An algorithm for the three-index assignment problem
Operations Research
Perspectives of Monge properties in optimization
Discrete Applied Mathematics
Computational Optimization and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
Establishing motion correspondence using extended temporal scope
Artificial Intelligence
Randomized parallel algorithms for the multidimensional assignment problem
Applied Numerical Mathematics - Numerical algorithms, parallelism and applications
Test Problem Generator for the Multidimensional Assignment Problem
Computational Optimization and Applications
GRASP with Path Relinking for Three-Index Assignment
INFORMS Journal on Computing
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Asymptotic behavior of the expected optimal value of the multidimensional assignment problem
Mathematical Programming: Series A and B
Discrete Applied Mathematics
Target tracking with distributed sensors: The focus of attention problem
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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The Multidimensional Assignment Problem (MAP) (abbreviated s-AP in the case of s dimensions) is an extension of the well-known assignment problem. The most studied case of MAP is 3-AP, though the problems with larger values of s also have a large number of applications. We consider several known neighborhoods, generalize them and propose some new ones. The heuristics are evaluated both theoretically and experimentally and dominating algorithms are selected. We also demonstrate that a combination of two neighborhoods may yield a heuristics which is superior to both of its components.