service operations

Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework. Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs. Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.

Existing process mining benchmarks provide only macro-level performance metrics (e.g., throughput time, completion rate), hindering identification of concrete improvement opportunities. To address this, we propose an executable process execution benchmarking method: it aligns event logs from the target organization and benchmark processes based on behavioral similarity, automatically identifying semantically equivalent and substitutable activity units; then constructs a joint feasibility–performance-impact assessment framework to generate ranked, evidence-driven process modification recommendations. This work pioneers the shift from descriptive benchmark analysis to prescriptive, “actionable” improvement guidance. Evaluated across multiple real-world process scenarios, our approach achieves an average throughput time reduction of 12.7%, significantly enhancing both the precision and implementability of process optimization.

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 work addresses the challenge of ensuring service continuity for multi-stage industrial workflows in B5G/6G networks, where conventional per-request QoS mechanisms fall short. To overcome this limitation, the authors propose a capability-aware collaborative planning framework that proactively exposes sustainable QoS capabilities within a finite network planning window. Industrial applications leverage this foresight to map workflow phases and submit demand trajectories, enabling workflow-level, forward-looking joint evaluation and dynamic coordination updates. By integrating network capability modeling, demand mapping, and adaptive coordination, the approach transcends traditional request-granularity constraints. Experimental validation on a real B5G system and large-scale simulations demonstrates that the proposed method significantly enhances service continuity, reduces request rejection rates, and substantially improves workflow completion rates under high network loads.

To address performance overhead escalation and transaction boundary degradation arising from process decomposition during monolith-to-microservices migration, this paper proposes a lightweight, trace-based what-if analysis method. The approach comprises three stages: execution trace collection and rewriting, performance-sensitive call-chain simulation, and abstract modeling of transaction boundaries—enabling rapid, quantitative assessment of non-functional property changes induced by service decomposition alternatives. Its core innovation lies in introducing the first trace-rewriting analysis paradigm prioritizing usability and speed, requiring neither source-code modification nor deployment in production-like environments. Evaluated on industrial case studies, the method completes each scenario assessment in seconds—achieving two orders-of-magnitude improvement in analysis efficiency—and thereby significantly facilitates high-frequency, low-friction iteration over service boundaries and informed trade-off decisions.

本文提出了一种服务健康工程方法，通过结合遥测、工作流完成情况等手段来检测分布式系统中的静默故障和异步工作停滞问题。

This study addresses a critical gap in geriatric care research, which has predominantly emphasized operational efficiency while lacking robust connections to clinical health outcomes. Through a systematic review of 30 interdisciplinary studies at the intersection of industrial engineering and operations research applied to elderly care, the work categorizes existing literature into three thematic domains: home-based medical care, polypharmacy management, and chronotherapeutic clinical scheduling. It proposes a novel conceptual framework that explicitly links operational optimization with clinical outcomes, advocating a paradigm shift from isolated task-level improvements toward integrated, multi-layer decision-making. By incorporating human-centered design, systems analysis, and emerging technologies such as digital twins and large language models, the study highlights the field’s current overemphasis on workforce scheduling at the expense of health impact, thereby laying a theoretical and technical foundation for intelligent care decision systems spanning hospital and community settings.

This study investigates how task-queue ordering rules, worker autonomy, and queue visibility influence performance and quality in service operations. Through a preregistered online experiment integrating dynamically arriving real-world order-picking tasks, behavioral measures, and personality assessments—while controlling for task complexity and arrival dynamics—the research demonstrates that mandating an “easiest-first” sequencing rule significantly enhances accuracy by mitigating workers’ self-selection bias. Conversely, granting workers autonomy over task sequencing reduces overall performance. Although task-arrival notifications trigger short-term efficiency spikes, long-term removal of queue information does not impair sustained performance. These findings underscore the critical role of queue design in human–algorithm collaboration and identify specific personality traits predictive of error propensity.

This study addresses the inefficiency in serverless platforms caused by complex, non-conservative information flows among functions. It introduces Hodge decomposition—a novel application in this domain—to construct a service topology model that decomposes observed operational flows into locally correctable components and globally persistent harmonic modes. The work demonstrates that harmonic flows are intrinsic structural characteristics of the system rather than artifacts of misconfiguration, and leverages this insight to propose new optimization mechanisms such as the “dumping effect.” By constructing service flow spectra and performing harmonic analysis, the approach effectively identifies architectural-level performance bottlenecks, thereby validating its efficacy in uncovering structural inefficiencies and guiding targeted performance optimizations.

This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.