Deep Reinforcement Learning for Autonomous Industrial Resource Allocation in Semiconductor Manufacturing: Joint Optimization of Production Throughput, Resource Utilization, Operational Costs, and Profitability

  • Authors

    • Trang Huynh Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-V9I3P102

    Published 09-21-2026

  • Deep Reinforcement Learning, Semiconductor Manufacturing, Resource Allocation, Production Scheduling, Resource Utilization, Operational Cost, Profitability

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    Semiconductor manufacturing is characterized by complex re-entrant production flows, heterogeneous equipment, stochastic machine availability, variable processing times, and continuously changing production priorities, making efficient resource allocation a major operational challenge. This study develops a deep reinforcement learning (DRL) framework for autonomous industrial resource allocation in semiconductor manufacturing, with simultaneous optimization of production throughput, resource utilization, operational costs, and profitability. The proposed framework models the manufacturing system as a sequential decision-making environment in which an intelligent agent observes production states, including machine availability, queue conditions, work-in-process levels, equipment utilization, demand requirements, and operating costs, and subsequently selects appropriate resource-allocation actions. A multi-objective reward function is formulated to balance competing operational and economic objectives while penalizing excessive delays, idle capacity, and unnecessary expenditure. The proposed DRL approach is evaluated within a simulation-based semiconductor production environment and compared with conventional dispatching rules, heuristic scheduling methods, and reinforcement-learning baselines. Performance is assessed using throughput, machine utilization, cycle time, operational cost, profitability, and scheduling robustness under machine breakdowns, demand fluctuations, bottleneck congestion, maintenance events, and rush-order arrivals. The study provides a systematic framework for integrating intelligent production scheduling with economic decision-making and offers a foundation for adaptive, data-driven, and increasingly autonomous semiconductor manufacturing systems.

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