Search algorithm of the assembly sequence of products by using past learning results

Keijiro Watanabe, Shuhei Inada

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


In the future smart factory, the production system will further head in the direction of on-demand production. Products will be assembled one by one based on different specifications of customers. In these recognitions, this paper considers a method for raising productivity of the robot work cell. Under the assumption that the dual-arm robot assembles products in the work cell where one robot is in charge of all steps of assembling the product, we propose a computational algorithm for searching the efficient assembly sequence and work assignment to the robot hands utilizing reinforcement learning. Furthermore, we intend to use past learning results to determine work plans of robots more effectively. The proposed methods can eliminate or decrease the workload of the robot teaching. In addition, they can contribute to shorten the assembly time of products by giving the efficient work plan. In this research, the basic theory for automating the work planning of actual assembled products is considered using a building block model.

Original languageEnglish
Article number107615
JournalInternational Journal of Production Economics
Publication statusPublished - 2020 Aug


  • Assembly sequence
  • Disassembly sequence
  • Dual-arm robot
  • Neural-network
  • Q-learning
  • Reinforcement learning

ASJC Scopus subject areas

  • General Business,Management and Accounting
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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