Published
A continuous time model for a short-term multiproduct batch process scheduling
Modelo de programación de la producción por lotes de múltiples productos con tiempo continuo
Keywords:
MIP modeling, goal programming, batch process scheduling, short-term scheduling, mathematical formulation, Monte Carlo simulation (en)programación entera mixta, programación de la producción por lotes, industria química, modelación matemática, simulación Monte Carlo (es)
In the chemical industry, it is common to find production systems characterized by having a single stage or a previously identified bottleneck stage, with multiple non-identical parallel stations and with setup costs that depend on the production sequence. This paper proposes a mixed integer production-scheduling model that identifies lot size and product sequence that maximize profit. It considers multiple typical industry conditions, such as penalties for noncompliance or out of service periods of the productive units (or stations) for preventive maintenance activities. The model was validated with real data from an oil chemical company. Aiming to analyze its performance, we applied the model to 155 instances of production, which were obtained using Monte Carlo technique on the historical production data of the same company. We obtained an average 12 % reduction in the total cost of production and a 19 % increase in the estimated profit.
En la industria química es común encontrar sistemas de producción caracterizados por tener una sola etapa o una etapa cuello de botella, con múltiples estaciones paralelas, no idénticas, y con costos de preparación o alistamiento dependientes de la secuencia de producción. Este artículo propone un modelo lineal mixto de programación de la producción que busca identificar el tamaño de lote y la secuenciación de productos con el objetivo de maximizar el beneficio. Considera múltiples condiciones típicas de la industria, tales como la penalización por incumplimientos, la programación de mantenimientos preventivos de las estaciones y la disponibilidad temporal de las estaciones. El modelo se validó con datos reales de una empresa de la industria del petróleo. Buscando analizar el desempeño del modelo, se analizaron los resultados de aplicar el modelo a 155 instancias generadas aplicando simulación Montecarlo, a los datos históricos de producción de la misma compañía. Se obtuvo una reducción del 12 % en la reducción total del costo de producción y un incremento del 19 % en la utilidad estimada.
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Copyright (c) 2018 Jenny Díaz Ramírez, Jose Ignacio Huertas
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