Using lagrangian relaxation to solve ready mixed concrete dispatching problems

Research output: Contribution to book/Conference proceedings/Anthology/ReportChapter in book/Anthology/ReportContributedpeer-review

Contributors

  • Pavan Kumar Narayanan - , State University of New York (SUNY) at Buffalo (Author)
  • David Rey - , University of New South Wales (Author)
  • Mojtaba Maghrebi - , University of New South Wales (Author)
  • S. Travis Waller - , University of New South Wales (Author)

Abstract

The logistics and planning problem of delivering ready mixed concrete (RMC) to a set of demand customers from multiple depots is addressed. The RMC dispatching problem (RMCDP) is closely related to the vehicle routing problem, with the difference that a truck may visit demand nodes in the RMCDP more than once. This class of routing problems can be represented by using mixed-integer programming (MIP) and is known to be NP-hard. Solving RMC delivery problems is often achieved through heuristics and metaheuristic-based methods as exact solution approaches are often unable to find optimal solutions efficiently, in particular when multiple depots are represented in the model. Although a variety of methods are available to solve MIP models, in this paper an attempt is made to solve the RMCDP by using a Lagrangian relaxation technique. Namely, a solution algorithm based on Lagrangian relaxation is derived to reduce the complexity of the initial MIP model and show that the proposed relaxation is able to provide promising computation results on a realistic data set representative of an active RMCDP in the region of Adelaide, Australia.

Details

Original languageEnglish
Title of host publicationTransportation Research Record
PublisherUS National Research Council
Pages84-90
Number of pages7
ISBN (electronic)9780309295758
Publication statusPublished - 2015
Peer-reviewedYes
Externally publishedYes

Publication series

SeriesTransportation Research Record
Volume2498
ISSN0361-1981

External IDs

ORCID /0000-0002-2939-2090/work/141543808