Urban traffic signal systems based on fixed-time plans may not adequately respond to variations in traffic demand among signalized intersections, limiting their operational efficiency. This study proposes a data-driven framework based on a Multilayer Perceptron (MLP) to support traffic signal timing optimization in an urban corridor. The framework uses historical traffic observations to estimate traffic volume, cycle length, and effective green time and subsequently transforms the predicted outputs into feasible signal-timing configurations while preserving the existing two-phase structure. The approach was evaluated using 5880 intersection–interval records collected from six signalized intersections and compared with the existing fixed-time configuration. The results showed consistent operational improvements across all six intersections. The Vehicle Congestion Index (VCI) decreased by 23.7–65.6%, while average control delay decreased by 9.5–26.2%. The largest improvement was observed at Intersection 2, where the VCI decreased by 65.6 and average control delay decreased by 26.2%. The MLP-based configurations also modified cycle length, green time, and red time according to the operating conditions of individual intersections rather than applying a uniform adjustment. Statistical analysis supported the observed reduction in control delay, with a paired-samples t-test yielding p = 0.000782 and a Wilcoxon signed-rank test yielding p = 0.015625. These findings indicate that integrating traffic prediction with signal-timing optimization can provide a practical data-driven alternative to conventional fixed-time control, particularly in environments where fully adaptive traffic-control infrastructure is limited. The framework was evaluated offline within a single six-intersection corridor; therefore, further validation using real-time data and broader networks is required.

Operational production planning has become a key factor in a company’s competitiveness, enabling efficient resource management and a rapid response to fluctuations in demand. However, many manufacturing organizations base their decisions on a conceptual perspective, creating imbalances between supply and demand. In this context, this study proposes a hybrid framework that integrates demand forecasting techniques and discrete-event simulation to optimize production planning at a dairy plant under scenarios involving random demand. First, a predictive model was developed using ARIMA as a baseline for comparison, and then parameters were adjusted in neural networks to estimate demand. The results showed that neural networks performed better, specifically the RN2 model, in generating future projections. Subsequently, the estimates were used as inputs for the simulation model to represent the company’s current system, enabling the identification of bottlenecks and the evaluation of improvement scenarios. The findings indicated that the company’s current capacity is insufficient to meet current and projected demand. Similarly, the “Assembly” and “Quality Control” stations were identified as the main operational constraints. The proposed improvement increased production capacity by 27.7%, enabling the company to meet demand. The evidence shows that integrating forecasting with simulation is an effective tool, as it provides data and scenarios that closely reflect reality, allowing companies to anticipate constraints and, most importantly, reduce risks and make more informed investment decisions.

El modelado de inventarios permite entender el comportamiento de los sistemas de abastecimiento y demanda en una empresa mediante la construcción, solución y análisis de representaciones del mundo real, facilitando la gestión operativa. Este estudio combina la inteligencia artificial con la teoría matemática de los inventarios para mejorar el control de las existencias. Tras evaluar la situación actual de la empresa, se determinó que el modelo de Cantidad Económica de Pedido (EOQ) probabilístico era el más adecuado para optimizar los costos en un entorno de demanda variable, abordando problemas como la ruptura del inventario o exceso del mismo, mediante el punto de pedido y el stock de seguridad. Se entrenaron cinco redes neuronales con el objetivo de combinar su capacidad predictiva con el modelo EOQ. Entre ellas, destacó la arquitectura 3-6-1 con el optimizador ADAM, la cual alcanzó una precisión del 98%. Su implementación permitió una reducción del 27 % en los costos operativos.

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context.