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Adaptive search heuristics for the generalized assignment problem
dc.contributor.author | Ramalhinho Lourenço, Helena |
dc.contributor.author | Serra, Daniel |
dc.date.accessioned | 2007-10-02T11:50:20Z |
dc.date.available | 2007-10-02T11:50:20Z |
dc.date.issued | 2002 |
dc.identifier.issn | 1134-5632 |
dc.identifier.uri | http://hdl.handle.net/2099/3627 |
dc.description.abstract | The Generalized Assignment Problem consists of assigning a set of tasks to a set of agents at minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the agent’s resource. We present the application of a MAX-MIN Ant System (MMAS) and a greedy randomized adaptive search procedure (GRASP) to the generalized assignment problem based on hybrid approaches. The MMAS heuristic can be seen as an adaptive sampling algorithm that takes into consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of a comparative analysis of the two adaptive heuristics, followed by concluding remarks and ideas on future research in generalized assignment related problems. |
dc.format.extent | 209-234 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 2002 Vol. 9 Núm. 2 [ -3 ] |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other | Generalized assignment |
dc.subject.other | Local search |
dc.subject.other | GRASP |
dc.subject.other | Tabu search |
dc.subject.other | Ant colony optimization (ACO) |
dc.title | Adaptive search heuristics for the generalized assignment problem |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.rights.access | Open Access |