A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows

  • Authors:

  • Thibaut Vidal;Teodor Gabriel Crainic;Michel Gendreau;Christian Prins

  • Affiliations:

  • Institut Charles Delaunay - LOSI, Université de Technologie de Troyes, 12 rue Marie Curie, BP 2060, 10010 Troyes Cedex, France and CIRRELT et Département d'informatique et de recherche o ...;CIRRELT et Département de management et technologie, ícole des sciences de la gestion, UQAM, Montréal, Canada H3C 3P8;CIRRELT et Département de mathématiques et génie industriel, ícole Polytechnique, Montréal, Canada H3C 3A7;Institut Charles Delaunay - LOSI, Université de Technologie de Troyes, 12 rue Marie Curie, BP 2060, 10010 Troyes Cedex, France

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2013

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Abstract

The paper presents an efficient Hybrid Genetic Search with Advanced Diversity Control for a large class of time-constrained vehicle routing problems, introducing several new features to manage the temporal dimension. New move evaluation techniques are proposed, accounting for penalized infeasible solutions with respect to time-window and duration constraints, and allowing to evaluate moves from any classical neighbourhood based on arc or node exchanges in amortized constant time. Furthermore, geometric and structural problem decompositions are developed to address efficiently large problems. The proposed algorithm outperforms all current state-of-the-art approaches on classical literature benchmark instances for any combination of periodic, multi-depot, site-dependent, and duration-constrained vehicle routing problem with time windows.