Aug 2026· Chemical engineering research & design· Vol 233, pp. 173-185· 0 citations· 39 references
MathematicsComputer Science
TL;DR
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.
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.
One crucial activity for a company’s long-term sustainability is establishing an effective production planning policy. Currently, CV Lembah Melintang relies on consumer demand to determine its production process, which often leads to suboptimal resource utilization. This research aims to develop an optimal production plan using a Multi-Goal Programming (MGP) model to maximize resource use and increase company income.
The MGP model, an extension of the goal programming method, allows for multiple objectives by incorporating deviation variables to handle conflicts between goals. In this study, the objectives are: maximizing total production, minimizing production costs, and maximizing profit, subject to relevant resource constraints. Data analysis and computation were carried out using POM-QM v5 software.
The results indicate that the production plan successfully met the company’s targets: sales volume was achieved, production costs remained below the target limit of IDR 922,105,662.4, and profit reached the target of IDR 564,080,764 for the 2023 period. The study concludes that the MGP model is an effective decision-support tool for improving production planning policies and aligning multiple strategic goals in manufacturing environments.
H. Cipta, Rina Widyasari· Jurnal Matematika UNAND· 0 citations
Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.
This work develops an inverse optimization approach to jointly learn the decision-maker's preferences and the decision rules governing their choices, which leads to better predictions and greater flexibility in capturing and replicating expert decision making.
Anurag Holani, Rishabh Gupta, J. Wassick et al.· 1 citation
Demand uncertainty complicates inventory decision-making and requires decision-support systems that are both accurate and transparent. However, existing studies primarily emphasize either demand forecasting or inventory optimization, with limited attention to integrating explainability into a unified decision-making framework. This study develops and evaluates an Explainable Optimization Framework that combines Extreme Gradient Boosting (XGBoost) for demand forecasting, SHapley Additive exPlanations (SHAP) for model interpretability, and Mixed Integer Programming (MIP) for inventory optimization. The framework was developed following the Design Science Research methodology, encompassing problem identification, artifact development, demonstration, evaluation, and communication. Model performance was assessed using rolling-origin backtesting to provide a robust evaluation under dynamic and uncertain demand conditions. Forecasting accuracy was measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that XGBoost achieved the highest forecasting accuracy, with an RMSE of 43.87, MAE of 33.92, and MAPE of 7.61%. The forecasted demand was subsequently incorporated into the MIP optimization model, resulting in a 22.3% reduction in total inventory cost, an increase in service level from 91.2% to 96.8%, an improvement in fill rate from 89.7% to 95.4%, a 57.1% reduction in stockout frequency, and an increase in inventory turnover from 5.8 to 7.2 compared with the baseline approach. SHAP analysis identified historical demand, promotional activities, and product price as the most influential variables affecting demand predictions, providing transparent explanations that enhance managerial trust and support informed inventory decisions. Overall, the proposed framework demonstrates that integrating forecasting, explainability, and mathematical optimization into a unified decision pipeline significantly improves operational efficiency, inventory performance, decision transparency, and resilient data-driven supply chain management across diverse industrial sectors
Alfry Aristo Jansen Sinlae, Fajriana Fajriana, Yenny Suzana et al.· International Journal of Eng...· 0 citations
The assignment problem is one of the most fundamental optimization models in operations research, focusing on the efficient allocation of limited resources to specific tasks while minimizing total cost or maximizing overall effectiveness. Because of its mathematical simplicity and computational efficiency, the model has become an essential decision – support tool across manufacturing, logistics, healthcare, education, transportation and many other industries. This paper presents a comprehensive examination of the assignment problem through a practical case study approach. It begins with an overview of the historical evolution of the assignment problem, followed by a review of relevant literature and a discussion of its theoretical foundations. The paper further distinguishes the assignment problem from other optimization techniques, including transportation and linear programming models. To illustrate its practical applicability, a real-world-inspired machine-to-job allocation problem is formulated and solved systematically using the Hungarian Method. Each stage of the solution process is explained with appropriate tables and interpretations to enhance conceptual understanding. The study also highlights the diverse applications of assignment models across multiple industries and discusses emerging research directions involving artificial intelligence, machine learning, fuzzy optimization, and dynamic decision-making. The findings demonstrate that the assignment problem remains a powerful analytical tool for improving operational efficiency and supporting evidence-based managerial decisions in increasingly complex organizational environments.
Karunasree Padala, Vijaya Sree Vignatha Vangala· International Journal of Lat...· 0 citations
Material Requirement Planning (MRP) is an important part of production management because it ensures the timely availability of materials while reducing costs and surplus inventory. This study introduces a Goal Programming (GP)-based optimization model for material planning in a furniture manufacturing company that produces three product types: dining tables, folding chairs, and fittings. The suggested methodology combines MRP concepts with preemptive multi-objective optimization to address common challenges in the manufacturing process and ultimately to reduce production costs, inventory holding costs, and expenses related to resource idle time and overtime. The mathematical formulation is built on a preemptive priority structure and solved using two computing approaches: Microsoft Excel Solver for baseline linear programming cost minimization and MATLAB's goal attain function for multi-objective goal programming. The suggested model achieves zero excess in priority production goals while keeping total production costs at 10.3% of the LP-derived optimum. The combination of MRP and Goal Programming is demonstrated to provide a practical, scalable, and computationally efficient decision-support framework for production managers in small and medium-sized manufacturing industries, outperforming conventional single-objective planning approaches reported in the literature.
N. Raut, Harshal Nemade· international journal of eng...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
