Using priority rules for resource-constrained project scheduling problem in static environment. (July 2022)

• Record Type: Journal Article
• Authors: Ðumić, Mateja; Jakobović, Domagoj
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Genetic programming -- Resource constrained project scheduling problem -- Priority rules -- Iterative priority rules -- Rollout -- Static environment; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108239
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Two adaptations for evolving priority rules for static environment. Iterative priority rules and roll-out approach. A comparison of results with dynamic priority rules and genetic algorithm. An analysis of the time required for evolving rules and solving new instances. Adapted priority rules achieve better results than the one evolved with standard GP. Abstract: The resource-constrained project scheduling problem (RCPSP) is one of the scheduling problems that belong to the class of NP-hard problems. Therefore, heuristic approaches are usually used to solve it. One of the most commonly used heuristic approaches are priority rules (PRs). PRs are easy to use, fast and able to respond to system changes, which makes them applicable in a dynamic environment. The disadvantage of PRs is that when applied in a static environment, they do not achieve results of the same quality as heuristic approaches designed for a static environment. Moreover, a new PR must be evolved separately for each optimization criterion, which is a challenging process. Therefore, recently significant effort has been put into the automatic development of PRs. Although PRs are mainly used in a dynamic environment, they are also used in a static environment in situations where speed and simplicity are more important than the quality of the obtained solution. Since PRs evolved for a dynamic environment do not use all the information available in a static environment, this paper analyzes two adaptations for evolving PRs in a static environment for the RCPSP - iterative priority rules and rollout approach. This paper shows that these approaches achieve better results than the PRs evolved and used without these adaptations. The results of the approaches presented in the paper were also compared with the results obtained with the genetic algorithm as a representative of the heuristic approaches used mainly in the static environment.