Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management

Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management
Author :
Publisher : Springer
Total Pages : 280
Release :
ISBN-10 : 9783540693901
ISBN-13 : 3540693904
Rating : 4/5 (904 Downloads)

Book Synopsis Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management by : Andreas Fink

Download or read book Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management written by Andreas Fink and published by Springer. This book was released on 2008-09-08 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistics and supply chain management deal with managing the ?ow of goods or services within a company, from suppliers to customers, and along a supply chain where companies act as suppliers as well as customers. As transportation is at the heart of logistics, the design of tra?c and transportation networks combined with the routing of vehicles and goods on the networks are important and demanding planning tasks. The in?uence of transport, logistics, and s- ply chain management on the modern economy and society has been growing steadily over the last few decades. The worldwide division of labor, the conn- tion of distributed production centers, and the increased mobility of individuals lead to an increased demand for e?cient solutions to logistics and supply chain management problems. On the company level, e?cient and e?ective logistics and supply chain management are of critical importance for a company’s s- cessanditscompetitiveadvantage. Properperformanceofthelogisticsfunctions can contribute both to lower costs and to enhanced customer service. Computational Intelligence (CI) describes a set of methods and tools that often mimic biological or physical principles to solve problems that have been di?cult to solve by classical mathematics. CI embodies neural networks, fuzzy logic, evolutionary computation, local search, and machine learning approaches. Researchersthat workinthis areaoften comefromcomputer science,operations research,or mathematics, as well as from many di?erent engineering disciplines. Popular and successful CI methods for optimization and planning problems are heuristic optimization approaches such as evolutionary algorithms, local search methods, and other types of guided search methods.


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