Advances in computational intelligence in transport, logistics, and supply chain management
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Bibliographic Information
Advances in computational intelligence in transport, logistics, and supply chain management
（Studies in computational intelligence, v. 144）
Springer, c2008
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Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
Traffic and Transport Networks.- Combined Genetic Computation of Microscopic Trip Demand in Urban Networks.- Genetically Optimized Infrastructure Design Strategies in Degradable Transport Networks.- Genetic Algorithm for Constraint Optimal Toll Ring Design.- Real Time Identification of Road Traffic Control Measures.- Simultaneous Airline Scheduling.- Vehicle Routing.- GRASP with Path Relinking for the Capacitated Arc Routing Problem with Time Windows.- A Scatter Search Algorithm for the Split Delivery Vehicle Routing Problem.- Stochastic Local Search Procedures for the Probabilistic Two-Day Vehicle Routing Problem.- The Oil Drilling Model and Iterative Deepening Genetic Annealing Algorithm for the Traveling Salesman Problem.- Online Transportation and Logistics Using Computationally Intelligent Anticipation.- Supply Chain Management.- Supply Chain Inventory Optimisation with Multiple Objectives: An Industrial Case Study.- Decomposition of Dynamic Single-Product and Multi-product Lotsizing Problems and Scalability of EDAs.- Hybrid Genetic Algorithms for the Lot Production and Delivery Scheduling Problem in a Two-Echelon Supply Chain.
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