Stronger K-tree relaxations for the vehicle routing problem [An article from: European Journal of Operational Research]
Book Details
Author(s)C. Martinhon, A. Lucena, N. Maculan
PublisherElsevier
ISBN / ASINB000RR0UVK
ISBN-13978B000RR0UV2
MarketplaceFrance 🇫🇷
Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
A Lagrangian based exact solution algorithm for the vehicle routing problem (VRP), defined on an undirected graph, is introduced in this paper. Lower bounds are obtained by allowing exponentially many inequalities as candidates to Lagrangian dualization. Three different families of strong valid inequalities (each one with exponentially many elements) are used within VRP formulations. For each of them, separation procedures are proposed for points that define incidence vectors of K-trees. Violated inequalities identified in this way are then dualized in a relax and cut framework. Upper bounds are generated through a Lagrangian Clarke and Wright heuristic. A variable fixation test based on (approximating) linear programming reduced costs, is also implemented. Computational results are presented for the proposed algorithm.
Description:
A Lagrangian based exact solution algorithm for the vehicle routing problem (VRP), defined on an undirected graph, is introduced in this paper. Lower bounds are obtained by allowing exponentially many inequalities as candidates to Lagrangian dualization. Three different families of strong valid inequalities (each one with exponentially many elements) are used within VRP formulations. For each of them, separation procedures are proposed for points that define incidence vectors of K-trees. Violated inequalities identified in this way are then dualized in a relax and cut framework. Upper bounds are generated through a Lagrangian Clarke and Wright heuristic. A variable fixation test based on (approximating) linear programming reduced costs, is also implemented. Computational results are presented for the proposed algorithm.
