A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions

Chen, Haojie, Zhang, Jian, Li, Rong, Ding, Guofu and Qin, Sheng-feng (2022) A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions. Applied Soft Computing, 124. p. 109087. ISSN 1568-4946

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Official URL: https://doi.org/10.1016/j.asoc.2022.109087

Abstract

This study proposes a novel hyper-heuristic based two-stage genetic programming framework (HH-TGP) to solve the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI). It divides the evolution of genetic programming into generation and selection stages, and then establishes a multi-state combination scheduling mode with multiple priority rules (PRs) for the first time to realize resource constrained project scheduling under both stochastic activity duration and new project insertion. In the generation stage, based on a modified attribute set for multi-project scheduling, NSGA-II is hybridized to evolve a non-dominated PR set for forming a selectable PR set. While in the selection stage, the whole decision-making process is divided into multiple states based on the completion activity duration, and a weighted normalized evolution process with two crossovers, two mutations and four local search operators to match the optimal PR for each state from the PR set. Under the existing benchmark, HH-TGP is compared with the existing methods to verify its effectiveness.

Item Type: Article
Additional Information: Funding information: This research is supported by the National Key Research and Development Program of China (Grant number 2020YFB1712200).
Uncontrolled Keywords: Multi-state combination scheduling, Genetic programming, Hyper-heuristic, Priority rule, Stochastic resource constrained multi-project scheduling
Subjects: G400 Computer Science
W200 Design studies
Department: Faculties > Arts, Design and Social Sciences > Design
Depositing User: John Coen
Date Deposited: 07 Jun 2022 09:28
Last Modified: 02 Jun 2023 08:00
URI: https://nrl.northumbria.ac.uk/id/eprint/49262

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