Pith. sign in

REVIEW 3 major objections 4 minor 168 references

Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Enactive AI claims reliable AI in complex systems comes from coupling organizational purpose with bounded operational state, not from model scale.

desk verdict A serious, honest conceptual framework for decision-centric AI; the load-bearing sufficiency condition is under-specified, so the central 'realization' claim is asserted rather than shown, but it earns referee time. read the letter →

arxiv 2608.03413 v1 pith:T5NUH7WA submitted 2026-08-04 cs.AI cs.ET

Zuojun Max Shen , Yuan Qu , Pujun Zhang , Anbang Liu , Yunhao Liang This is my paper
classification cs.AIcs.ET
keywords EnactiveAIDecisionIntelligenceComplexSystemsOrganizationalWorldSiteSchemaSufficiencySystem-GroundedForecasting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that as AI moves from generating outputs to taking consequential actions in enterprises and industrial systems, reliability depends on embedding AI in a decision architecture rather than on model scale alone. It proposes Enactive AI, an architecture in which an Organizational World (purpose, authority, trade-offs) and a Site World (bounded operational state, constraints, execution) are coupled by Schema Intelligence and kept in motion by an Enactive Decision Cycle. The claim is that this coupling delivers three capabilities: forecasts grounded in system structure, decisions grounded in feasible and authorized interventions, and evaluation grounded in the coupled system's response. Three industrial illustrations—e-commerce logistics, telecommunications supply, and semiconductor material handling—are offered as concrete settings where the architecture could be realized, with the strongest direct evidence coming from deployed forecasting and inventory systems.

What carries the argument

Schema Intelligence is the coupling mechanism: it maintains a decision-relevant topology structure of entities and typed relations, and instantiates each decision episode as a decision formulation—the operationally usable representation of a focal course of action linking objectives, states, constraints, authority, model interfaces, and feedback signals. The Site World grounds that formulation as a decision-sufficient state–action representation, the subset of operating facts needed to determine feasibility and consequences. The Enactive Decision Cycle is the temporal form that keeps intent framing, state grounding, action evaluation, operational enactment, and feedback learning connected. T

What would settle it

Run the AMHS evaluation the paper describes: compare the incumbent control policy, a strong dynamic-dispatching baseline, a Site-only configuration, and the full Enactive configuration on the same state data, action candidates, hard constraints, and computational budget over a bounded field pilot with washout periods. If the full Enactive configuration does not improve transport-related tool starvation, hot-lot tardiness, or cycle time relative to the Site-only configuration, the claimed value of organizational mappings and execution lineage is unsupported. More directly, identify one focal de

Watch

Extended reading notes

Core claim

Enactive AI claims that reliable AI decision-making in complex systems is a system property, not a model property. The three capability requirements—system-grounded forecasting, consequence-grounded decision-making, and system-wise judgement—are realized by the architecture as coupled decision functions: Schema Intelligence supplies the semantic and relational substrate; the Organizational World reasons over purpose and coordinated courses of action; the Site World grounds action in current operating conditions; and the Enactive Decision Cycle keeps these relations active across execution and feedback. The architecture's distinctive principles are selective coupling (only dependencies whose

Load-bearing premise

The load-bearing premise is decision sufficiency: for each focal decision there is a bounded representation of the system whose omission of non-material dependencies does not change action feasibility, expected consequences, or evaluation; if that fails, the Site World cannot be safely bounded without requiring the full digital replica the paper disavows.

Editorial extensions

If this is right

  • An AI system can participate in consequential decisions without a full digital replica of the system, because selective coupling bounds what must be represented.
  • Evaluation of AI should shift from predictive accuracy or benchmark scores to system-level criteria: objective alignment, feasibility preservation, consequence evaluability, release integrity, and execution fidelity.
  • Feedback from execution should revise the structural relations through which future decisions are formulated, not only model parameters.
  • Decision rights and authority conditions become part of the architecture, so a technically feasible action may be withheld or escalated for organizational reasons.
  • The same architecture can support routine decisions inside preauthorized envelopes while escalating exceptions that reveal stale assumptions or missing dependencies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If decision sufficiency can be formalized and tested, the framework becomes a design principle for when to extend or shrink a Site World, turning a conceptual boundary into an engineering criterion.
  • The proposed four-configuration comparison in the semiconductor case (incumbent, strong baseline, Site-only, full Enactive) is a transferable template for measuring whether organizational mappings and execution lineage add value beyond better prediction.
  • The framework implies a governance corollary the paper only gestures at: the same selective-coupling machinery that decides which dependencies are material could be used to certify that an AI-mediated decision was authorized, executed as commanded, and revised with traceable evidence.
  • If the architecture is right, model-centric AI evaluation in operational domains will increasingly be supplemented by decision-centric audits that trace objective-to-measure lineage and commanded-to-realized action.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper proposes Enactive AI, a conceptual architecture for AI-mediated decision-making in complex enterprise and industrial systems. The architecture comprises an Organizational World (strategic-institutional world model), a Site World (bounded operational world model), Schema Intelligence (semantic/topological coupling), and an Enactive Decision Cycle (five stages: intent framing, state grounding, action evaluation, operational enactment, feedback learning). The paper claims these components jointly realize system-grounded forecasting, consequence-grounded decision-making, and system-wise judgement (§3.7). Three industrial settings—JD.com supply chain, a telecommunications operator, and semiconductor AMHS—are presented as illustrations, with explicit caveats that full-architecture causal validation remains open.

