Injection mold production sustainable scheduling using deep reinforcement learning

Seunghoon Lee, Yongju Cho, Young Hoon Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

Original languageEnglish
Article number8718
Pages (from-to)1-17
Number of pages17
JournalSustainability (Switzerland)
Volume12
Issue number20
DOIs
Publication statusPublished - 2020 Oct 2

Bibliographical note

Funding Information:
Funding: This work is supported by the regional industry base organization support project (P0001955, Support project to innovate IoT and big data-based mold manufacturing value chain), funded by the Ministry of Trade, Industry and Energy (MOTIE).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law

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