Stochastic Greedy Two-neighborhood Search Algorithm For Hybrid Disassembly Line Balancing Problem Considering Human Posture

Peng Ji, Xu Wang, Xi Wang Guo, Jiacun Wang, Shujin Qin, Ying Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the continuous development of manufacturing industry, many waste products are produced as a side effect. Recycling these waste products can significantly improve the utilization of resources and promote the green development of the environment. Establishing a manual disassembly line is one way to recycle scrap, but the mixture of multiple disassembly lines can be more effective. This paper proposes a hybrid disassembly line balance problem that takes human posture into consideration. This problem combines the U -shaped and linear disassembly line features. We establish a mathematical model to obtain maximum profit, and propose to use the stochastic greedy two-neighborhood search algorithm can solve this problem. The algorithm searches different neighborhoods to obtain the near-optimal solution. The experimental results show that the algorithm can get a good solution.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Cyber-Physical Social Intelligence, ICCSI 2022
EditorsXuemin Chen, Jun Wang, Jiacun Wang, Ying Tang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages350-355
Number of pages6
ISBN (Electronic)9781665498357
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022 - Nanjing, China
Duration: Nov 18 2022Nov 21 2022

Publication series

NameProceedings of the International Conference on Cyber-Physical Social Intelligence, ICCSI 2022

Conference

Conference2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022
Country/TerritoryChina
CityNanjing
Period11/18/2211/21/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Cognitive Neuroscience

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