caa a22 4500 605469318 CHVBK 20210128100321.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s00500-014-1455-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1455-x Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem [Elektronische Daten] [Paweł Myszkowski, Marek Skowroński, Łukasz Olech, Krzysztof Oślizło] In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO. The Author(s), 2014 Ant colony optimization nationallicence Project scheduling problem nationallicence Metaheuristics nationallicence Hybrid ACO nationallicence Multi-objective optimization nationallicence Benchmark dataset nationallicence Myszkowski Paweł Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut Skowroński Marek Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut Olech Łukasz Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut Oślizło Krzysztof Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3599-3619 1432-7643 19:12<3599 2015 19 500 https://doi.org/10.1007/s00500-014-1455-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1455-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Myszkowski Paweł Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut NATIONALLICENCE 700 1- Skowroński Marek Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut NATIONALLICENCE 700 1- Olech Łukasz Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut NATIONALLICENCE 700 1- Oślizło Krzysztof Department of Artificial Intelligence, Wroclaw University of Technology, Wrocław, Poland aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3599-3619 1432-7643 19:12<3599 2015 19 500