Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.cor.2010.11.009 | Computers & OR |
Keywords | DocType | Volume |
cost coefficient,Assignment problem,minmax regret assignment,Assignment Problem,heuristic method,Benders decomposition,lower bound,extensive computational experiment,Genetic algorithm,hybrid population-based heuristics,Hybrid heuristic,Interval data,interval data,robust assignment problem,Minmax regret optimization,heuristic algorithm,local search | Journal | 38 |
Issue | ISSN | Citations |
8 | Computers and Operations Research | 7 |
PageRank | References | Authors |
0.45 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordi Pereira | 1 | 25 | 2.38 |
| Igor Averbakh | 2 | 699 | 54.76 |