Significance. If the architecture were formalized and validated, its decision-sufficient Site World could offer a useful bridge between LLM/agentic AI and system-level operations. The paper has notable strengths: it explicitly separates recommendation, approval, command, and realized action; it repeatedly hedges empirical claims; and Section 4.3 proposes a concrete, falsifiable evaluation design (four configurations, ablations, shadow operation, field pilot). However, the central 'realization' claim currently rests on an informal, circular decision-sufficiency principle and on retrospective alignment of prior component work. The paper's honesty about its empirical boundaries is a credit, but it does not by itself secure the central claim. A major revision is needed either to formalize the principle and its certification procedure or, if the contribution is intended as a research agenda, to state the architectural claims as hypotheses rather than established capabilities.

major comments (3)
  1. [§3.2, §3.4, §3.7] The decision-sufficiency principle is unfalsifiable as stated. Selective coupling is defined as retaining dependencies 'whose omission could change ... feasibility, admissibility, consequences, or evaluation' (§3.2), and decision sufficiency is defined as retaining distinctions whose omission could change 'technical feasibility, material consequences, risk, or comparison' (§3.4). 'Material' is thus defined via the full downstream consequences the architecture is supposed to predict. With partial observation and delayed endogenous feedback, no local certificate of non-materiality is derivable; any Site World boundary can be justified post hoc. Consequently, the §3.7 claim that the three capabilities are 'realized' is a definitional restatement. The paper must provide an operational formalization—e.g., a counterfactual sensitivity/verification procedure—or explicitly present the realizatio
  2. [§4.1–4.3] The illustrations do not validate the architecture. The JD.com outcomes (33.21% forecast improvement, $6.13M and $22.32M savings, 26.1%/51.7%/40.4% inventory-cost reductions, 0.85/2.19 percentage-point service gains) come from specific component models and field experiments; nothing shows these were caused by, or even instantiated, the Enactive AI architecture as defined. The paper's own caveats are decisive: §4.1 states full causal validation 'remains an open empirical task,' §4.3 calls the AMHS evidence 'model-contingent' and 'not field validation of the complete Enactive AI architecture,' and the telecom case (§4.2) has no empirical results. The abstract's claim of 'practical value' should be softened accordingly.
  3. [§3.5, §5.1.3] The feedback-revision mechanism is asserted without a stability condition. The cycle claims that execution feedback can 'selectively revise' the relational structure of the world models, and §5.1.3 promises adaptive resilience. But in the delayed, endogenous settings the paper itself highlights, an observed discrepancy can be caused by noise, model error, execution deviation, or a changed dependency; the paper acknowledges that lineage alone does not establish causality (§3.2) yet gives no procedure for deciding when to update structure versus parameters versus escalate. Without such a procedure, the 'self-evolving' loop is an aspiration, and the cycle's revision capability is untestable. The paper should add a concrete decision rule or clearly label this as future work rather than part of the realized architecture.
minor comments (4)
  1. [References] Bertsimas and Kallus appears twice as identical entries (2020a and 2020b); Shen et al. 2025a and 2025b are the same paper. These duplicates should be merged.
  2. [Figures] Figure 1 is not explicitly referenced in the text; check figure numbering around Sections 1 and 3.1.
  3. [Prose] The abstract's 'the power of AI is not verified under these real-world complex systems' and Section 2.1's 'road to the next frontier ... is crumpled' are awkward and should be rewritten.
  4. [§4.1] The INFORMS awards and recognitions are not evidence for the architecture itself. They could be moved to acknowledgments or omitted from the technical narrative.

Circularity Check

2 steps flagged · score 6.0 of 10

The core 'realization' claim and the decision-sufficiency principle are definitionally self-supporting; empirical validation is explicitly deferred, so the paper is a plausible but unsecured conceptual framework.

  1. self definitional [Sections 3.2 and 3.4 (selective coupling; decision sufficiency)]
    "A central design principle is decision sufficiency. The Site World retains the distinctions whose omission could change the technical feasibility, material consequences, risk, or comparison of candidate actions under the focal objective. The Site World’s basic unit is a decision-sufficient state–action representation: the subset of state variables, resources, constraints, candidate actions, consequence estimates, and execution signals needed to determine whether the focal course of action can be realized and how it can be connected to execution."

    Decision sufficiency is defined as retaining exactly those distinctions whose omission could change feasibility, consequences, risk, or comparison. The Site World is then defined as the subset 'needed to determine whether the focal course of action can be realized.' So the claim that a bounded Site World preserves decision relevance is true by definition of 'decision-sufficient.' Selective coupling (§3.2) has the same form: it is defined as representing dependencies whose omission could change feasibility/admissibility/consequences/evaluation. No procedure is given for identifying which dependencies are material, and the paper's own complex-systems premises (partial observation, delayed feedback) make local certification non-trivial. Any boundary can be rationalized post hoc, so this load-

  2. self definitional [Section 3.7, 'Realization of Core Capabilities']
    "The three capability requirements identified earlier are realized by the architecture as coupled decision functions rather than as isolated model properties. ... System-grounded forecasting. In Enactive AI, forecasting is a coupled projection of how a course of action may propagate through the system."

    The §3.7 'realization' of the three capabilities is a restatement of the components' definitions. 'System-grounded forecasting' is stipulated to be what Organizational World/Site World/Schema Intelligence jointly do; 'consequence-grounded decision-making' and 'system-wise judgement' are described as the same component roles. No independent test distinguishes 'realized' from 'asserted.' The paper's own limits support this: §4.1 says 'causal validation of the complete Enactive AI framework in its full generality remains an open empirical task,' and §4.3 says the AMHS artifacts are 'not field validation of the complete Enactive AI architecture.' Thus the central realization claim holds by definition, not by demonstration.

full rationale

This is a conceptual architecture paper with no equation-level derivation, so the strongest circularity is definitional rather than statistical. The load-bearing guarantee—that a bounded Site World plus selective coupling preserves decision relevance—is built into the definitions: a site is decision-sufficient if it contains what the focal decision needs, and dependencies are selectively coupled if omitting them would change the decision. The paper provides no operational procedure to certify non-materiality, so the principle cannot fail, and any chosen boundary can be defended post hoc. Section 3.7 then presents the three capability requirements as 'realized' by these components; that realization is a paraphrase of the roles assigned to the components, not a falsifiable result. I weighed the paper's explicit disclaimers (§4.1: 'causal validation ... remains an open empirical task'; §4.3: artifacts are 'not field validation of the complete Enactive AI architecture') and its heavy reliance on the authors' own prior works as illustrations rather than as loading-bearing proof. The JD.com, telecom, and AMHS discussions are presented as illustrations/existence proofs, and the underlying JD.com results are externally published field studies; consequently I do not treat the self-citations as the main circularity. The central conceptual claims, however, are definitionally self-supported, giving a score of 6 rather than 0-2. The paper has independent organizing content and an honest research agenda, so 8-10 would overstate the circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 4 invented entities

