Buch, Englisch, 216 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 359 g
Buch, Englisch, 216 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 359 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-642-09887-1
Verlag: Springer
The vehicle routing problem (VRP) is one of the most famous combinatorial optimization problems. In simple terms, the goal is to determine a set of routes with overall minimum cost that can satisfy several geographical scattered - mands. A ?eet of vehicles located in one or more depots is available to ful?ll the requests. A large number of variants exist, adding di?erent constraints to the original de?nition. Some examples are related to the number of depots, the ordering for visiting the customers or to time windows specifying a desirable period to arrive to a given location. The original version of this problem was proposed by Dantzig and Ramser in 1959 [1]. In their seminal paper, the authors address the calculation of a set of optimal routes for a ?eet of gasoline delivery trucks. Since then, the VRP has attractedtheattentionofalargenumberofresearchers.Aconsiderablepartofits success is a consequence of its practical interest, as it resembles many real-world problems faced everyday by distribution and transportation companies, just to mention a few applications areas. In this context, the development of e?cient optimization techniques is crucial. They are able to provide new and enhanced solutionstologisticoperations,andmaythereforeleadtoasubstantialreduction in costs for companies. Additionally, and from a research oriented perspective, the VRP is a challenging NP-hard problem providing excellent benchmarks to access the e?ciency of new global optimization algorithms.
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Weitere Infos & Material
A Review of Bio-inspired Algorithms for Vehicle Routing.- A GRASP × Evolutionary Local Search Hybrid for the Vehicle Routing Problem.- An Evolutionary Algorithm for the Open Vehicle Routing Problem with Time Windows.- Using Genetic Algorithms for Multi-depot Vehicle Routing.- Hybridizing Problem-Specific Operators with Meta-heuristics for Solving the Multi-objective Vehicle Routing Problem with Stochastic Demand.- Exploiting Fruitful Regions in Dynamic Routing Using Evolutionary Computation.- EVITA: An Integral Evolutionary Methodology for the Inventory and Transportation Problem.- A Memetic Algorithm for a Pick-Up and Delivery Problem by Helicopter.- When the Rubber Meets the Road: Bio-inspired Field Service Scheduling in the Real World.
