Programação de operações em duas etapas para um flow shop multiperíodo não tradicional

This documents presents a detailed review of a two-stage approach to scheduling operations in a multi-period flow shop where various tasks have to be completed in the same time window. Some tasks are delivered immediately and must be scheduled as early as possible within the time window. Others are carried out in the latest stage in order to minimize their time of permanence in the system. It proposes a time decomposition strategy to address the multi-period issue, combining structured programming and linear programming in such a way that, for each period, two stages are run consisting of two mathematical models that schedule tasks based on their priority. The proposed approach was validated through the resolution of the problem of scheduling work for a feed manufacturing company where a schedule was prepared for each time period, meeting the requirements for immediate delivery products and those which can be delivered at a later point in time. The schedule minimizes the work-in-progress inventory of the first and the time of non-permanence in the system of the latter.En el presente documento se desarrolla una metodología de dos etapas para programar las operaciones en un flowshop multiperíodo, en éste se tienen trabajos que aunque que se deben terminar en la misma ventana de tiempo, unos son de entrega inmediata y deben programarse en el momento más temprano de la ventana, y otros en el momento más tardío, de tal modo que se minimice su tiempo de permanencia en el sistema. Se plantea una estrategia de descomposición temporal para el problema multiperíodo, en la que se combina la programación estructurada con la programación lineal, de tal modo que para cada periodo se corren dos fases compuestas de dos modelos matemáticos que programan los trabajos según su prioridad. La metodología planteada se valida en un problema de programación de trabajos en la industria de alimentos concentrados, obteniéndose como resultado un scheduling para cada periodo que satisface los requerimientos de los productos de entrega inmediata y los de entrega en el momento más tardío. Del tal modo que se minimiza para los primeros el inventario de producto en proceso y para los segundos el tiempo de no permanencia en el sistema.No presente documento, desenvolve-se uma metodologia de duas etapas para programar as operações em um flow shop multiperíodo, nele há trabalhos que embora devem terminar na mesma janela de tempo, uns são de entrega imediata e devem programar-se no momento mais cedo da janela e outros no momento mais tardio, de tal modo que se minimize seu tempo de permanência no sistema. Apresenta-se uma estratégia de descomposição temporal para o problema multiperíodo, na qual se combina a programação estruturada com a programação linear, de tal modo que para cada período correm duas fases compostas de dois modelos matemáticos que programam os trabalhos conforme sua prioridade. A metodologia apresentada valida-se em um problema de programação de trabalhos na indústria de alimentos concentrados, obtendo-se como resultado uma programação para cada período de tempo que satisfaça os requerimentos dos produtos de entrega imediata e os de entrega no momento mais tardio. Del tal modo que se minimiza para os primeiros o inventário de produto no processo e para os segundos o tempo de não permanência no sistema

Stochastic petri nets in the impact of the capacity in the service in mass-produced restaurants

in the present paper, a methodology based on stochastic petri nets is proposed to measure the impact caused by the capacity constraints of productive factors on the level of service in mass-produced restaurants. The system under study is identified and a discrete simulation model based on Stochastic Petri Nets is developed, which considers the most representative components and characteristics of the system and the interrelations between them. The test is performed in a restaurant that serves more than 4,500 lunches on day, where the impact of the production and service stages can be identified and the most critical and its effects were characterizedin the present paper, a methodology based on stochastic petri nets is proposed to measure the impact caused by the capacity constraints of productive factors on the level of service in mass-produced restaurants. The system under study is identified and a discrete simulation model based on Stochastic Petri Nets is developed, which considers the most representative components and characteristics of the system and the interrelations between them. The test is performed in a restaurant that serves more than 4,500 lunches on day, where the impact of the production and service stages can be identified and the most critical and its effects were characterized

Stochastic petri nets in the impact of the capacity in the service in mass-produced restaurants

in the present paper, a methodology based on stochastic petri nets is proposed to measure the impact caused by the capacity constraints of productive factors on the level of service in mass-produced restaurants. The system under study is identified and a discrete simulation model based on Stochastic Petri Nets is developed, which considers the most representative components and characteristics of the system and the interrelations between them. The test is performed in a restaurant that serves more than 4,500 lunches on day, where the impact of the production and service stages can be identified and the most critical and its effects were characterizedin the present paper, a methodology based on stochastic petri nets is proposed to measure the impact caused by the capacity constraints of productive factors on the level of service in mass-produced restaurants. The system under study is identified and a discrete simulation model based on Stochastic Petri Nets is developed, which considers the most representative components and characteristics of the system and the interrelations between them. The test is performed in a restaurant that serves more than 4,500 lunches on day, where the impact of the production and service stages can be identified and the most critical and its effects were characterized

El proceso de análisis jerárquico (AHP) y la toma de decisiones multicriterio. Ejemplo de aplicación.

