A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions

Haojie Chen, Guofu Ding, Jian Zhang*, Rong Li, Lei Jiang, Shengfeng Qin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness.
Original languageEnglish
Article number116911
Number of pages19
JournalExpert Systems with Applications
Volume198
Early online date22 Mar 2022
DOIs
Publication statusPublished - 15 Jul 2022

Fingerprint

Dive into the research topics of 'A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions'. Together they form a unique fingerprint.

Cite this