Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Tracking and data association
Integer and combinatorial optimization
Integer and combinatorial optimization
Flight scheduling and maintenance base planning
Management Science
An algorithm for the three-index assignment problem
Operations Research
Linear network optimization: algorithms and codes
Linear network optimization: algorithms and codes
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Approximation algorithms for multi-dimensional assignment problems with decomposable costs
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Computational Optimization and Applications
A New Lagrangian Relaxation Based Algorithm for a Class ofMultidimensional Assignment Problems
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
A class of lagrangian relaxation algorithms for the multidimensional assignment problem
A class of lagrangian relaxation algorithms for the multidimensional assignment problem
GRASP for linear integer programming
Metaheuristics
Cybernetics and Systems Analysis
A multi-objective model for environmental investment decision making
Computers and Operations Research
Local Search Heuristics for the Multidimensional Assignment Problem
Graph Theory, Computational Intelligence and Thought
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Local search heuristics for the multidimensional assignment problem
Journal of Heuristics
WAOA'06 Proceedings of the 4th international conference on Approximation and Online Algorithms
Some assignment problems arising from multiple target tracking
Mathematical and Computer Modelling: An International Journal
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The focal problem for centralized multisensor multitarget tracking is the data association problem of partitioning the observations into tracks and false alarms so that an accurate estimate of the true tracks can be recovered. Large classes of these association problems can be formulated as multidimensional assignment problems, which are known to be NP-hard for three dimensions or more. The assignment problems that result from tracking are large scale, sparse and noisy. Solution methods must execute in real-time. The Greedy Randomized Adaptive Local Search Procedure (GRASP) has proven highly effective for solving many classes NP-hard optimization problems. This paper introduces four GRASP implementations for the multidimensional assignment problem, which are combinations of two constructive methods (randomized reduced cost greedy and randomized max regret) and two local search methods (two-assignment-exchange and variable depth exchange). Numerical results are shown for a two random problem classes and one tracking problem class.