The framework rests on conceptual components rather than fitted numerical parameters, so the free parameter list is empty. The axioms capture the domain assumptions and the paper-specific design principles that must hold for the architecture to function. The four named components are invented conceptual entities with no independent evidence beyond the paper's own retrospective case-study mapping.

assumptions (5)
  • domain assumption Complex organizational and operational settings are complex systems exhibiting interdependence, nonlinearity, emergence, and feedback.
    Adopted from the systems literature reviewed in Section 2.2; this is the foundation for the entire framework. If false, the problem the paper addresses is moot.
  • ad hoc to paper A decision-sufficient representation can be defined per focal decision, omitting non-material dependencies without changing feasibility, consequences, or evaluation.
    Stated as the design principle of the Site World and Schema Intelligence in Sections 3.2 and 3.4. It is load-bearing and not formally defined or proven.
  • domain assumption Organizational purpose, authority, commitments, and trade-offs can be represented as a world model that AI can reason over.
    Assumed throughout Section 3.3. No evidence is presented that managerial semantics can be formalized to the degree the architecture requires.
  • ad hoc to paper Execution feedback can selectively revise the relational structure of the world models, and this revision remains stable and governed.
    Central to the Enactive Decision Cycle (Sections 3.5 and 3.6); treated as achievable but no stability, convergence, or governance analysis is provided.
  • domain assumption Organizational and operational systems are sufficiently near-decomposable that selective coupling does not omit decision-relevant dependencies.
    Invoked in Section 3.2 via Simon (1962); the paper assumes real systems satisfy this property in practice.
invented entities (4)
  • Organizational World
    purpose: A world model representing enterprise intent, objectives, policies, authority, commitments, and managerial semantics.
    New named component; supported only by the paper's retrospective alignment of the authors' prior work, with no independent empirical handle.
  • Site World
    purpose: A bounded operational world model representing physical state, resources, constraints, executable actions, and execution feedback.
    New named component; its boundary depends on the unproven decision-sufficiency assumption.
  • Schema Intelligence
    purpose: Coupling mechanism that translates organizational intent into decision formulations and returns operational evidence to organizational meaning.
    New named component; described as an inference and mapping layer but not implemented or independently validated in this paper.
  • Enactive Decision Cycle
    purpose: Temporal loop sustaining framing, grounding, evaluation, enactment, and feedback revision across decision episodes.
    New naming for a decision cycle; closely resembles established cycles such as OODA and PDCA, which the paper does not cite.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems." pith.science (2026). https://pith.science/paper/T5NUH7WA

@misc{pith2026260803413,
  author       = {Pith},
  title        = {Pith review of: Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T5NUH7WA}},
  note         = {Machine review of arXiv:2608.03413}
}
read the original abstract

As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems. However, the power of AI is not verified under these real-world complex systems for various reasons, considering reliability, feasibility, resilience, and responsibility requirements in real commercial and industrial operations. This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback. Four complementary roles organize the framework: an Organizational World defines operations management logic and an organizational behavior world model behind an enterprise from a strategic-institutional horizon; a Site World defines a physically bounded industrial optimization and execution world model from an operational-realization horizon; Schema Intelligence provides the coupling mechanism between two world models to weave various AI applications via two models; and Enactive Decision Cycle triggers the self-evolving dynamic process to update and audit the entire framework. By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment. Enactive AI points toward a future in which AI progress is measured not only by what models can generate or automate, but by how reliably intelligent systems can support consequential action, responsible governance, and durable social value in the complex systems that shape modern life, which we believe will define the next frontier of AI research for enterprise-level and industrial complex systems.

Figures

Figures reproduced from arXiv: 2608.03413 by the authors.

Figure 1
Figure 1. Enactive AI Architecture is measured not only by what AI can generate or predict, but by the reliability of situated action: whether AI can preserve meaning, feasibility, accountability, and adaptation as decisions unfold through complex systems. The contributions of this paper can be summarized as: • First, we point out AI-mediated decision-making in complex systems as a frontier problem that follows from AI’s move… view at source ↗
Figure 2
Figure 2. Enactive AI as an operational decision system. Panel (a) distinguishes organizational [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Schema Intelligence as system topology modeling for operational decision formulation. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

168 extracted references · 59 canonical work pages

  1. [1]

    , title =

    Sutton, Richard S. , title =. ACM SIGART Bulletin , volume =. 1991 , doi =

    1991

  2. [2]

    and Thompson, Evan and Rosch, Eleanor , title =

    Varela, Francisco J. and Thompson, Evan and Rosch, Eleanor , title =. 1991 , isbn =

    1991

  3. [3]

    Artificial Intelligence , volume =

    Froese, Tom and Ziemke, Tom , title =. Artificial Intelligence , volume =. 2009 , doi =

    2009

  4. [4]

    , title =

    Rafiee, Banafsheh and Sutton, Richard S. , title =. arXiv preprint arXiv:2605.24238 , year =. 2605.24238 , archiveprefix =

    arXiv

  5. [5]

    Recurrent World Models Facilitate Policy Evolution , booktitle =

    Ha, David and Schmidhuber, J. Recurrent World Models Facilitate Policy Evolution , booktitle =. 2018 , eprint =

    2018

  6. [6]

    Nature , volume =

    Schrittwieser, Julian and Antonoglou, Ioannis and Hubert, Thomas and Simonyan, Karen and Sifre, Laurent and Schmitt, Simon and Guez, Arthur and Lockhart, Edward and Hassabis, Demis and Graepel, Thore and Lillicrap, Timothy and Silver, David , title =. Nature , volume =. 2020 , doi =

    2020

  7. [7]

    2022 , note =

    LeCun, Yann , title =. 2022 , note =

    2022

  8. [8]

