[1]QIN Minmin,LIU Lifang,QI Xiaogang.Hybrid genetic long-nosed raccoon optimization algorithm for maintenance resource allocation and scheduling[J].CAAI Transactions on Intelligent Systems,2023,18(6):1322-1335.[doi:10.11992/tis.202303035]
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Hybrid genetic long-nosed raccoon optimization algorithm for maintenance resource allocation and scheduling

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1322-1335 Column: 学术论文—人工智能基础 Public date: 2023-11-05
Title:
Hybrid genetic long-nosed raccoon optimization algorithm for maintenance resource allocation and scheduling
Author(s):
QIN Minmin1;  LIU Lifang1;  QI Xiaogang2; 3
1. School of Computer Science and Technology, Xidian University, Xi’an 710071, China;
2. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China;
3. Xi’an Key Laboratory of network modeling and resource scheduling, Xi’an 710071, China
Keywords:
resource constraints; project scheduling; multimode; resource allocation; allocation scheduling; dynamic publishing; multiple maintenance centers
CLC:
TP391
DOI:
10.11992/tis.202303035
Abstract:
Traditional resource-constrained project scheduling problems (RCPSPs) can hardly meet current practical needs. Hence, expanding the RCPSP is an inevitable trend. Therefore, this paper abstracts the original RCPSP problem based on the characteristics of dynamically releasing maintenance tasks for equipment, adding multimodal resource allocation issues related to equipment. Thus, a multimode dynamic resource allocation and scheduling model for multiple maintenance centers is established. This paper proposes a hybrid optimization algorithm combining genetic and long-nosed raccoon algorithms to solve the proposed model effectively. The algorithm is added with the selection, crossover, and mutation operators of the genetic algorithm based on the original coati optimization algorithm, which are mainly used to expand the search range, thereby jumping out of local optimization. Furthermore, greedy operator functions are added to further improve the quality of candidate solutions. Comparative analysis of the results of simulative experiments revealed that the newly proposed genetic long-nosed raccoon hybrid optimization algorithm is superior to other algorithms in terms of convergence speed and solution quality.
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