Fleet Size and Mix Vehicle Routing: A Multi-Criterion Grouping Genetic Algorithm Approach

Chapter
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 666)

Abstract

The need for efficient transportation is ever increasing in every society over the globe. Transportation costs account for a significant percentage of the total cost of a product. Strong global competition continues to aggravate the demand for higher efficiency, high quality of service, timeliness, reactivity, and cost-effectiveness in transportation. It is therefore important to optimize vehicle routing in order to provide cost-effective services to customers and to maintain the momentum of the business in the long term. Multiple criteria such as routing cost and workload balancing should be considered. This chapter considers the fleet size and mix vehicle routing problem (FSMVRP), where the fleet size and its composition are to be determined. A multi-criterion grouping genetic algorithm (GGA) with unique grouping genetic operators is presented and tested on benchmark problems. Comparative computational results show that GGA is competitive in multi-criterion decision making.

Keywords

Fleet size and mix Vehicle routing Multi-criterion decision making Genetic algorithms Grouping genetic algorithm Logistics 
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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringNamibia University of Science and TechnologyWindhoekNamibia
  2. 2.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

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