    Business World Model

    Pang, Cecil and Sayama, Hiroki , title =. arXiv preprint arXiv:2606.10044 , year =. 2606.10044 , archiveprefix =

    work page Pith review arXiv

Show all 168 references
  1. [9]

    arXiv preprint arXiv:2604.01359 , year =

    Mantsivoda, Andrei and Gavrilina, Darya , title =. arXiv preprint arXiv:2604.01359 , year =. doi:10.48550/arXiv.2604.01359 , url =. 2604.01359 , archiveprefix =

    doi

  2. [10]

    arXiv preprint arXiv:2605.12178 , year =

    Nair, Jishnu Sethumadhavan and Bechard, Patrice and Maheshwary, Rishabh and Dasgupta, Surajit and Ramachandran, Sravan and Bhagat, Aakash and Radhakrishna, Shruthan and Pattnaik, Pulkit and Obando-Ceron, Johan and Malay, Shiva Krishna Reddy and Davasam, Sagar and Subramanian, ...

    doi arXiv

  3. [11]

    A Context Engineering Framework for Improving Enterprise

    Yang, Xi and Lozano, Aur. A Context Engineering Framework for Improving Enterprise. arXiv preprint arXiv:2603.22083 , year =. doi:10.48550/arXiv.2603.22083 , url =. 2603.22083 , archiveprefix =

    doi

  4. [12]

    and Grigas, Paul , title =

    Elmachtoub, Adam N. and Grigas, Paul , title =. Management Science , volume =. 2022 , doi =

    2022

  5. [13]

    and Amos, Brandon and Kolter, J

    Donti, Priya L. and Amos, Brandon and Kolter, J. Zico , title =. Advances in Neural Information Processing Systems , volume =. 2017 , url =

    2017

  6. [14]

    2012 , month = dec, url =

    2012

  7. [15]

    Semantic Sensor Network Ontology , institution =

    Haller, Armin and Janowicz, Krzysztof and Cox, Simon and Le Phuoc, Danh and Taylor, Kerry and Lefran. Semantic Sensor Network Ontology , institution =. 2017 , month = oct, url =

    2017

  8. [16]

    IFAC-PapersOnLine , volume =

    Kritzinger, Werner and Karner, Matthias and Traar, Georg and Henjes, Jan and Sihn, Wilfried , title =. IFAC-PapersOnLine , volume =. 2018 , doi =

    2018

  9. [17]

    Manufacturing Letters , volume =

    Lee, Jay and Bagheri, Behrad and Kao, Hung-An , title =. Manufacturing Letters , volume =. 2015 , doi =

    2015

  10. [18]

    Cyber-Physical Systems in Manufacturing , journal =

    Monostori, L. Cyber-Physical Systems in Manufacturing , journal =. 2016 , doi =

    2016

  11. [19]

    The International Journal of Advanced Manufacturing Technology , volume =

    Fischbach, Andreas and Strohschein, Jan and Bunte, Andreas and Stork, J. The International Journal of Advanced Manufacturing Technology , volume =. 2020 , doi =

    2020

  12. [20]

    International Conference on Learning Representations , year =

    Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan , title =. International Conference on Learning Representations , year =. 2210.03629 , archiveprefix =

    arXiv

  13. [21]

    and Burger, Doug and Wang, Chi , title =

    Wu, Qingyun and Bansal, Gagan and Zhang, Jieyu and Wu, Yiran and Li, Beibin and Zhu, Erkang and Jiang, Li and Zhang, Xiaoyun and Zhang, Shaokun and Liu, Jiale and Awadallah, Ahmed Hassan and White, Ryen W. and Burger, Doug and Wang, Chi , title =. arXiv preprint arXiv:2308.081...

    arXiv

  14. [22]

    Proceedings of the 6th Conference on Robot Learning , series =

    Ichter, Brian and Brohan, Anthony and Chebotar, Yevgen and others , title =. Proceedings of the 6th Conference on Robot Learning , series =. 2023 , url =

    2023

  15. [23]

    Advances in Neural Information Processing Systems , volume =

    Janner, Michael and Fu, Justin and Zhang, Marvin and Levine, Sergey , title =. Advances in Neural Information Processing Systems , volume =. 2019 , eprint =

    2019

  16. [24]

    Proceedings of the 34th International Conference on Machine Learning , series =

    Achiam, Joshua and Held, David and Tamar, Aviv and Abbeel, Pieter , title =. Proceedings of the 34th International Conference on Machine Learning , series =. 2017 , url =

    2017

  17. [25]

    Safe Reinforcement Learning via Shielding , booktitle =

    Alshiekh, Mohammed and Bloem, Roderick and Ehlers, R. Safe Reinforcement Learning via Shielding , booktitle =. 2018 , doi =

    2018

  18. [26]

    Artificial Intelligence , volume =

    Givan, Robert and Dean, Thomas and Greig, Matthew , title =. Artificial Intelligence , volume =. 2003 , doi =

    2003

  19. [27]

    Science , volume =

    Machine Learning: Trends, Perspectives, and Prospects , author =. Science , volume =. 2015 , doi =

    2015

  20. [28]

    Nature , volume =

    Deep Learning , author =. Nature , volume =. 2015 , doi =

    2015

  21. [29]

    Foundations and Trends in Information Retrieval , volume =

    Learning to Rank for Information Retrieval , author =. Foundations and Trends in Information Retrieval , volume =. 2009 , doi =

    2009

  22. [30]

    Nature , volume =

    Human-Level Control through Deep Reinforcement Learning , author =. Nature , volume =. 2015 , doi =

    2015

  23. [31]

    arXiv preprint arXiv:1706.03762 , year =

    Attention Is All You Need , author =. arXiv preprint arXiv:1706.03762 , year =. doi:10.48550/arXiv.1706.03762 , url =

    doi arXiv

  24. [32]

    Advances in Neural Information Processing Systems , volume =

    Language Models are Few-Shot Learners , author =. Advances in Neural Information Processing Systems , volume =. 2020 , doi =

    2020

  25. [33]

    arXiv preprint arXiv:2108.07258 , year =

    On the Opportunities and Risks of Foundation Models , author =. arXiv preprint arXiv:2108.07258 , year =. doi:10.48550/arXiv.2108.07258 , url =

    doi arXiv

  26. [34]

    Advances in Neural Information Processing Systems , volume =

    Training Language Models to Follow Instructions with Human Feedback , author =. Advances in Neural Information Processing Systems , volume =. 2022 , doi =

