A surrogate-assisted dual-tree genetic programming framework for dynamic resource constrained multi-project scheduling problem

HaoJie Chen, XinYu Li and Liang Gao

International Journal of Production Research, 2024, vol. 62, issue 16, 5631-5653

Abstract: Genetic programming has achieved great success in project scheduling by generating Priority Rules (PRs) through evolution. However, the frequent disturbance factors in practice not only lead to the appropriate PR changes in different states, but also increase the calculation consumption in evaluation. In this paper, a novel Hyper-heuristic-based Surrogate-Assisted Dual-Tree Genetic Programming (HSDGP) framework is proposed for the Dynamic Resource Constrained Multi-Project Scheduling Problem with new project Insertions and resource Disruptions (DRCMPSP-ID). Uniquely, the proposed method automatically evolves two PRs for scheduling DRCMPSP-ID under normal and disruptive states respectively, and an expansion search mechanism based on neighbourhood is designed to improve PR generation ability by generating a large number of offspring and implement the search of dual-tree encoding. Furthermore, in order to estimate the fitness of new individuals, an activity-sequence based surrogate is proposed to deal with the input of activity sequence during schedule generation and reduce the evaluation calculation consumption. Based on the instances constructed by the existing benchmark, the experimental result shows the superiority of HSDGP and the impact of key parameters on its performance.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2023.2294109 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:62:y:2024:i:16:p:5631-5653

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2023.2294109

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().