A genetic programming hyper-heuristic with whale optimization algorithm for the dynamic resource-constrained multi-project scheduling problems

The resource-constrained multi-project scheduling problem (RCMPSP) often treats resource transfer time as a fixed parameter, neglecting its real-world variability. However, in high-end electronic equipment assembly and testing, resource transfer time is dynamically influenced by factors such as kit...

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Bibliographic Details
Published in:Expert systems with applications Vol. 295; p. 128881
Main Authors: Chao, Yutong, Zhuang, Cunbo, Guo, Haoxin, Liu, Jianhua
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2026
Subjects:
Genetic programming
Hyper-heuristics
Resource transfer
Resource-constrained multi-project scheduling
Resource transfer
Hyper-heuristics
Resource-constrained multi-project scheduling
Genetic programming
ISSN:0957-4174
Online Access:Get full text
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Summary:The resource-constrained multi-project scheduling problem (RCMPSP) often treats resource transfer time as a fixed parameter, neglecting its real-world variability. However, in high-end electronic equipment assembly and testing, resource transfer time is dynamically influenced by factors such as kit completion rates. This paper studies a dynamic RCMPSP with adjustable resource transfer times based on kit completion rates (DRCMPSP-RT&MK). While traditional genetic programming hyper-heuristic (GPHH) algorithms struggle with large-scale problems, we propose an enhanced algorithm, GPHH-WOA, which integrates the whale optimization algorithm (WOA) into GPHH and incorporates dynamic task and resource-transfer attributes into its rule-optimization process. To validate the algorithm’s effectiveness, we first compare the proposed method against six heuristic task-priority rules with static attributes. Second, we benchmark it against two existing GPHH variants and their surrogate-assisted versions. Experiments on three self-generated datasets of varying scales demonstrate that the proposed method significantly improves solution quality, with greater advantages as problem complexity increases. The results confirm the algorithm’s feasibility and effectiveness for large-scale DRCMPSP-RT&MK in dynamic environments.
ISSN:0957-4174
DOI:10.1016/j.eswa.2025.128881