The book at hand presents a careful selection of relevant applications of CI methods for transport, logistics, and supply chain management problems. The chapters illustrate the current state-of-the-art in the application of CI methods in these fields and should help and inspire researchers and practitioners to apply and develop efficient methods. A few contributions in this book are extended versions of papers presented at EvoTransLog2007: The First European Workshop on Evolutionary Computation in Transportation and Logistics which was held in Valencia, Spain, in 2007. The majority of contributions are from additional, specially selected researchers, who have done relevant work in different areas of transport, logistics, and supply chain management. The goal is to broadly cover representative applications in these fields as well as different types of solution approaches. On the application side, the contributions focus on design of traffic and transportation networks, vehicle routing, and other important aspects of supply chain management such as inventory management, lot sizing, and lot scheduling. On the method side, the contributions deal with evolutionary algorithms, local search approaches, and scatter search combined with other CI techniques such as neural networks or fuzzy approaches. The book is structured according to the application domains. Thus, it has three parts dealing with traffic and transportation networks, vehicle routing, and supply chain management.

Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management (Studies in Computational Intelligence) | Andreas Fink (Editor), Franz Rothlauf (Editor) | Springer

Table of Contents
Chapter 1: Combined Genetic Computation of Microscopic Trip Demand in Urban Networks
Chapter 2: Genetically Optimized Infrastructure Design Strategies in Degradable Transport Networks
Chapter 3: Genetic Algorithm for Constraint Optimal Toll Ring Design
Chapter 4: Real Time Identification of Road Traffic Control Measures
Chapter 5: Simultaneous Airline Scheduling
Chapter 6: GRASP with Path Relinking for the Capacitated Arc Routing Problem with Time Windows
Chapter 7: A Scatter Search Algorithm for the Split Delivery Vehicle Routing Problem
Chapter 8: Stochastic Local Search Procedures for the Probabilistic Two-Day Vehicle Routing Problem
Chapter 9: The Oil Drilling Model and Iterative Deepening Genetic Annealing Algorithm for the Traveling Salesman Problem
Chapter 10: Online Transportation and Logistics Using Computationally Intelligent Anticipation
Chapter 11: Supply Chain Inventory Optimisation with Multiple Objectives
Chapter 12: Decomposition of Dynamic Single-Product and Multi-product Lotsizing Problems and Scalability of EDAs
Chapter 13: Hybrid Genetic Algorithms for the Lot Production and Delivery Scheduling Problem in a Two-Echelon Supply Chain.