    2022

  27. [35]

    arXiv preprint arXiv:2303.08774 , year =

    GPT-4 Technical Report , author =. arXiv preprint arXiv:2303.08774 , year =. doi:10.48550/arXiv.2303.08774 , url =

    doi arXiv

  28. [36]

    arXiv preprint arXiv:2302.04761 , year =

    Toolformer: Language Models Can Teach Themselves to Use Tools , author =. arXiv preprint arXiv:2302.04761 , year =. doi:10.48550/arXiv.2302.04761 , url =

    doi arXiv

  29. [37]

    Frontiers of Computer Science , volume =

    A Survey on Large Language Model Based Autonomous Agents , author =. Frontiers of Computer Science , volume =. 2024 , doi =

    2024

  30. [38]

    2026 , howpublished =

    2026

  31. [39]

    Proceedings of the American Philosophical Society , volume =

    The Architecture of Complexity , author =. Proceedings of the American Philosophical Society , volume =. 1962 , url =

    1962

  32. [40]

    Science Advances , volume =

    The Accuracy, Fairness, and Limits of Predicting Recidivism , author =. Science Advances , volume =. 2018 , doi =

    2018

  33. [41]

    2023 , url =

    Report of the Royal Commission into the Robodebt Scheme , author =. 2023 , url =

    2023

  34. [42]

    Journal of Information Technology Teaching Cases , volume =

    Managing Unintended Consequences of Algorithmic Decision-Making: The Case of Robodebt , author =. Journal of Information Technology Teaching Cases , volume =. 2024 , doi =

    2024

  35. [43]

    Science , volume =

    The Parable of Google Flu: Traps in Big Data Analysis , author =. Science , volume =. 2014 , doi =

    2014

  36. [44]

    JAMA Internal Medicine , volume =

    External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients , author =. JAMA Internal Medicine , volume =. 2021 , doi =

    2021

  37. [45]

    2019 , url =

    Collision Between Vehicle Controlled by Developmental Automated Driving System and Pedestrian, Tempe, Arizona, March 18, 2018 , author =. 2019 , url =

    2018

  38. [46]

    Harvard Business Review , volume =

    The Triple-A Supply Chain , author =. Harvard Business Review , volume =. 2004 , url =

    2004

  39. [47]

    International Journal of Physical Distribution & Logistics Management , volume =

    Ericsson's Proactive Supply Chain Risk Management Approach after a Serious Sub-Supplier Accident , author =. International Journal of Physical Distribution & Logistics Management , volume =. 2004 , doi =

    2004

  40. [48]

    MIT Sloan Management Review , volume =

    Managing Risk to Avoid Supply-Chain Breakdown , author =. MIT Sloan Management Review , volume =. 2004 , url =

    2004

  41. [49]

    MIT Sloan Management Review , volume =

    The Toyota Group and the Aisin Fire , author =. MIT Sloan Management Review , volume =. 1998 , url =

    1998

  42. [50]

    International Journal of Production Economics , volume =

    Implications of the Tohoku Earthquake for Toyota's Coordination Mechanism: Supply Chain Disruption of Automotive Semiconductors , author =. International Journal of Production Economics , volume =. 2015 , doi =

    2015

  43. [51]

    International Journal of Production Economics , volume =

    Perspectives in Supply Chain Risk Management , author =. International Journal of Production Economics , volume =. 2006 , doi =

    2006

  44. [52]

    A Position Paper Motivated by COVID-19 Outbreak , author =

    Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. A Position Paper Motivated by COVID-19 Outbreak , author =. International Journal of Production Research , volume =. 2020 , doi =

    2020

  45. [53]

    Manufacturing & Service Operations Management , volume =

    OM Forum: Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community , author =. Manufacturing & Service Operations Management , volume =. 2026 , doi =

    2026

  46. [54]

    ACM Computing Surveys , volume =

    Survey of Hallucination in Natural Language Generation , author =. ACM Computing Surveys , volume =. 2023 , doi =

    2023

  47. [55]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =

    On Faithfulness and Factuality in Abstractive Summarization , author =. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =. 2020 , doi =

    2020

  48. [56]

    Management Science , volume =

    From Predictive to Prescriptive Analytics , author =. Management Science , volume =. 2020 , doi =

    2020

  49. [57]

    Advances in Neural Information Processing Systems , volume =

    Task-Based End-to-End Model Learning in Stochastic Optimization , author =. Advances in Neural Information Processing Systems , volume =. 2017 , url =

    2017

  50. [58]

    arXiv preprint arXiv:1803.10122 , year =

    World Models , author =. arXiv preprint arXiv:1803.10122 , year =. doi:10.48550/arXiv.1803.10122 , url =

    doi arXiv

  51. [59]

    International Conference on Learning Representations , year =

    Dream to Control: Learning Behaviors by Latent Imagination , author =. International Conference on Learning Representations , year =. doi:10.48550/arXiv.1912.01603 , url =

    1912 doi arXiv

  52. [60]

    arXiv preprint arXiv:2301.04104 , year =

    Mastering Diverse Domains through World Models , author =. arXiv preprint arXiv:2301.04104 , year =. doi:10.48550/arXiv.2301.04104 , url =

    doi arXiv

  53. [61]

    Artificial Intelligence , volume =

    Intelligence without Representation , author =. Artificial Intelligence , volume =. 1991 , doi =

    1991

  54. [62]

    Management Science , volume =

    A Practical End-to-End Inventory Management Model with Deep Learning , author =. Management Science , volume =. 2023 , doi =

    2023

  55. [63]

    arXiv preprint arXiv:2501.15942 , year =

    TimeHF: Billion-Scale Time Series Models Guided by Human Feedback , author =. arXiv preprint arXiv:2501.15942 , year =. doi:10.48550/arXiv.2501.15942 , url =

    doi arXiv

  56. [64]

    arXiv preprint arXiv:2512.19001 , year =

    ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management , author =. arXiv preprint arXiv:2512.19001 , year =. doi:10.48550/arXiv.2512.19001 , url =

    doi

  57. [65]

    arXiv preprint arXiv:2606.29366 , year =

    Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models , author =. arXiv preprint arXiv:2606.29366 , year =. doi:10.48550/arXiv.2606.29366 , url =

    doi arXiv

  58. [66]

    arXiv preprint arXiv:2509.03811 , year =

    Rethinking Supply Chain Planning: A Generative Paradigm , author =. arXiv preprint arXiv:2509.03811 , year =. doi:10.48550/arXiv.2509.03811 , url =

    doi

  59. [67]

