Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

Seunghoon Lee, Yongju Cho and Young Hoon Lee
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Seunghoon Lee: Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
Yongju Cho: Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Seobuk-gu, Cheonan-si 31056, Korea
Young Hoon Lee: Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea

Sustainability, 2020, vol. 12, issue 20, 1-17

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.

Keywords: deep reinforcement learning; sustainable scheduling; mold manufacturing; DQN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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