Large neighborhood search for the double traveling salesman problem with multiple stacks Metadata

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Title

  • Main Title Large neighborhood search for the double traveling salesman problem with multiple stacks

Creator

  • Author: Bent, Russell W
    Creator Type: Personal
    Creator Info: Los Alamos National Laboratory
  • Author: Van Hentenryck, Pascal
    Creator Type: Personal
    Creator Info: BROWN UNIV

Contributor

  • Sponsor: United States. Department of Energy.
    Contributor Type: Organization

Publisher

  • Name: Los Alamos National Laboratory
    Place of Publication: [New Mexico]
    Additional Info: LANL

Date

  • Creation: 2009-01-01

Language

  • English

Description

  • Content Description: This paper considers a complex real-life short-haul/long haul pickup and delivery application. The problem can be modeled as double traveling salesman problem (TSP) in which the pickups and the deliveries happen in the first and second TSPs respectively. Moreover, the application features multiple stacks in which the items must be stored and the pickups and deliveries must take place in reserve (LIFO) order for each stack. The goal is to minimize the total travel time satisfying these constraints. This paper presents a large neighborhood search (LNS) algorithm which improves the best-known results on 65% of the available instances and is always within 2% of the best-known solutions.

Subject

  • Keyword: Computers
  • Keyword: Artificial Intelligence
  • Keyword: Algorithms
  • STI Subject Categories: 97

Source

  • Conference: Inter. Conf. on Integration of AI and OR Techniques in CP 2009 ; May 27, 2009 ; Pittsburg, PA

Collection

  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI

Institution

  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Article

Format

  • Text

Identifier

  • Report No.: LA-UR-09-00056
  • Report No.: LA-UR-09-56
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956471
  • Archival Resource Key: ark:/67531/metadc926656