    INFORMS Journal on Applied Analytics , volume =

    Supercharged by Advanced Analytics, JD.com Attains Agility, Resilience, and Shared Value Across Its Supply Chain , author =. INFORMS Journal on Applied Analytics , volume =. 2024 , doi =

    2024

  60. [68]

    INFORMS Journal on Applied Analytics , volume =

    JD.com Improves Fulfillment Efficiency with Data-Driven Integrated Assortment Planning and Inventory Allocation , author =. INFORMS Journal on Applied Analytics , volume =. 2025 , doi =

    2025

  61. [69]

    European Journal for Philosophy of Science , volume =

    What Is a Complex System? , author =. European Journal for Philosophy of Science , volume =. 2013 , doi =

    2013

  62. [70]

    Nature , volume =

    Globally Networked Risks and How to Respond , author =. Nature , volume =. 2013 , doi =

    2013

  63. [71]

    Nature , volume =

    Machine Behaviour , author =. Nature , volume =. 2019 , doi =

    2019

  64. [72]

    Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =

    Fairness and Abstraction in Sociotechnical Systems , author =. Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =. 2019 , doi =

    2019

  65. [73]

    Management Science , volume =

    Bertsimas, Dimitris and Kallus, Nathan , title =. Management Science , volume =. 2020 , doi =

    2020

  66. [74]

    , title =

    Galbraith, Jay R. , title =. Interfaces , volume =. 1974 , doi =

    1974

  67. [75]

    Journal of Political Economy , volume =

    Aghion, Philippe and Tirole, Jean , title =. Journal of Political Economy , volume =. 1997 , doi =

    1997

  68. [76]

    , title =

    Sanchez, Ron and Mahoney, Joseph T. , title =. Strategic Management Journal , volume =. 1996 , doi =

    1996

  69. [77]

    , title =

    Sterman, John D. , title =. System Dynamics Review , volume =. 1994 , doi =

    1994

  70. [78]

    Knowledge Graphs , journal =

    Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and de Melo, Gerard and Gutierrez, Claudio and Kirrane, Sabrina and Labra Gayo, Jos. Knowledge Graphs , journal =. 2020 , doi =

    2020

  71. [79]

    Retrieval-Augmented Generation for Knowledge-Intensive

    Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K. Retrieval-Augmented Generation for Knowledge-Intensive. Advances in Neural Information Processing Systems , volume =

  72. [80]

    and Boley, Harold and Tabet, Said and Grosof, Benjamin and Dean, Mike , title =

    Horrocks, Ian and Patel-Schneider, Peter F. and Boley, Harold and Tabet, Said and Grosof, Benjamin and Dean, Mike , title =. 2004 , month = may, url =

    2004

  73. [81]

    Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting , journal =

    Lim, Bryan and Ar. Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting , journal =. 2021 , doi =

    2021

  74. [82]

    , title =

    Law, Averill M. , title =. 2015 , isbn =

    2015

  75. [83]

    Ontology Matching , edition =

    Euzenat, J. Ontology Matching , edition =. 2013 , doi =

    2013

  76. [84]

    Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems , pages =

    Lenzerini, Maurizio , title =. Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems , pages =. 2002 , publisher =

    2002

  77. [85]

    van der Aalst, Wil M. P. , title =. 2016 , doi =

    2016

  78. [86]

    Proceedings of the 41st International Conference on Machine Learning , series =

    Ahmaditeshnizi, Ali and Gao, Wenzhi and Udell, Madeleine , title =. Proceedings of the 41st International Conference on Machine Learning , series =. 2024 , publisher =

    2024

  79. [87]

    2025 , eprint =

    Cosmos World Foundation Model Platform for Physical. 2025 , eprint =

    2025

  80. [88]

    Advances in neural information processing systems , volume=

    Reflexion: Language agents with verbal reinforcement learning , author=. Advances in neural information processing systems , volume=

  81. [89]

    First conference on language modeling , year=

    Autogen: Enabling next-gen LLM applications via multi-agent conversations , author=. First conference on language modeling , year=

  82. [90]

    arXiv preprint arXiv:2303.17760 , year=

    Camel: Communicative agents for" mind" exploration of large language model society , author=. arXiv preprint arXiv:2303.17760 , year=

    arXiv

  83. [91]

    Communications of the ACM , volume=

    Generality in artificial intelligence , author=. Communications of the ACM , volume=. 1987 , publisher=

    1987

  84. [92]

    1984 , publisher=

    The fifth generation , author=. 1984 , publisher=

    1984

  85. [93]

    Communications of the ACM , volume=

    Computer science as empirical inquiry: Symbols and search , author=. Communications of the ACM , volume=. 1976 , publisher=

    1976

  86. [94]

    AI magazine , volume=

    Does machine learning really work? , author=. AI magazine , volume=

  87. [95]

    Advances in neural information processing systems , volume=

    Imagenet classification with deep convolutional neural networks , author=. Advances in neural information processing systems , volume=

  88. [96]

    nature , volume=

    Human-level control through deep reinforcement learning , author=. nature , volume=. 2015 , publisher=

    2015

  89. [97]

    Bert: Pre-training of deep bidirectional transformers for language understanding , author=. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) , pages=

    2019

  90. [98]

    Advances in Neural Information Processing Systems , volume=

    Webshop: Towards scalable real-world web interaction with grounded language agents , author=. Advances in Neural Information Processing Systems , volume=

  91. [99]

    The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

    Swe-agent: Agent-computer interfaces enable automated software engineering , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

  92. [100]

    2006 , publisher=

    Pattern recognition and machine learning , author=. 2006 , publisher=

    2006

  93. [101]

    American Scientist , volume =

    Science and Complexity , author =. American Scientist , volume =. 1948 , url =

    1948

  94. [102]

