Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows

  • Manuel López-Ibáñez
  • Christian Blum
  • Dhananjay Thiruvady
  • Andreas T. Ernst
  • Bernd Meyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5482)


The travelling salesman problem with time windows is a difficult optimization problem that appears, for example, in logistics. Among the possible objective functions we chose the optimization of the makespan. For solving this problem we propose a so-called Beam-ACO algorithm, which is a hybrid method that combines ant colony optimization with beam search. In general, Beam-ACO algorithms heavily rely on accurate and computationally inexpensive bounding information for differentiating between partial solutions. In this work we use stochastic sampling as an alternative to bounding information. Our results clearly demonstrate that the proposed algorithm is currently a state-of-the-art method for the tackled problem.


Travel Salesman Problem Travel Salesman Problem Partial Solution Assembly Line Balance Heuristic Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Manuel López-Ibáñez
    • 1
  • Christian Blum
    • 1
  • Dhananjay Thiruvady
    • 2
    • 3
  • Andreas T. Ernst
    • 3
  • Bernd Meyer
    • 2
  1. 1.ALBCOM Research GroupUniversitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Calyton School of Information TechnologyMonash UniversityAustralia
  3. 3.CSIRO Mathematics and Information SciencesAustralia

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