Print

Generating Experimental Data for the Generalized Assignment Problem

Publication: Research - peer-reviewJournal article

The Generalized Assignment Problem (GAP) is the problem of finding the minimal cost assignment of jobs to machines such that each job is assigned to exactly one machine, subject to capacity restrictions on the machines. We propose a new stochastic model for the GAP. A tight condition on this stochastic model under which the GAP is feasible with probability one when the number of jobs goes to infinity is derived. This new stochastic model enables us to analyze the adequacy of most of the random generators given for the GAP in the literature. We demonstrate that the random generators commonly used to test solution procedures for the GAP tend to create easier problem instances when the number of machines m increases. We describe a greedy heuristic for the GAP, and use it to illustrate the results from the paper.

Publication information

Original languageEnglish
JournalOperations Research
Volume49
Issue number6
Pages (from-to)866-878
ISSN0030-364X
DOIs
StatePublished - 2001
Externally publishedYes

ID: 40951362