[Studies in Computational Intelligence] Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management Volume 144 || Online Transportation and Logistics Using Computationally Intelligent Anticipation

✍ Scribed by Fink, Andreas; Rothlauf, Franz

• Book ID: 121412523
• Publisher: Springer Berlin Heidelberg
• Year: 2008
• Tongue: English
• Weight: 350 KB
• Edition: 1
• Category: Article
• ISBN: 3540693904
• DOI: 10.1007/978-3-540-69390-1_10

✦ Synopsis

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.