    Cybernetics: Or Control and Communication in the Animal and the Machine , author =

  95. [103]

    1956 , url =

    An Introduction to Cybernetics , author =. 1956 , url =

    1956

  96. [104]

    1968 , isbn =

    General System Theory: Foundations, Development, Applications , author =. 1968 , isbn =

    1968

  97. [105]

    arXiv preprint arXiv:2605.24238 , year =

    Toward Enactive Artificial Intelligence , author =. arXiv preprint arXiv:2605.24238 , year =. doi:10.48550/arXiv.2605.24238 , url =

    doi arXiv

  98. [106]

    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume =

    Time-Series Forecasting with Deep Learning: A Survey , author =. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume =. 2021 , doi =

    2021

  99. [107]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume =

    Are Transformers Effective for Time Series Forecasting? , author =. Proceedings of the AAAI Conference on Artificial Intelligence , volume =. 2023 , doi =

    2023

  100. [108]

    arXiv preprint arXiv:1806.01261 , year =

    Relational Inductive Biases, Deep Learning, and Graph Networks , author =. arXiv preprint arXiv:1806.01261 , year =. doi:10.48550/arXiv.1806.01261 , url =

    doi arXiv

  101. [109]

    arXiv preprint arXiv:2508.02076 , year=

    Everyone contributes! Incentivizing strategic cooperation in multi-LLM systems via sequential public goods games , author=. arXiv preprint arXiv:2508.02076 , year=

    arXiv

  102. [110]

    2009 , isbn =

    An Introduction to MultiAgent Systems , author =. 2009 , isbn =

    2009

  103. [111]

    2004 , isbn =

    Automated Planning: Theory and Practice , author =. 2004 , isbn =

    2004

  104. [112]

    American Economic Review , volume =

    The Design of Mechanisms for Resource Allocation , author =. American Economic Review , volume =

  105. [113]

    2004 , isbn =

    Workflow Management: Models, Methods, and Systems , author =. 2004 , isbn =

    2004

  106. [114]

    Sloan Management Review , volume =

    Scenario Planning: A Tool for Strategic Thinking , author =. Sloan Management Review , volume =

  107. [115]

    2007 , doi =

    Algorithmic Game Theory , editor =. 2007 , doi =

    2007

  108. [116]

    Journal of Operations Management , volume =

    Strengthening Supply Chain Resilience During COVID-19: A Case Study of JD.com , author =. Journal of Operations Management , volume =. 2021 , doi =

    2021

  109. [117]

    2004 , doi =

    Convex Optimization , author =. 2004 , doi =

    2004

  110. [118]

    1997 , isbn =

    Introduction to Linear Optimization , author =. 1997 , isbn =

    1997

  111. [119]

    2009 , isbn =

    Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations , author =. 2009 , isbn =

    2009

  112. [120]

    arXiv preprint arXiv:2509.24256 , year=

    Graph Foundation Models: Bridging Language Model Paradigms and Graph Optimization , author=. arXiv preprint arXiv:2509.24256 , year=

    arXiv

  113. [121]

    Proceedings of the 37th International Conference on Machine Learning , year =

    Learning to Simulate Complex Physics with Graph Networks , author =. Proceedings of the 37th International Conference on Machine Learning , year =. doi:10.48550/arXiv.2002.09405 , url =

    2002 doi arXiv

  114. [122]

    Science , volume =

    More Is Different: Broken Symmetry and the Nature of the Hierarchical Structure of Science , author =. Science , volume =. 1972 , doi =

    1972

  115. [123]

    Industrial Dynamics , author =

  116. [124]

    Journal of the Atmospheric Sciences , volume =

    Deterministic Nonperiodic Flow , author =. Journal of the Atmospheric Sciences , volume =. 1963 , doi =

    1963

  117. [125]

    Daedalus , volume =

    Complex Adaptive Systems , author =. Daedalus , volume =. 1992 , url =

    1992

  118. [126]

    Complexity , volume =

    What Is Complexity? Remarks on Simplicity and Complexity , author =. Complexity , volume =. 1995 , doi =

    1995

  119. [128]

    Physical Review Letters , volume =

    Self-Organized Criticality: An Explanation of the 1/f Noise , author =. Physical Review Letters , volume =. 1987 , doi =

    1987

  120. [129]

    Science , volume =

    Complexity and the Economy , author =. Science , volume =. 1999 , doi =

    1999

  121. [130]

    Nature , volume =

    Collective Dynamics of Small-World Networks , author =. Nature , volume =. 1998 , doi =

    1998

  122. [131]

    Science , volume =

    Emergence of Scaling in Random Networks , author =. Science , volume =. 1999 , doi =

    1999

  123. [132]

    SIAM Review , volume =

    The Structure and Function of Complex Networks , author =. SIAM Review , volume =. 2003 , doi =

    2003

  124. [133]

    2009 , isbn =

    Complexity: A Guided Tour , author =. 2009 , isbn =. doi:10.1093/oso/9780195124415.001.0001 , url =

    2009

  125. [134]

    1984 , isbn =

    Normal Accidents: Living with High-Risk Technologies , author =. 1984 , isbn =

    1984

  126. [135]

    2012 , doi =

    Engineering a Safer World: Systems Thinking Applied to Safety , author =. 2012 , doi =

    2012

  127. [136]

    Proceedings of the 37th International Conference on Machine Learning , series =

    Performative Prediction , author =. Proceedings of the 37th International Conference on Machine Learning , series =. 2020 , publisher =

    2020

  128. [137]

    European Journal of Operational Research , year =

    Hu, Hao and Qi, Yongzhi and Kang, Ningxuan and Chen, Zhe and Wang, Ling and Chen, Jian and Shen, Zuo-Jun Max , title =. European Journal of Operational Research , year =

  129. [138]

    Engineering , year =

    Shen, Zuo-Jun Max and Lin, Shaochong , title =. Engineering , year =

  130. [139]

    Service Science , year =

    Liu, Mo and Bai, Yumo and Qi, Meng and Shen, Zuo-Jun Max , title =. Service Science , year =

  131. [140]

