O problema da roteirização periódica de veículos
DOI:
https://doi.org/10.14295/transportes.v16i1.8Abstract
Este artigo trata do problema de roteirização periódica, uma generalização do problema clássico de roteirização de veículos. Neste problema, os pontos a serem atendidos podem ter diferentes freqüências de visitas e a roteirização deve considerar um período de planejamento. Portanto, é preciso decidir quais são os melhores dias de visita para cada ponto (respeitando sua freqüência de visitas), para que a roteirização seja otimizada para todo o horizonte de planejamento. Este problema ocorre no contexto da logística inbound na indústria automobilística. Neste caso, não só os roteiros de coleta devem ser determinados como também os dias em que cada fornece- dor pode ser visitado, dado que os diferentes fornecedores podem ter diferentes freqüências. Com a finalidade de explorar novas abor- dagens para este tipo de problema de maneira que houvesse um aumento na qualidade de soluções e diminuição do tempo de proces- samento computacional, são propostas três estratégias heurísticas de solução, baseadas em inserção, GRASP e algoritmos genéticos. As estratégias foram implementadas e testadas para problemas encontrados na literatura, bem como problemas gerados aleatoriamente.
Abstract: This article deals with the period vehicle routing problem, which can be viewed as a generalization of the classical vehicle routing problem. In this problem, points to be serviced may have different frequencies and the routing is accomplished over a given planning horizon. Thus, one must decide which are the best days to visit each point (respecting its given frequency), so that the routing over the overall planning horizon is optimized. This problem arises in the context of inbound logistics in the automotive industry. In this case, pick-up routes should be determined as well as the days each supplier must be visited, given that the different suppliers may have different frequencies. To explore new approaches to this problem, aiming to improved solutions and reduced CPU time, three new heuristics are proposed, based on GRASP and genetic algorithm. The heuristics were implemented and tested against problems found in the literature, as well as in randomly generated problems.
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