2002
DOI: 10.1057/palgrave.jors.2601352
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Partially dynamic vehicle routing—models and algorithms

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Cited by 151 publications
(107 citation statements)
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References 11 publications
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“…Larsen et al [88,90] use the effective degree of dynamism to define a framework classifying D-VRPs among weakly, moderately, and strongly dynamic problems, with values of δ e being respectively lower than 0.3, comprised between 0.3 and 0.8, and higher than 0.8.…”
Section: Measuring Dynamismmentioning
confidence: 99%
“…Larsen et al [88,90] use the effective degree of dynamism to define a framework classifying D-VRPs among weakly, moderately, and strongly dynamic problems, with values of δ e being respectively lower than 0.3, comprised between 0.3 and 0.8, and higher than 0.8.…”
Section: Measuring Dynamismmentioning
confidence: 99%
“…There has been some work on dynamic versions of the VRPs (DVRP) [25] with and without the consideration of time windows but not specifically on the dynamic MDVRP. Algorithms can be broadly divided into simple policies or rules, insertion procedures, and metaheuristics.…”
Section: Dynamic Vrpsmentioning
confidence: 99%
“…Algorithms can be broadly divided into simple policies or rules, insertion procedures, and metaheuristics. [25] evaluate simple policies such as First Come First Serve or Nearest Neighbor policies based on [2] in a dynamic or partially dynamic setting. Insertion procedures insert new tasks in the best position of the current routes on the basis of a rolling horizon [36] or double horizon [31].…”
Section: Dynamic Vrpsmentioning
confidence: 99%
“…More recent developments in this area include the following: Yang et al (2004) and Chen and Xu (2006) have developed mathematical-programming-based algorithms for different variants of the dynamic vehicle routing model. Larsen et al (2002), Attanasio et al (2004), Liao (2004), Van Hemert and La Poutre (2004), Du et al (2005), Montemanni et al (2005), Tang and Hu (2005), Fabri and Recht (2006), and Potvin et al (2006) have developed various rule-based and local search techniques, such as insertion methods, tabu search, ant colony optimization, intra-route improvement, inter-route improvement, etc., for dynamic vehicle routing. Godfrey and Powell (2002a,b), Hu et al (2003), Bent and Van Hentenryck (2004), Larsen et al (2004), and Thomas and White (2004) have used "look-ahead" approaches to tackling the dynamism of real-time routing problems.…”
mentioning
confidence: 99%