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

arXiv: https://arxiv.org/abs/2608.07398 · HTML: https://mlsanity.com/paper/2608.07398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 21 of 21)

- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations](https://mlsanity.com/paper/1505.05116/) (2015)
- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [JuMP: A Modeling Language for Mathematical Optimization](https://mlsanity.com/paper/1508.01982/) (2015)
- [Inverse Optimization: Theory and Applications](https://mlsanity.com/paper/2109.03920/) (2021)
- [Inverse Optimization with Noisy Data](https://mlsanity.com/paper/1507.03266/) (2015)
- [Data-driven Inverse Optimization with Imperfect Information](https://mlsanity.com/paper/1512.05489/) (2015)
- [Learning Linear Programs from Optimal Decisions](https://mlsanity.com/paper/2006.08923/) (2020)
- [Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices](https://mlsanity.com/paper/1911.02067/) (2019)
- [Short-term Forecasting of Price-responsive Loads Using Inverse Optimization](https://mlsanity.com/paper/1607.07209/) (2016)
- [Inverse Optimization for Routing Problems](https://mlsanity.com/paper/2307.07357/) (2023)
- [Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks](https://mlsanity.com/paper/1703.04010/) (2017)
- [Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization](https://mlsanity.com/paper/2009.07961/) (2020)
- [Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods](https://mlsanity.com/paper/2008.00301/) (2020)
- [On the Complexity of Inverse Mixed Integer Linear Optimization](https://mlsanity.com/paper/2104.09002/) (2021)
- [Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization](https://mlsanity.com/paper/2210.15393/) (2022)
- [BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification](https://mlsanity.com/paper/2405.17875/) (2024)
- [Gap-gradient methods for solving generalized mixed integer inverse optimization: an application to political gerrymandering](https://mlsanity.com/paper/2406.09457/) (2024)
- [Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization](https://mlsanity.com/paper/2410.02774/) (2024)
- [An inverse mixed-integer optimization framework for learning interpretable models of expert decision making](https://mlsanity.com/paper/2608.05585/) (2026)

## Abstract (arXiv)

Production planning in the manufacturing industry often relies on the use of optimization models, but defining an appropriate objective function can be a challenge. In practice, planners must balance competing goals, manage uncertainty, and account for qualitative business preferences that are difficult to quantify. As a result, many optimization models fail to match expert behavior, limiting trust and adoption. In this work, we propose a data-driven inverse optimization framework to infer the objective function implicitly captured in expert planners' decisions. We formulate the production planning problem as a mixed-integer linear program, where the unknown objective function is represented as a weighted sum of hypothesized cost terms. A suboptimality-loss-based inverse optimization method is then applied to learn the objective weights from historical production plans. The proposed approach is applied to a real industrial case provided by Dow, where the inferred weights reveal that avoiding inventory shortages and maintaining consistent cycle lengths dominate the planners' decision-making. Time- and product-dependent extensions further improve predictive accuracy and uncover evolving priorities. Expert interviews confirm the practical validity of these insights. Overall, this study shows that inverse optimization can transform tacit human expertise into interpretable models, enabling more accurate and trusted decision-support tools for complex industrial systems.