Multidimensional dual-feasible functions and fast lower bounds for the vector packing problem

Multidimensional dual-feasible functions and fast lower bounds for the vector packing problem

0.00 Avg rating0 Votes
Article ID: iaor20141173
Volume: 233
Issue: 1
Start Page Number: 43
End Page Number: 63
Publication Date: Feb 2014
Journal: European Journal of Operational Research
Authors: , , ,
Keywords: packing, vector optimization
Abstract:

In this paper, we address the 2‐dimensional vector packing problem where an optimal layout for a set of items with two independent dimensions has to be found within the boundaries of a rectangle. Many practical applications in areas such as the telecommunications, transportation and production planning lead to this combinatorial problem. Here, we focus on the computation of fast lower bounds using original approaches based on the concept of dual‐feasible functions. Until now, all the dual‐feasible functions proposed in the literature were 1‐dimensional functions. In this paper, we extend the principles of dual‐feasible functions to the m‐dimensional case by introducing the concept of vector packing dual‐feasible function, and we propose and analyze different new families of functions. All the proposed approaches were tested extensively using benchmark instances described in the literature. Our computational results show that these functions can approximate very efficiently the best known lower bounds for this problem and improve significantly the convergence of branch‐and‐bound algorithms.

Reviews

Required fields are marked *. Your email address will not be published.