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A hybrid genetic algorithm for the resource-constrained project scheduling problem |
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| Authors: | Vicente Valls Francisco Ballestín Sacramento Quintanilla |
| Institution: | 1. Dpto. de Estadística e Investigación Operativa, Facultad de Matemáticas, Universitat de Valencia, Dr. Moliner, 50, 46100 Burjassot, Valencia, Spain;2. Dpto. de Estadística e Investigación Operativa, Facultad de Ciencias Económicas y Empresariales, Universidad Pública de Navarra, Campus Arrosadía s/n, 31006 Pamplona, Spain;3. Dpto. de Economía Financiera y Matemática, Facultad de Económicas y Empresariales, Universitat de Valencia, Avda. de los Naranjos, s/n, Edificio Departamental Oriental, 46071 Valencia, Spain |
| Abstract: | In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j60 and j120. And that it is competitive with other state-of-the-art heuristics for the instance set j30. |
| Keywords: | Project management Scheduling Heuristics |
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