arXiv:2608. 07398v1 Announce Type: cross Abstract: Production planning in the manufacturing industry often relies on the use of optimization models, but defining an appropriate objective function can be a challenge.
arXiv:2608. 15124v1 Announce Type: new Abstract: In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features.
arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,...
arXiv:2607. 18530v1 Announce Type: cross Abstract: Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management.
arXiv:2606. 00002v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems.
The article "Supply Chain Analytics: A Data-Driven Approach" presents a mathematically rigorous framework that integrates statistical learning with robust decision-making for logistics and operations management. It covers topics from empirical demand forecasting to optimal inventory and network control under uncertainty, including sample minimization, dynamic programming for inventory replenishment, network fulfillment, and distributionally robust optimization using transport theory. The work also links predictive models with prescriptive algorithms such as column generation for vehicle routing and non‑homogeneous queueing regimes, offering both theoretical foundations and algorithmic guidance for building resilient, automated supply chain systems.
arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.
arXiv:2607. 13735v1 Announce Type: new Abstract: The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering.
Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability.
The tutorial titled "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers" explores how modern deep learning techniques—such as neural networks, transformers, large language models, and deep reinforcement learning—can be integrated with operations research and management science to address complex, uncertain, and dynamic decision problems. It argues that deep learning should complement, not replace, optimization, offering adaptability and scalable approximation while OR/MS provides rigorous constraint and uncertainty modeling. The tutorial organizes the field around predict‑then‑optimize, decision‑aware learning, constraint‑aware decision generation, and deep reinforcement learning, and highlights applications across supply chains, healthcare, energy, and autonomous systems.
The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering. Despite a rich literature spanning quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference-time optimization, practitioners are often left navigating these techniques through heuristics rather than principled methodology.
The paper introduces a score‑calibrated robustness framework that transforms any fixed point predictor into a decision‑relevant uncertainty representation using distribution‑free conformal calibration. By employing the conformal score as the core unit of robustness, the authors derive both reliability‑based robust optimization and target‑oriented Conformal Robust Satisficing formulations, linking them through a shared robust decision frontier and a fragility measure. Experiments on synthetic data and a real online‑grocery inventory case study demonstrate the framework’s ability to improve reliability, reduce costs, and provide interpretable uncertainty scales for black‑box predictors.
arXiv:2509. 01924v4 Announce Type: replace-cross Abstract: Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides.