A nature inspired metaheuristic for optimal leveling of resources in project managementShow others and affiliations
2018 (English)In: SETN '18: Proceedings of the 10th Hellenic Conference on Artificial Intelligence, ACM Digital Library, 2018, article id 17Conference paper, Published paper (Refereed)
Abstract [en]
Resource Leveling is a constrained problem with a large solution space, especially when the projects have numerous tasks. This makes the problem hard to tackle. In this study, a novel algorithm named sonar inspired optimization (SIO) belonging in Nature Inspired Intelligence is applied in both benchmark and artificial resource-leveling problems to investigate its performance. Results are compared to those derived from a Hybrid Genetic Algorithm (HGA) previously developed. Experimental findings show that the proposed algorithm is very promising as in most cases the obtained solutions prove superior or equally good to those of HGA and other competitive approaches. An additional approach of the proposed metaheuristic is that it generates only feasible solutions. The algorithm has been implemented in different programming languages so that in future applications will be user friendly for project managers.
Place, publisher, year, edition, pages
ACM Digital Library, 2018. article id 17
Series
ACM International Conference Proceeding Series
Keywords [en]
Nature-inspired algorithms, Resource Leveling, Sonar Inspired Optimization, Artificial intelligence, Benchmarking, Constraint theory, Genetic algorithms, Problem oriented languages, Sonar, Constrained problem, Feasible solution, Future applications, Hybrid genetic algorithms, Large solutions, Nature inspired algorithms, Project managers, Project management
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-63838DOI: 10.1145/3200947.3201014Scopus ID: 2-s2.0-85052014752ISBN: 9781450364331 (print)OAI: oai:DiVA.org:hj-63838DiVA, id: diva2:1845903
Conference
10th Hellenic Conference on Artificial Intelligence, SETN 2018, 9-12 July 2018
2024-03-202024-03-202024-03-20Bibliographically approved