TY - JOUR
T1 - A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions
AU - Chen, Haojie
AU - Zhang, Jian
AU - Li, Rong
AU - Ding, Guofu
AU - Qin, Shengfeng
N1 - Funding information: This research is supported by the National Key Research and Development Program of China (Grant number 2020YFB1712200).
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - Multi-state combination scheduling
KW - Genetic programming
KW - Hyper-heuristic
KW - Priority rule
KW - Stochastic resource constrained multi-project scheduling
UR - http://www.scopus.com/inward/record.url?scp=85131688808&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2022.109087
DO - 10.1016/j.asoc.2022.109087
M3 - Article
SN - 1568-4946
VL - 124
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 109087
ER -