Annals of Operations Research - Special issue on Tabu search
Reactive search, a history-sensitive heuristic for MAX-SAT
Journal of Experimental Algorithmics (JEA)
Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
Management Science
The budgeted maximum coverage problem
Information Processing Letters
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Local Optimization and the Traveling Salesman Problem
ICALP '90 Proceedings of the 17th International Colloquium on Automata, Languages and Programming
Selected Papers from AISB Workshop on Evolutionary Computing
A Heuristic Method for the Set Covering Problem
Operations Research
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
A unified approach to approximating partial covering problems
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A New Hybrid Iterated Local Search for the Open Vehicle Routing Problem
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 01
Analysis of Approximation Algorithms for k-Set Cover Using Factor-Revealing Linear Programs
Theory of Computing Systems
Iterated robust tabu search for MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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In this paper, we propose a heuristic algorithm to solve a new variant of the partial set covering problem. In this variant, each element $$e_i$$ei has a gain $$g_i$$gi (i.e., a positive profit), each set $$s_j$$sj has a cost $$c_j$$cj (i.e., a negative profit), and each set $$s_j$$sj is part of a unique group $$G_k$$Gk that has a fixed cost $$f_k$$fk (i.e., a negative profit). The objective is to maximize profit and it is not necessary to cover all of the elements. We present an industrial application of the model and propose a hybrid heuristic algorithm to solve it; the proposed algorithm is an iterated-local-search algorithm that uses two levels of perturbations and a tabu-search heuristic. Whereas the first level of perturbation diversifies the search around the current local optimum, the second level of perturbation performs long jumps in the search space to help escape from local optima with large basins of attraction. The proposed algorithm is evaluated on thirty real-world problems and compared to a memetic algorithm. Computational results show that most of the solutions found by ITS are either optimal or very close to optimality.