Generating Experimental Data for the Generalized Assignment Problem

H. Edwin Romeijn, Dolores Romero Morales

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalOperations Research
Issue number6
Pages (from-to)866-878
Publication statusPublished - 2001
Externally publishedYes

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