Exact and heuristic algorithms for the interval data robust assignment problem

Authors:
Jordi Pereira;Igor Averbakh
Affiliations:
Escola Tècnica Superior d'Enginyers Industrials de Barcelona, Universitat Politècnica de Catalunya, Avda. Diagonal 647, 7th floor, 08028 Barcelona, Spain;Department of Management, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4
Venue:
Computers and Operations Research
Year:
2011

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Abstract

We consider the Assignment Problem with interval data, where it is assumed that only upper and lower bounds are known for each cost coefficient. It is required to find a minmax regret assignment. The problem is known to be strongly NP-hard. We present and compare computationally several exact and heuristic methods, including Benders decomposition, using CPLEX, a variable depth neighborhood local search, and two hybrid population-based heuristics. We report results of extensive computational experiments.