TY - JOUR
T1 - A resource-constrained multi-project scheduling problem considering discrete time-resource leveling optimization for the assembly process of complex products
AU - Pei, Fengque
AU - Sun, Xu
AU - Liu, Jianhua
AU - Zhuang, Cunbo
AU - Ding, Hongyan
N1 - Publisher Copyright:
© 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/3
Y1 - 2026/3
N2 - The assembly process of complex aerospace products such as satellites involves multi-project parallelism and multi-resource sharing, which can be modeled as a type of resource constrained multi-project scheduling problem (RCMPSP). Existing research on RCMPSP has predominantly prioritized time-based optimization, while frequently overlooking practical considerations such as resource utilization efficiency. This paper investigates a new multi-objective RCMPSP for complex product assembly process considering discrete time-resource leveling optimization. To solve the problem, we propose an enhanced non-dominated sorting genetic algorithm (NSGA II) driven by general variable neighborhood search (GVNS). The novelty of the algorithm lies in its improved initialization method based on the NEH heuristic, which enhances convergence. Additionally, two new neighborhood structures and population evolution mechanisms are designed. Pareto frontier decision-making is performed based on the entropy weight TOPSIS method. Finally, computational experiments are carried out based on MPSPLIB and engineering case. Experimental results show that the proposed GVNS-NSGA II outperforms the standard NSGA II, the improved MOEA/D and JAYA in terms of scheduling results, quality of Pareto solutions, coverage of solution set, and IGD metric. The proposed model and algorithm provide a decision-making tool for enterprises to improve production efficiency and resource stability in complex product assembly.
AB - The assembly process of complex aerospace products such as satellites involves multi-project parallelism and multi-resource sharing, which can be modeled as a type of resource constrained multi-project scheduling problem (RCMPSP). Existing research on RCMPSP has predominantly prioritized time-based optimization, while frequently overlooking practical considerations such as resource utilization efficiency. This paper investigates a new multi-objective RCMPSP for complex product assembly process considering discrete time-resource leveling optimization. To solve the problem, we propose an enhanced non-dominated sorting genetic algorithm (NSGA II) driven by general variable neighborhood search (GVNS). The novelty of the algorithm lies in its improved initialization method based on the NEH heuristic, which enhances convergence. Additionally, two new neighborhood structures and population evolution mechanisms are designed. Pareto frontier decision-making is performed based on the entropy weight TOPSIS method. Finally, computational experiments are carried out based on MPSPLIB and engineering case. Experimental results show that the proposed GVNS-NSGA II outperforms the standard NSGA II, the improved MOEA/D and JAYA in terms of scheduling results, quality of Pareto solutions, coverage of solution set, and IGD metric. The proposed model and algorithm provide a decision-making tool for enterprises to improve production efficiency and resource stability in complex product assembly.
KW - Complex product
KW - Discrete time-resource leveling optimization
KW - General variable neighborhood search
KW - NSGA II
KW - Resource constrained multi-project scheduling problem
UR - https://www.scopus.com/pages/publications/105035671562
U2 - 10.1016/j.swevo.2026.102356
DO - 10.1016/j.swevo.2026.102356
M3 - Article
AN - SCOPUS:105035671562
SN - 2210-6502
VL - 103
JO - Swarm and Evolutionary Computation
JF - Swarm and Evolutionary Computation
M1 - 102356
ER -