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Line-Based Optimization of LTL-Shipments Using a Multi-Step Genetic Algorithm

In: Automation, Communication and Cybernetics in Science and Engineering 2013/2014

Author

Listed:
  • Christian Tummel

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Tobias Pyttel

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Philipp Wolters

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Eckart Hauck

    (RWTH Aachen University, IMA/ZLW & IfU)

  • Sabina Jeschke

    (RWTH Aachen University, IMA/ZLW & IfU)

Abstract

Motivated by the so-called “CloudLogistic”-concept as an innovative, line-based way for dealing with less than truck load (LTL) shipments in cooperation networks, this paper introduces a genetic algorithm as a heuristical approach for dealing with multi-objective optimization problems. Based on the implied optimization problem – the NP-hard multi-depot heterogeneous fleet vehicle routing problem with time windows and assignment restrictions (m-VRPTWAR) - four different optimization goals of the “CloudLogistic”-concept are introduced and a multi-step approach is motivated. Therefore, two different optimization steps are presented and transferred into a genetic algorithm. Additionally, two innovative problem-specific genetic operators are introduced by combining a generation-based approach and a usage-based approach in order to create a useful mutation process. A further usage-based approach is used to realize a problem-specific crossover operator. The presented genetic multi-step approach is a useful concept for dealing with multi-objective optimization problems without the need of a single combined fitness function.

Suggested Citation

  • Christian Tummel & Tobias Pyttel & Philipp Wolters & Eckart Hauck & Sabina Jeschke, 2014. "Line-Based Optimization of LTL-Shipments Using a Multi-Step Genetic Algorithm," Springer Books, in: Sabina Jeschke & Ingrid Isenhardt & Frank Hees & Klaus Henning (ed.), Automation, Communication and Cybernetics in Science and Engineering 2013/2014, edition 127, pages 695-711, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-08816-7_54
    DOI: 10.1007/978-3-319-08816-7_54
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