Scholarlink

<p>Modern manufacturing requires scheduling methods that adapt to changing order arrivals, machine disruptions, customer priorities, stakeholder preferences, and time-varying energy conditions. This paper proposes a preference-conditioned deep reinforcement learning (DRL) approach for dynamic scheduling in sustainable and robust manufacturing. The approach is embedded in a cyber-physical production system (CPPS)-oriented framework that links production states, machine availability, energy-related background data, simulation-based learning, performance monitoring, and decision support. Within this framework, a Double Deep Q-Network (DDQN) scheduler is developed for joint job sequencing, machine assignment, and start-time adjustment. The scheduler uses a candidate-based state representation for dynamic order arrivals, vector-valued Q-output for objective-specific value estimation, and a priority- and preference-aware reward design. Customer priorities are treated as order-level attributes, while stakeholder preferences are encoded as system-level objective weightings. This enables one policy to consider energy-related cost, carbon emissions, energy demand, and tardiness while adapting to different preference profiles. The concept is demonstrated in an on-demand manufacturing (ODM)-oriented parallel CNC machining case with heterogeneous orders, product-specific setup and processing requirements, hourly electricity prices, carbon-intensity signals, and curriculum-adaptive machine breakdowns. DDQN is compared with three dispatching rules and two DRL baselines under shared training and testing scenarios. The results show that DDQN achieves the lowest energy-related cost and carbon emissions in training and unseen testing while maintaining acceptable delivery performance. Overall, the study demonstrates the potential of CPPS-oriented and preference-conditioned DRL for adaptive, energy-aware, and robust scheduling in smart manufacturing systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Preference-conditioned deep reinforcement learning for dynamic scheduling in sustainable and robust manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

查看全文
  • Chao Zhang,
  • Gabriela Ventura Silva,
  • Christoph Herrmann

摘要

Modern manufacturing requires scheduling methods that adapt to changing order arrivals, machine disruptions, customer priorities, stakeholder preferences, and time-varying energy conditions. This paper proposes a preference-conditioned deep reinforcement learning (DRL) approach for dynamic scheduling in sustainable and robust manufacturing. The approach is embedded in a cyber-physical production system (CPPS)-oriented framework that links production states, machine availability, energy-related background data, simulation-based learning, performance monitoring, and decision support. Within this framework, a Double Deep Q-Network (DDQN) scheduler is developed for joint job sequencing, machine assignment, and start-time adjustment. The scheduler uses a candidate-based state representation for dynamic order arrivals, vector-valued Q-output for objective-specific value estimation, and a priority- and preference-aware reward design. Customer priorities are treated as order-level attributes, while stakeholder preferences are encoded as system-level objective weightings. This enables one policy to consider energy-related cost, carbon emissions, energy demand, and tardiness while adapting to different preference profiles. The concept is demonstrated in an on-demand manufacturing (ODM)-oriented parallel CNC machining case with heterogeneous orders, product-specific setup and processing requirements, hourly electricity prices, carbon-intensity signals, and curriculum-adaptive machine breakdowns. DDQN is compared with three dispatching rules and two DRL baselines under shared training and testing scenarios. The results show that DDQN achieves the lowest energy-related cost and carbon emissions in training and unseen testing while maintaining acceptable delivery performance. Overall, the study demonstrates the potential of CPPS-oriented and preference-conditioned DRL for adaptive, energy-aware, and robust scheduling in smart manufacturing systems.