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.
Bibliographical noteFunding 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).
© 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