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.
Highlights
In Industry 4.0, manufacturing companies consider technical issues [1] in addition to economic, environmental, and social issues [2] for sustainability
This study developed a scheduling method that uses the deep Q-network (DQN) as the algorithm for deep reinforcement learning (RL), which is applied to the scheduling for mold manufacturing systems
Intelligent systems have been required to cope with the dynamic environment for complex production systems to enhance sustainability
Summary
In Industry 4.0, manufacturing companies consider technical issues [1] in addition to economic, environmental, and social issues [2] for sustainability. The demand and type of products are dependent on the customers, and satisfying the due date plays an important role in determining competitiveness in the mold industry. Developing effective and intelligent scheduling that can counteract flexibly while considering the products’ due date and the diverse product types in a dynamic environment is necessary for the mold industry. To address the complexity and dynamic environment of mold manufacturing, deep reinforcement learning (RL) is employed for the mold scheduling problem.
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