A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions. (15th July 2022)

• Record Type: Journal Article
• Authors: Chen, HaoJie; Ding, Guofu; Zhang, Jian; Li, Rong; Jiang, Lei; Qin, Shengfeng
• Is Part Of: Expert systems with applications. Volume 198(2022)
• Publication Date: 2022-07-15
• Subjects: Filtering evolution -- Genetic programming -- Priority rule -- Stochastic resource constrained multi-project scheduling; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116911
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A HH-FGP framework is proposed to solve the SRCMPSP-NPI. The evolutionary process is divided to achieve PR sampling and generation. A new evaluation mechanism is proposed for depth range and attributes filtering. The search operators are improved to avoid producing PRs with illegal depth. 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.