arXiv subjects

Zuojun Max Shen

Publications and source records attributed to Zuojun Max Shen.

4 recordsLinked to original sources

arXiv2026

Machine Learning for Scheduling Decision Systems: A Critical Review of Architecture, Assurance, and Deployment

Machine learning supports scheduling through prediction, search guidance, or schedule formation, but model-level evaluations obscure the downstream work, technical authority, and controls needed to release decisions. We conduct a critical integrative review combining structured candidate identification and purposive full-text synthesis, treating the complete reported scheduling decision system (from problem specification to release and conditional recovery) as the unit of analysis. Our system-level taxonomy distinguishes the function of learned outputs, schedule formation, and binding control along the normal release path. It separates learning and adaptation from decision assurance, and technical authority from organizational decision rights. Reported timing and solver guarantees depend on downstream work and the decision space left after learned commitments; transfer of retained capability differs from architectural reuse, and operational maturity from automated release. Evidence supports selected quality-computation-time trade-offs, bounded transfer of retained capability, performance within specified regimes, and operating use in several configurations. It does not support a system-equivalent ranking of learning and optimization, general cross-task transfer, or common conclusions about lifecycle economics and long-run field performance. Four operations-management propositions link lifecycle value to effective reuse, technical authority to forms of change, deadline-feasible assurance and recovery, and organizational rights to information and accountability. Solver-led, shared-authority, and model-led configurations are alternative designs, not maturity stages; model performance alone does not justify greater release authority.

math.OC↗

arXiv2026

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

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.

cs.AI↗

arXiv2022

Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting

In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. However, incorporating ML is generally considered to sacrifice interpretability inevitably. In addition, existing hybrid algorithms usually rely on theoretical models with statistical assumptions and focus only on the accuracy of aggregate predictions, and thus suffer from accuracy problems, especially in component estimates. In response to the above issues, this research explores the possibility of improving accuracy without losing interpretability in time series forecasting. We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability. Accordingly, we propose the W-R algorithm, a hybrid algorithm that combines decomposition and ML from a novel perspective. Specifically, the W-R algorithm replaces the standard additive combination function with a weighted variant and uses ML to modify the estimates of all components simultaneously. We mathematically analyze the theoretical basis of the algorithm and validate its performance through extensive numerical experiments. In general, the W-R algorithm outperforms all decomposition-based and ML benchmarks. Based on P50_QL, the algorithm relatively improves by 8.76% in accuracy on the practical sales forecasts of JD.com and 77.99% on a public dataset of electricity loads. This research offers an innovative perspective to combine the statistical and ML algorithms, and JD.com has implemented the W-R algorithm to make accurate sales predictions and guide its marketing activities.

cs.LG↗

arXiv2021

Optimizing timetable and network reopen plans for public transportation networks during a COVID19-like pandemic

The recovery of the public transportation system is critical for both social re-engagement and economic rebooting after the shutdown during pandemic like COVID-19. In this study, we focus on the integrated optimization of service line reopening plan and timetable design. We model the transit system as a space-time network. In this network, the number of passengers on each vehicle at the same time can be represented by arc flow. We then apply a simplified spatial compartmental model of epidemic (SCME) to each vehicle and platform to model the spread of pandemic in the system as our objective, and calculate the optimal open plan and timetable. We demonstrate that this optimization problem can be decomposed into a simple integer programming and a linear multi-commodity network flow problem using Lagrangian relaxation techniques. Finally, we test the proposed model using real-world data from the Bay Area Rapid Transit (BART) and give some useful suggestions to system managers.

econ.GN↗