Generating experimental data for the generalized assignment problem

Generating experimental data for the generalized assignment problem

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Article ID: iaor2003384
Country: United States
Volume: 49
Issue: 6
Start Page Number: 866
End Page Number: 878
Publication Date: Nov 2001
Journal: Operations Research
Authors: ,
Keywords: statistics: general
Abstract:

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.

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