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03608nam a22005295i 4500 |
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978-3-540-85152-3 |
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20151204142022.0 |
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|a 9783540851523
|9 978-3-540-85152-3
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|a 10.1007/978-3-540-85152-3
|2 doi
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|a Bio-inspired Algorithms for the Vehicle Routing Problem
|h [electronic resource] /
|c edited by Francisco Babtista Pereira, Jorge Tavares.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2009.
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| 300 |
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|a XVI, 216 p. 57 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 161
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|a 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.
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|a 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 demands. Biological inspired computation is a field devoted to the development of computational tools modeled after principles that exist in natural systems. The adoption of such design principles enables the production of problem solving techniques with enhanced robustness and flexibility, able to tackle complex optimization situations. The goal of the volume is to present a collection of state-of-the-art contributions describing recent developments concerning the application of bio-inspired algorithms to the VRP. Over the 9 chapters, different algorithmic approaches are considered and a diverse set of problem variants are addressed. Some contributions focus on standard benchmarks widely adopted by the research community, while others address real-world situations.
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|a Engineering.
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|a Operations research.
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|a Decision making.
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|a Software engineering.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Engineering.
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|a Appl.Mathematics/Computational Methods of Engineering.
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|a Software Engineering.
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|a Operation Research/Decision Theory.
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| 700 |
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|a Pereira, Francisco Babtista.
|e editor.
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|a Tavares, Jorge.
|e editor.
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783540851516
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 161
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|u http://dx.doi.org/10.1007/978-3-540-85152-3
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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