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<p>This paper presents an online scheduling framework for a multi-compartment Automated Guided Vehicle (AGV) with optional onboard cobot, embedded in a Digital Twin environment that provides real-time machine-state data. The scheduler is a Genetic Algorithm (GA) with mission-index encoding and feasibility-by-construction initialization, generating valid sequences under capacity, traceability, and precedence constraints. To isolate the value of machine-state awareness, the GA operates in two modes: an RPT-blind baseline using logistics data only, and an RPT-aware extension that integrates machine Remaining Processing Time (RPT) into feasibility and fitness evaluation. Validation across eleven scenarios on a precision aerospace workshop reveals that the impact of RPT-awareness is regime-dependent. In non-saturated and capacity-constrained settings, readiness-driven synchronization reduces transport waste (Trip Time −34–51%, Empty Trip Time −54–70% vs. RPT-blind) but increases makespan by 5–11%, because conservative waiting can delay feeding of bottleneck resources. In coordination-sensitive and high-mix regimes, RPT-awareness improves both dimensions simultaneously, reducing makespan by up to 10% (810 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\rightarrow\)</EquationSource> </InlineEquation> 728&#xa0;min) alongside comparable logistics gains. Relative to the FIFO baseline representing current industrial practice, GA-based automation reduces Trip Time by 15–60% and Empty Trip Time by 15–77%. RPT-awareness also enables proactive cobot scheduling, completing all eligible inline missions (up to 29 per shift versus 4–9 in RPT-blind mode). The results indicate that RPT-aware scheduling is most effective where coordination complexity, rather than processing capacity, bounds system performance.</p>

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Online dynamic scheduling for multi-compartment AGV and cobot in manufacturing systems via remaining processing time integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Emma Salatiello,
  • Silvestro Vespoli,
  • Guido Guizzi,
  • Andrea Grassi

摘要

This paper presents an online scheduling framework for a multi-compartment Automated Guided Vehicle (AGV) with optional onboard cobot, embedded in a Digital Twin environment that provides real-time machine-state data. The scheduler is a Genetic Algorithm (GA) with mission-index encoding and feasibility-by-construction initialization, generating valid sequences under capacity, traceability, and precedence constraints. To isolate the value of machine-state awareness, the GA operates in two modes: an RPT-blind baseline using logistics data only, and an RPT-aware extension that integrates machine Remaining Processing Time (RPT) into feasibility and fitness evaluation. Validation across eleven scenarios on a precision aerospace workshop reveals that the impact of RPT-awareness is regime-dependent. In non-saturated and capacity-constrained settings, readiness-driven synchronization reduces transport waste (Trip Time −34–51%, Empty Trip Time −54–70% vs. RPT-blind) but increases makespan by 5–11%, because conservative waiting can delay feeding of bottleneck resources. In coordination-sensitive and high-mix regimes, RPT-awareness improves both dimensions simultaneously, reducing makespan by up to 10% (810 \(\rightarrow\) 728 min) alongside comparable logistics gains. Relative to the FIFO baseline representing current industrial practice, GA-based automation reduces Trip Time by 15–60% and Empty Trip Time by 15–77%. RPT-awareness also enables proactive cobot scheduling, completing all eligible inline missions (up to 29 per shift versus 4–9 in RPT-blind mode). The results indicate that RPT-aware scheduling is most effective where coordination complexity, rather than processing capacity, bounds system performance.