A cross decomposition algorithm for capacitated facility location
Operations Research
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Hashing vectors for tabu search
Annals of Operations Research - Special issue on Tabu search
Capacitated facility location: separation algorithms and computational experience
Mathematical Programming: Series A and B - Special issue on computational integer programming
Tabu Search
Algorithms for Network Programming
Algorithms for Network Programming
Solving the uncapacitated facility location problem using tabu search
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
WEA'03 Proceedings of the 2nd international conference on Experimental and efficient algorithms
Near-optimal solutions to large-scale facility location problems
Discrete Optimization
Solving the two-stage capacitated facility location problem by the lagrangian heuristic
ICCL'12 Proceedings of the Third international conference on Computational Logistics
Kernel search for the capacitated facility location problem
Journal of Heuristics
Applied p-median and p-center algorithms for facility location problems
Expert Systems with Applications: An International Journal
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A tabu search heuristic procedure is developed, implemented and computationally tested for the capacitated facility location problem. The procedure uses different memory structures. Visited solutions are stored in a primogenitary linked quad tree. For each facility, the recent move at which the facility changed its status and the frequency it has been open are also stored. These memory structures are used to guide the main search process as well as the diversification and intensification processes. Lower bounds on the decreases of total cost are used to measure the attractiveness of the moves and to select moves in the search process. A specialized network algorithm is developed to exploit the problem structure in solving transportation problems. Criterion altering, solution reconciling and path relinking are used to perform intensification functions. The performance of the procedure is tested through computational experiments using test problems from the literature and new test problems randomly generated. It found optimal solutions for almost all test problems from the literature. As compared to the heuristic method of Lagrangean relaxation with improved subgradient scheme, the tabu search heuristic procedure found much better solutions using much less CPU time.