production systems

In cloud environments, selecting optimal data protection strategies for business continuity and disaster recovery remains challenging due to the lack of quantitative foundations for evaluating reliability and aligning with organizational Recovery Time Objectives (RTOs) and operational requirements. Method: This paper proposes an integrated assessment framework that synergistically combines system dynamics modeling and simulation-based optimization. It quantitatively evaluates key performance indicators—including recovery timeliness, data integrity, and system robustness—across public and hybrid cloud scenarios by simulating mainstream recovery mechanisms. Contribution/Results: The framework innovatively applies system dynamics to model time-varying dependencies during recovery processes and establishes interpretable, traceable mappings between policy parameters, technical metrics, and business objectives. Empirical validation demonstrates its reproducibility and practical utility, providing cloud-native organizations with a quantifiable, verifiable, and actionable decision-support methodology for data protection strategy selection.

OSCM faces challenges of theoretical fragmentation and operational complexity. This study develops the first encyclopedic, nonlinear knowledge organization framework for OSCM, integrating systematic literature review, multi-case comparative analysis, conceptual framework modeling, and practice mapping. It synthesizes dominant paradigms—including digital supply chains, lean management, and resilience-by-design—into a standardized, end-to-end value-chain knowledge system. Its contributions are threefold: (1) a unified, role- and context-agnostic OSCM knowledge graph; (2) structured coupling of theoretical rigor with industrial practice; and (3) support for nonlinear, scenario-driven knowledge retrieval. The framework has been adopted as core pedagogical material by multiple universities and enabled supply chain governance upgrades in three manufacturing and retail enterprises.

This study addresses the challenges of fragmented coordination and delayed responsiveness in retail supermarket supply chains, which stem from reliance on manual decision-making. To overcome these limitations, the authors propose Flowr, a novel framework that integrates a multi-agent architecture with a human-in-the-loop mechanism, enabling end-to-end automated decision-making. Flowr employs a central reasoning large language model (LLM) to orchestrate multiple domain-specific fine-tuned LLMs, while introducing a supervisable collaboration interface based on the Model Context Protocol (MCP) to ensure scalability and accountability. Empirical validation on a real-world large-scale supermarket chain demonstrates that Flowr significantly reduces manual coordination overhead, improves demand–supply matching accuracy, and supports proactive anomaly resolution. The framework exhibits strong potential for generalization across industries.

In semiconductor foundry manufacturing, demand exhibits strong intermittency, high skewness, and non-normality, rendering conventional safety stock calculations—predicated on normality assumptions—highly inaccurate. Method: This paper proposes a data-driven, nonparametric safety stock sizing method that employs kernel density estimation (KDE) to accurately model the empirical demand distribution, integrates predictive uncertainty quantification, and embeds the formulation within a linear optimization framework to yield an end-to-end decision-making pipeline. Contribution/Results: To our knowledge, this is the first work to jointly leverage KDE and predictive variability analysis for safety stock optimization. Validated through realistic replenishment simulations, the method achieves equivalent service levels while significantly reducing safety stock—thereby enhancing supply chain resilience and inventory efficiency.

To address fragmented forecasting strategies, weak data-driven capabilities, and the absence of decision闭环 in supply chain forecasting, this paper proposes a KPI-driven big-data prediction management framework. Methodologically, it integrates multi-source heterogeneous data collection, phantom inventory impact modeling, and hierarchical periodic forecasting strategies, coupled with XGBoost/LSTM ensembles, Bayesian hyperparameter optimization, and dynamic preprocessing. This enables a closed-loop workflow spanning problem identification, modeling, and feedback. The key contribution is a novel KPI-oriented paradigm that tightly couples preprocessing, prediction, and decision-making—marking the first integration of inventory, workforce, and capacity KPIs into both feature engineering and feedback-driven model refinement. Empirical results demonstrate an 18.3% average reduction in MAPE for mid-to-long-term demand forecasting, a 22% improvement in inventory turnover ratio, and a 35% reduction in planning response cycle time, significantly enhancing forecast transparency and operational decision agility.

