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

2026-08-07

AI Summary

This study addresses the challenge of modeling the implicit multi-objective trade-offs and qualitative preferences embedded in expert decision-making within production planning, which often leads to optimization outcomes misaligned with real-world practices. Leveraging historical expert scheduling data, the authors propose an inverse optimization method based on suboptimality loss to infer a time- and product-dependent structure of objective weights from a mixed-integer linear programming model, thereby recovering experts’ latent preferences—such as aversion to inventory shortages and emphasis on period consistency. Applied in a real industrial setting at Dow Inc., this approach is the first to reveal how expert priority among objectives dynamically shifts under operational uncertainty. The method significantly improves behavioral prediction accuracy and enhances model trustworthiness through interpretable weight structures, earning validation from domain experts.

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. 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.

Problem

• production planning
• objective function
• expert decision-making
• inverse optimization
• manufacturing industry

Innovation

• inverse optimization
• production planning
• mixed-integer linear programming
• objective function inference
• data-driven decision support