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Product cooperative disassembly sequence and task planning based on genetic algorithm

  • Yongting TianEmail author
  • Xiufen ZhangEmail author
  • Zehua Liu
  • Xingyue Jiang
  • Junfang Xue
ORIGINAL ARTICLE
  • 122 Downloads

Abstract

To improve the disassembly efficiency, cooperative disassembly is essential for complex products. Operator task allocation is a significant challenge for cooperative disassembly sequence planning; however, it has not been studied previously to the best of our knowledge. To deal with this problem, a novel cooperative disassembly sequence and task planning (CDS&TP) method was proposed based on the genetic algorithm. A mathematical model of the CDS&TP problem was constructed based on the product’s disassembly hybrid graph model (DHGM). Focusing on the characteristics of the CDS&TP, a multi-layer chromosome coding method was proposed to describe the node layer and operator constraint layer. Thus, the initial population could be deduced through the node layer and operator constraints relationship rule model, which was proposed to construct the operator constraint and node layers. To obtain the task sequence of each operator and the disassembly time of each sequence, a chromosome fitness calculation formula was presented based on the disassembly set, which is the collection of all of the detachable nodes with a disassembly priority higher than the node. The chromosome evolution rules, such as the selection, crossover, and mutation operators, were redesigned to obtain the (approximate) optimal multiplayer cooperative disassembly sequences and task planning. Finally, a case study was proposed to validate the method.

Keywords

Disassembly sequence planning (DSP) Cooperative disassembly sequence and task planning (CDS&TP) Multi-layer encoding Genetic algorithm 

Notes

Acknowledgments

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding information

This research is supported by the National Natural Science Foundation of China (No. 51565044), the Inner Mongolia Natural Science Foundation of China (No. 2017MS (LH) 0510), and the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (NJYT-17-B08).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Mechanical EngineeringInner Mongolia University of TechnologyHohhotChina
  2. 2.College of Mechanical Engineering and Applied ElectronicsBeijing University of TechnologyBeijingChina

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