This study addresses the challenge of integrated logistics and production scheduling in flexible, personalized pharmaceutical manufacturing by proposing a unified optimization framework that simultaneously considers bin packing, equipment layout, task scheduling, and path planning for automated production lines based on planar transport systems. The approach leverages drug co-occurrence patterns and Hamiltonian path-based neighborhood optimization to determine equipment placement, formulates bin packing and layout as a mixed-integer quadratic program, employs constraint programming for task scheduling, and generates conflict-free vehicle routes through directed acyclic graph reasoning coupled with iterative conflict resolution. Experimental results on 40 real-world prescriptions demonstrate that the system efficiently handles up to 500 daily orders across diverse facility layouts, achieving high performance while maintaining computational tractability.

This study addresses the scheduling challenge in manufacturing supply chains arising from the dynamic evolution of workforce qualifications—such as skill decay and training-induced labor unavailability—and their tight coupling with production, inventory, and training decisions. The work proposes the first closed-loop skill-constrained Model Predictive Controller (MPC) that explicitly models mechanisms of skill acquisition, maintenance, and expiration. At each shift, the controller solves a finite-horizon mixed-integer program encompassing production, inventory, backorders, and training, incorporating binary qualification states, hard eligibility constraints, and an interpretable terminal value function that quantifies future skill gaps. Rolling optimization is employed to implement only the first-period actions. In SkillChain-Gym simulations under multidimensional disturbances, the policy significantly outperforms static cross-training and reactive heuristics when skill or labor bottlenecks are foreseeable; however, lightweight static strategies remain competitive under sudden shocks or ample redundancy, indicating the approach’s advantage stems from proactive exploitation of qualification dynamics rather than mere adaptivity.

This study addresses the challenge of automating workflows in complex industries—such as logistics, healthcare, and construction—where processes are fragmented across heterogeneous tools and involve multi-party collaboration. The work proposes orchestration as a core abstraction to enable effective automation by dynamically coordinating multi-step tasks, enforcing domain-specific constraints, managing human approvals, and integrating legacy systems. It introduces the novel concept of “orchestration bottlenecks” and develops a theoretical framework that unifies multi-agent systems, workflow modeling, constraint reasoning, and human–AI collaboration, while exposing critical gaps in current multi-agent approaches at the orchestration level. Based on distinct sources of operational friction across domains, the paper advocates for targeted architectural safeguards—such as constraint enforcement or explainability—and phased implementation strategies to provide actionable pathways for automation in complex operational environments.

This study addresses coordination and temporal feasibility in supply chains under stringent time and resource constraints by proposing a modular modeling approach based on Product Timed Petri Nets (PTPNs). The method integrates subsystem behaviors through synchronized transition labels and, for the first time, explicitly models supply chain managers as critical shared mobile resources to capture their coupled influence on system temporal feasibility. This formulation enables systematic analysis of the interplay between timing constraints and managerial capacity, facilitating the identification of key configurations that lead to successful execution, deadline violations, or temporal deadlocks. The approach thus provides “what-if” decision support for designing effective supply chain coordination strategies under dynamic operational constraints.

Traditional visualizations of supply chain flows often suffer from visual clutter that obscures critical patterns. This work proposes a multi-scale semantic zooming framework that integrates skeleton-based edge bundling (SBEB), hexagonal density heatmaps, and hierarchical inventory sunburst charts to coherently represent aggregated flows, spatial densities, and inventory structures across macro, meso, and micro scales. The SBEB algorithm is enhanced with directional sector clustering and adaptive detour constraints to improve geographic plausibility. Implemented using Vue3 and Deck.gl, the system aggregates raw order data into 202 warehouse-to-state flow paths, substantially reducing visual complexity while enhancing actionable insights.