–46.
Yi W, Kumar A. Ant colony optimization for disaster relief operations. Transport Res E. 2007;43:660–72.
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Xanthopoulos, A., Koulouriotis, D. (2013). A Multi-agent Based Framework for Vehicle Routing in Relief Delivery Systems. In: Zeimpekis, V., Ichoua, S., Minis, I. (eds) Humanitarian and Relief Logistics. Operations Research/Computer Science Interfaces Series, vol 54. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7007-6_9
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DOI: https://doi.org/10.1007/978-1-4614-7007-6_9
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