PMID- 37884636
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231029
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 26
TI  - A modified particle swarm optimization algorithm for a vehicle scheduling problem 
      with soft time windows.
PG  - 18351
LID - 10.1038/s41598-023-45543-z [doi]
LID - 18351
AB  - This article constructed a vehicle scheduling problem (VSP) with soft time 
      windows for a certain ore company. VSP is a typical NP-hard problem whose optimal 
      solution can not be obtained in polynomial time, and the basic particle swarm 
      optimization(PSO) algorithm has the obvious shortcoming of premature convergence 
      and stagnation by falling into local optima. Thus, a modified particle swarm 
      optimization (MPSO) was proposed in this paper for the numerical calculation to 
      overcome the characteristics of the optimization problem such as: multiple 
      constraints and NP-hard. The algorithm introduced the "elite reverse" strategy 
      into population initialization, proposed an improved adaptive strategy by 
      combining the subtraction function and "ladder strategy" to adjust inertia 
      weight, and added a "jump out" mechanism to escape local optimal. Thus, the 
      proposed algorithm can realize an accurate and rapid solution of the algorithm's 
      global optimization. Finally, this article made typical benchmark functions 
      experiment and vehicle scheduling simulation to verify the algorithm performance. 
      The experimental results of typical benchmark functions proved that the search 
      accuracy and performance of the MPSO algorithm are superior to other algorithms: 
      the basic PSO, the improved particle swarm optimization (IPSO), and the chaotic 
      PSO (CPSO). Besides, the MPSO algorithm can improve an ore company's profit by 
      48.5-71.8% compared with the basic PSO in the vehicle scheduling simulation.
CI  - (c) 2023. The Author(s).
FAU - Qiao, Jinwei
AU  - Qiao J
AD  - School of Mechanical and Automotive Engineering, Qilu University of Technology 
      (Shandong Academy of Sciences), Jinan, 250353, People's Republic of China.
AD  - Shandong Institute of Mechanical Design and Research, Jinan, 250353, People's 
      Republic of China.
FAU - Li, Shuzan
AU  - Li S
AD  - School of Mechanical and Automotive Engineering, Qilu University of Technology 
      (Shandong Academy of Sciences), Jinan, 250353, People's Republic of China.
AD  - Shandong Institute of Mechanical Design and Research, Jinan, 250353, People's 
      Republic of China.
FAU - Liu, Ming
AU  - Liu M
AD  - School of Mechanical and Automotive Engineering, Qilu University of Technology 
      (Shandong Academy of Sciences), Jinan, 250353, People's Republic of China.
AD  - Shandong Institute of Mechanical Design and Research, Jinan, 250353, People's 
      Republic of China.
FAU - Yang, Zhi
AU  - Yang Z
AD  - School of Mechanical and Automotive Engineering, Qilu University of Technology 
      (Shandong Academy of Sciences), Jinan, 250353, People's Republic of China. 
      yangzhi@qlu.edu.cn.
AD  - Shandong Institute of Mechanical Design and Research, Jinan, 250353, People's 
      Republic of China. yangzhi@qlu.edu.cn.
FAU - Chen, Jun
AU  - Chen J
AD  - School of Mechanical and Automotive Engineering, Qilu University of Technology 
      (Shandong Academy of Sciences), Jinan, 250353, People's Republic of China.
AD  - Shandong Institute of Mechanical Design and Research, Jinan, 250353, People's 
      Republic of China.
FAU - Liu, Pengbo
AU  - Liu P
AD  - School of Mechanical and Automotive Engineering, Qilu University of Technology 
      (Shandong Academy of Sciences), Jinan, 250353, People's Republic of China.
AD  - Shandong Institute of Mechanical Design and Research, Jinan, 250353, People's 
      Republic of China.
FAU - Li, Huiling
AU  - Li H
AD  - Shandong Innovation and Development Research Institute, Jinan, 250353, People's 
      Republic of China.
FAU - Ma, Chi
AU  - Ma C
AD  - Zaozhuang Xinjinshan Intelligent Equipment Co., Ltd, Zaozhuang, 277400, People's 
      Republic of China.
LA  - eng
GR  - ZR2020ME116/Key Projects of Natural Science Foundation of Shandong Province/
GR  - 2022TSGC2051/The Innovation Ability Improvement Project for Technology-based 
      Small- and Medium-sized Enterprises of Shandong Province/
GR  - 2023TSGC0024/The Innovation Ability Improvement Project for Technology-based 
      Small- and Medium-sized Enterprises of Shandong Province/
GR  - 2023TSGC0931/The Innovation Ability Improvement Project for Technology-based 
      Small- and Medium-sized Enterprises of Shandong Province/
GR  - 2021CXGC010207/Key R\&D plan of Shandong Province, China/
PT  - Journal Article
DEP - 20231026
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
PMC - PMC10603129
COIS- The authors declare no competing interests.
EDAT- 2023/10/27 00:42
MHDA- 2023/10/27 00:43
PMCR- 2023/10/26
CRDT- 2023/10/26 23:33
PHST- 2023/08/13 00:00 [received]
PHST- 2023/10/20 00:00 [accepted]
PHST- 2023/10/27 00:43 [medline]
PHST- 2023/10/27 00:42 [pubmed]
PHST- 2023/10/26 23:33 [entrez]
PHST- 2023/10/26 00:00 [pmc-release]
AID - 10.1038/s41598-023-45543-z [pii]
AID - 45543 [pii]
AID - 10.1038/s41598-023-45543-z [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 26;13(1):18351. doi: 10.1038/s41598-023-45543-z.