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

  • Book
  • © 2008

Overview

Editors:
  1. Andreas Fink
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    You can also search for this editor in PubMed   Google Scholar

  2. Franz Rothlauf
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  • Presents Advances of Computational Intelligence in Transportation and Logistics

Part of the book series: Studies in Computational Intelligence (SCI, volume 144)

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Table of contents (13 chapters)

  1. Front Matter

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  2. Traffic and Transport Networks

    1. Front Matter

      Pages 1-1
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    2. Combined Genetic Computation of Microscopic Trip Demand in Urban Networks

      • Theodore Tsekeris, Loukas Dimitriou, Antony Stathopoulos
      Pages 3-21

    3. Genetically Optimized Infrastructure Design Strategies in Degradable Transport Networks

      • Loukas Dimitriou, Theodore Tsekeris, Antony Stathopoulos
      Pages 23-43

    4. Genetic Algorithm for Constraint Optimal Toll Ring Design

      • Agachai Sumalee
      Pages 45-61

    5. Real Time Identification of Road Traffic Control Measures

      • Khaled Almejalli, Keshav Dahal, M. Alamgir Hossain
      Pages 63-80

    6. Simultaneous Airline Scheduling

      • Tobias Grosche, Franz Rothlauf
      Pages 81-108

  3. Vehicle Routing

    1. Front Matter

      Pages 109-109
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    2. GRASP with Path Relinking for the Capacitated Arc Routing Problem with Time Windows

      • Nacima Labadi, Christian Prins, Mohamed Reghioui
      Pages 111-135

    3. A Scatter Search Algorithm for the Split Delivery Vehicle Routing Problem

      • Vicente Campos, Angel Corberán, Enrique Mota
      Pages 137-152

    4. Stochastic Local Search Procedures for the Probabilistic Two-Day Vehicle Routing Problem

      • Karl F. Doerner, Walter J. Gutjahr, Richard F. Hartl, Guglielmo Lulli
      Pages 153-168

    5. The Oil Drilling Model and Iterative Deepening Genetic Annealing Algorithm for the Traveling Salesman Problem

      • Hoong Chuin Lau, Fei Xiao
      Pages 169-184

    6. Online Transportation and Logistics Using Computationally Intelligent Anticipation

      • Peter A. N. Bosman, Han La Poutré
      Pages 185-208

  4. Supply Chain Management

    1. Front Matter

      Pages 209-209
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    2. Supply Chain Inventory Optimisation with Multiple Objectives: An Industrial Case Study

      • Lionel Amodeo, Haoxun Chen, Aboubacar El Hadji
      Pages 211-230

    3. Decomposition of Dynamic Single-Product and Multi-product Lotsizing Problems and Scalability of EDAs

      • Jörn Grahl, Stefan Minner, Franz Rothlauf
      Pages 231-251

    4. Hybrid Genetic Algorithms for the Lot Production and Delivery Scheduling Problem in a Two-Echelon Supply Chain

      • S. Ali Torabi, Masoud Jenabi, S. Afshin Mansouri
      Pages 253-275

  5. Back Matter

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Keywords

About this book

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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