A cartographic approach to the dynamic vehicle routing problem with time windows and stochastic customers

• Autor(a) principal: Coral, Daniel Bustos
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-29102018-160027/
• topic: Abordagem cartográfica; Algoritmo memético; Cartographic approach; DVRPTWSC; DVRPTWSC; Memetic algorithm; Multi-agent system; Sistema multi-agente; VRPTW; VRPTW

This dissertation presents a cartographic approach to the dynamic vehicle routing problem with time windows and stochastic customers (DVRPTWSC). The objectives are to minimize the total travel time and maximize the number of new requests served. Addressing the DVRPTWSC requires solving the vehicle routing problem with time windows (VRPTW). A memetic algorithm (MA) for the VRPTW is proposed. The MA prunes the search space using the information gathered by a clustering procedure, which is applied to customers spatial data. The cartographic approach to the DVRPTWSC is incorporated into a multiagent system where a dispatcher agent plans the routes for vehicle agents. Before creating the initial routing plan, a cartographic processing is applied. This procedure uses hierarchical clustering to divide the region where customers are located into a hierarchy of nested regions. The initial routing plan considers known requests and potential requests sampled from known probability distributions. It is created using the search operators of the MA, which in turn use the information obtained from the hierarchical clustering to perform the search. Over the planning horizon, the dispatcher updates the routing plan: Potential requests that were included in the initial routing plan and do not materialize are removed and new requests are processed using the assignation of requests based on nested regions (ARNR). The ARNR procedure is aimed at reducing the number of vehicles considered for serving new requests. It tries to assign the requests among the vehicles that can serve them at low detour costs. The nested regions created in the cartographic processing are used to identify such vehicles. Experimental results show that the proposed MA performs competitively with state-of-the-art heuristics for the VRPTW. The proposed approach to the DVRPTWSC outperforms approaches that do not include potential requests in the initial routing plan. The use of the ARNR procedure significantly reduces the number of vehicles considered for serving new requests, and it yields solutions similar to those obtained when considering all vehicles in operation. The proposed approach performs consistently under three levels of dynamism: low, medium, and high.