Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
The multiple container packing problem: a genetic algorithm approach with weighted codings
ACM SIGAPP Applied Computing Review
Heuristic Solutions for the Multiple-Choice Multi-dimension Knapsack Problem
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Building an Adaptive Multimedia System using the Utility Model
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Towards the real time solution of strike force asset allocation problems
Computers and Operations Research
Computers and Operations Research
A Reactive Local Search-Based Algorithm for the Multiple-Choice Multi-Dimensional Knapsack Problem
Computational Optimization and Applications
Solving the multidimensional multiple-choice knapsack problem by constructing convex hulls
Computers and Operations Research
Computers and Operations Research
Synthetic Optimization Problem Generation: Show Us the Correlations!
INFORMS Journal on Computing
Hard multidimensional multiple choice knapsack problems, an empirical study
Computers and Operations Research
A column generation method for the multiple-choice multi-dimensional knapsack problem
Computational Optimization and Applications
Computers and Operations Research
A New Heuristic for Solving the Multichoice Multidimensional Knapsack Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We apply tabu search to the multiple-choice multidimensional knapsack problem. An initial solution involves a simple greedy heuristic. A neighbourhood is generated and a move is selected based on the best feasible neighbouring solution. The search continues for a fixed number of iterations and tabu search structures are used to improve the search process. We study the problem structure of available, benchmark, test problem instances and develop a new, more diverse set of test problem instances whose results better generalise to practice than do results obtained using existing test problems. We report the results of testing our tabu search approach versus existing solution approaches as applied to the available and our new test problem instances. The computational results show that our proposed approach performs comparably or better than the legacy heuristic approaches and clearly demonstrates that test problems affects how well results generalise to practice.