Long-Term Rapid Scenario Planning in the Semiconductor Industry Using Deep Reinforcement Learning

The creation and maintenance of effective production plans is a central problem in supply chain planning. NXP Semiconductors N.V. uses a Mathematical Programming (MP) model to generate offline production plans on both short and long-term horizons. However, production plans need online updates in response to unforeseen events. This process is carried out manually, making the updated plans not optimal. This paper proposes the use of Deep Reinforcement Learning (DRL) as a method to produce close-to-optimal updated plans. DRL is suitable for large-scale problems and can produce close-to-optimal scheduling solutions quickly, whereas MP models ensure optimality at the expense of high computational complexity. The performance of DRL is compared against traditional optimization methods and a MP model using a simulation model that mimics NXP's situation. The results of this study highlight the usefulness of DRL as a tool for short and long-term decision-making in supply chain planning within the semiconductor industry.