    INFORMS Journal on Applied Analytics , year =

    Shen, Zuo-Jun Max and Sun, Shuo and Qi, Yongzhi and Hu, Hao and Kang, Ningxuan and Zhang, Jianshen and Wang, Xin and Lin, Xiaoming , title =. INFORMS Journal on Applied Analytics , year =

  132. [141]

    Manufacturing & Service Operations Management , year =

    Lei, Dazhou and Qi, Yongzhi and Liu, Sheng and Geng, Dongyang and Zhang, Jianshen and Hu, Hao and Shen, Zuo-Jun Max , title =. Manufacturing & Service Operations Management , year =

  133. [142]

    and Shen, Zuo-Jun Max and Liu, Curtis and Zhu, Weimeng and Kang, Ningxuan , title =

    Hu, Hao and Qi, Yongzhi and Lee, Hau L. and Shen, Zuo-Jun Max and Liu, Curtis and Zhu, Weimeng and Kang, Ningxuan , title =. INFORMS Journal on Applied Analytics , year =

  134. [143]

    and Wu, Di and Yuan, Rong and Zhou, Wei , title =

    Shen, Zuo-Jun Max and Tang, Christopher S. and Wu, Di and Yuan, Rong and Zhou, Wei , title =. Manufacturing & Service Operations Management , year =

  135. [144]

    Manufacturing & Service Operations Management , year =

    Jiang, Hansheng and Cao, Junyu and Shen, Zuo-Jun Max , title =. Manufacturing & Service Operations Management , year =

  136. [145]

    Production and Operations Management , year =

    Lei, Dazhou and Hu, Hao and Geng, Dongyang and Zhang, Jianshen and Qi, Yongzhi and Liu, Sheng and Shen, Zuo-Jun Max , title =. Production and Operations Management , year =

  137. [146]

    Journal of Operations Management , year =

    Shen, Zuojun Max and Sun, Yiqi , title =. Journal of Operations Management , year =

  138. [147]

    Manufacturing & Service Operations Management , year =

    Salari, Nooshin and Liu, Sheng and Shen, Zuo-Jun Max , title =. Manufacturing & Service Operations Management , year =

  139. [148]

    Frontiers of Engineering Management , year =

    Wang, Lu and Deng, Tianhu and Shen, Zuo-Jun Max and Hu, Hao and Qi, Yongzhi , title =. Frontiers of Engineering Management , year =

  140. [149]

    Production and Operations Management , year =

    Mak, Ho-Yin and Shen, Zuo-Jun Max , title =. Production and Operations Management , year =

  141. [150]

    Journal of Data, Information and Management , year =

    Ge, Deng and Pan, Yi and Shen, Zuo-Jun Max and Wu, Di and Yuan, Rong and Zhang, Chao , title =. Journal of Data, Information and Management , year =

  142. [151]

    Wagner Prize Awarded to

    2024 Daniel H. Wagner Prize Awarded to. 2024 , month = nov, howpublished =

    2024

  143. [152]

    Franz Edelman Academy: Class of 2023 , year =

    2023

  144. [153]

    2023 , howpublished =

    2023

  145. [154]

    2024 , month = apr, howpublished =

    2024

  146. [155]

    Machine Learning , volume =

    Support-Vector Networks , author =. Machine Learning , volume =. 1995 , doi =

    1995

  147. [156]

    Machine Learning , volume =

    Bagging Predictors , author =. Machine Learning , volume =. 1996 , doi =

    1996

  148. [157]

    Journal of Computer and System Sciences , volume =

    A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting , author =. Journal of Computer and System Sciences , volume =. 1997 , doi =

    1997

  149. [158]

    Science , volume =

    Reducing the Dimensionality of Data with Neural Networks , author =. Science , volume =. 2006 , doi =

    2006

  150. [159]

    Agrawal, G. K. and Heragu, S. S. , title =. IEEE Transactions on Semiconductor Manufacturing , year =

  151. [160]

    IEEE/SEMI Conference and Workshop on Advanced Semiconductor Manufacturing , year =

    Sun, Dong-Seok and Park, No-Sik and Lee, Young-Joong and Jang, Young-Chul and Ahn, Chung-Sam and Lee, Tae-Eog , title =. IEEE/SEMI Conference and Workshop on Advanced Semiconductor Manufacturing , year =

  152. [161]

    and Wu, Cheng-Hung and Huang, Chih-Wei , title =

    Lin, James T. and Wu, Cheng-Hung and Huang, Chih-Wei , title =. Computers & Operations Research , year =

  153. [162]

    Computers & Industrial Engineering , year =

    Bartlett, Kelly and Lee, Junho and Ahmed, Shabbir and Nemhauser, George and Sokol, Joel and Na, Byungsoo , title =. Computers & Industrial Engineering , year =

  154. [163]

    2017 Winter Simulation Conference , year =

    Schmaler, Robert and Schmidt, Thorsten and Schoeps, Matthias and Luebke, Joerg and Hupfer, Ralf and Schlaus, Nikolas , title =. 2017 Winter Simulation Conference , year =

    2017

  155. [164]

    International Journal of Production Research , year =

    Hwang, Illhoe and Jang, Young Jae , title =. International Journal of Production Research , year =

  156. [165]

    and Wang, Junliang , title =

    Wu, Lihui and Zhang, Zhongwei and Zhang, Jie and Zhong, Ray Y. and Wang, Junliang , title =. Journal of Manufacturing Systems , year =

  157. [166]

    IISE Transactions , year =

    Ahn, Kyuree and Lee, Kanghoon and Yeon, Juneyoung and Park, Jinkyoo , title =. IISE Transactions , year =

  158. [167]

    IEEE Transactions on Semiconductor Manufacturing , year =

    Hong, Sangpyo and Hwang, Illhoe and Jang, Young Jae , title =. IEEE Transactions on Semiconductor Manufacturing , year =

  159. [168]

    2024 Winter Simulation Conference , year =

    Japhne, Ferdinandz and Jang, Young Jae , title =. 2024 Winter Simulation Conference , year =

    2024

  160. [169]

    Computers & Industrial Engineering , year =

    Chou, Che-Wei and Chiu, Wei-Cheng and Hsu, Yu-Teng , title =. Computers & Industrial Engineering , year =

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.