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Computational Intelligence and Combinatorial Optimization Problems in Transportation Science

  • Manolis N. KritikosEmail author
  • Pantelis Z. Lappas
Chapter
  • 4 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 14)

Abstract

The purpose of this chapter is to highlight the use of computational intelligence algorithms for solving a special class of combinatorial optimization problems in transportation science called routing problems. Classical routing problems, such as the Traveling Salesman Problem and the Vehicle Routing Problem, as well as highly relevant extensions of classical routing problems like Vehicle Routing Problem with Time Windows and Inventory Routing Problem have received a great deal of attention from academics, consultants and practitioners in the field of Supply Chain Management. The contribution of this study is fourfold: (i) it provides a comprehensive review of various solution algorithms that have been proposed in literature as possible solutions to many of the complex issues surrounding routing problem management, (ii) it presents formulation schemes related to basic routing problems and their extensions, (iii) it promotes the use of selected computational algorithms called meta-heuristics and sim-heuristics by providing various graphical presentation formats so as to simplify complicated issues and convey meaningful insights into the routing problems and (iv) it points out interesting research directions for further development in routing problems.

Keywords

Computational intelligence Combinatorial optimization Routing problem Genetic algorithm Variable neighborhood search algorithm K-means clustering algorithm Sim-heuristic algorithm 

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Authors and Affiliations

  1. 1.Management Science Laboratory, Department of Management Science and TechnologyAthens University of Economics and BusinessAthensGreece
  2. 2.Stochastic Modeling and Applications Laboratory, Department of StatisticsAthens University of Economics and BusinessAthensGreece

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