Uncovering expert objectives in production planning via inverse optimization: An industrial case study

arXiv:2608.07398 · math.OC, cs.HC, cs.LG · Submitted 2026-08-07 · Read on arXiv

Shivi Dixit, Rishabh Gupta, Adam Kelloway, John Wassick, Qi Zhang

math.OC, cs.HC, cs.LG

Submitted: 2026-08-07

Updated: 2026-08-10

Journal ref: Chemical Engineering Research and Design, 2026

DOI: 10.1016/j.cherd.2026.07.065

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Planner — 74× in this paper · explained in 3 episodes
• Objective Function — 46× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Decision-Support Tool — 3× in this paper · explained in 2 episodes
• Reward Function — 3× in this paper · explained in 4 episodes
• Combinatorial Optimization — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Bilevel Optimization — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Human-in-the-loop — 1× in this paper · explained in 4 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Minnesota Supercomputing Institute — 1× in this paper
• Mixed-Integer Linear Program (MILP) — 1× in this paper · explained in 3 episodes
• Multi-objective Optimization — 1× in this paper · explained in 5 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Uncertainty Quantification — 1× in this paper · explained in 9 episodes

Sources

• Inverse Optimization: Theory and Applications
• Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization
• An inverse mixed-integer optimization framework for learning interpretable models of expert decision making
• Gap-gradient methods for solving generalized mixed integer inverse optimization: an application to political gerrymandering

Related papers

• Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise
• Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
• Incremental Learning in Mirror Flows
• Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
• Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets
• Second-Order Optimization with Asynchronous Curvature