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

Christian Tummel (), Tobias Pyttel, Philipp Wolters, Eckart Hauck and Sabina Jeschke
Additional contact information
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

A chapter in Automation, Communication and Cybernetics in Science and Engineering 2013/2014, 2014, pp 695-711 from Springer

Abstract: 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.

Keywords: Genetic Algorithm; m-VRPTWAR; Multi-Objective; Multi-Step; LTL; Generation-Based; Usage-Based; CloudLogistic (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-08816-7_54

Ordering information: This item can be ordered from
http://www.springer.com/9783319088167

DOI: 10.1007/978-3-319-08816-7_54

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().