Forecast-Based Optimization: Applying Predictive Models for Decision-Making Under Uncertain Constraints
DOI:
https://doi.org/10.5753/jserd.2026.6739Keywords:
Forecasting Algorithms, Machine Learning, Contextual Optimization, Decision Making, Resource AllocationAbstract
Process automation has become a commonly adopted concept of modern business operations, driven by increasing investments in accessible software and tools. Automation workflows streamline decision-making by reducing human error, aligning with company standards, and freeing resources for higher-value tasks. Intelligent systems represent the next step in automation, applying machine learning algorithms to go beyond rule-based decision-making. However, their implementation requires expert modeling and integration into daily routines, posing significant challenges for users to embrace. This paper discusses the end-to-end complexities associated with implementing an intelligent automated system and explores the benefits of integrating machine learning methods, especially forecasting algorithms, to improve the system's efficiency. It also provides a practical situation solved by introducing a two-stage hybrid approach based on the predict-then-optimize paradigm, addressing a problem with long-term intelligent decision-making. This methodology integrates forecasting models to predict future information for long-term evaluations, which are then utilized by an optimizer to recommend configurations that balance current and future outcomes. This approach reduces the limitations of traditional optimization, offering a practical solution for scenarios where future information is unavailable.
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Copyright (c) 2026 Edson M. Odake Junior, Fabio F. Oliveira, Rafael S. Azevedo, Jorge M. S. Vasconcellos, Leandro Andrade Vieira
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