Los problemas de toma de decisiones son procesos complejos en los cuales intervienen múltiples criterios, por lo cual es necesario utilizar herramientas que permitan discernir entre estos para obtener una solución que satisfaga en mejor grado la combinación de alternativas posibles. Una de estas herramientas, es el AHP (Proceso de Análisis Jerárquico). En este artículo se presenta el método, y un ejemplo de aplicación del mismo

El proceso de análisis jerárquico (AHP) y la toma de decisiones multicriterio. Ejemplo de aplicación.

Los problemas de toma de decisiones son procesos complejos en los cuales intervienen múltiples criterios, por lo cual es necesario utilizar herramientas que permitan discernir entre estos para obtener una solución que satisfaga en mejor grado la combinación de alternativas posibles. Una de estas herramientas, es el AHP (Proceso de Análisis Jerárquico). En este artículo se presenta el método, y un ejemplo de aplicación del mismo

Balanceo de líneas de producción en la industria farmacéutica mediante Programación por metas

Introduction: In a production Line it’s important that the stations’ cycle times are balanced and that they are low since this allows to reduce the work in process. However, doing this leads to an increase in the stations’ number, that is not favorable because it raises the costs associated with the stations, therefore it is necessary to define strategies that allow achieving a balance between these requirements. Objective: In this article we propose the formulation of a model for the line balancing, using the technique of multi-objective goal programming, applied to the pharmaceutical industry in order to minimize the stations’ number, minimize cycle time and inventory in process. Methodology: Goal programming is used to address a line balance model, which considers at the same time the assignment of multiple stations to one operation and the assignment of multiple operations to one station. Results: A significant decrease in cycle time and idle time at minimum costs is achieved, and a comparison between the deterministic and stochastic models is presented. Conclusions: Through this implementation of the LINGO model, the compliance of the proposed restrictions, the precedence of operations and the proper functioning of the model were validated through the optimal solutions obtained. The simulation is a tool that illustrates the complexity of the operations of the production system, which require, as in our case, more realistic modeling to understand the behavior of the process and evaluate different strategies.Introducción: En una línea de fabricación es muy importante que los tiempos de ciclo de las diferentes estaciones estén balanceados y que sean bajos, ya que esto permite disminuir los inventarios de producto en proceso, sin embargo, hacer esto conlleva a aumentar el número de estaciones, lo que no es favorable ya que eleva los costos fijos asociados a las estaciones, en tal sentido es necesario definir estrategias que permitan lograr un equilibrio entre estos requerimientos. Objetivo: En este artículo se propone la formulación de un modelo para el balanceo de línea, utilizando la técnica de programación multiobjetivo por metas, aplicada a la industria farmacéutica con el fin de minimizar el número de estaciones, minimizar el tiempo de ciclo y el inventario en proceso. Metodología: Se emplea la programación por metas para abordar un modelo de balance de línea, que considera al mismo tiempo la asignación de múltiples estaciones una operación y la asignación de múltiples operaciones a una estación. Resultados: Se logra una reducción significativa del tiempo ciclo y del tiempo ocioso a costos mínimos, además se presenta una comparación entre el modelo determinístico y estocástico. Conclusiones: A través de esta implementación del modelo en LINGO, se validó el cumplimiento de las restricciones planteadas, la precedencia de las operaciones y el buen funcionamiento del modelo mediante las soluciones óptimas obtenidas. La simulación, es una herramienta que permite ilustrar la complejidad de las operaciones del sistema de producción, las cuales requieren como en nuestro caso de una modelación más ajustada a la realidad para comprender el comportamiento del proceso y evaluar diferentes estrategia

Production line balancing in the pharmaceutical industry using Goal Programming

Introducción−En una línea de fabricación es muy impor-tante que los tiempos de ciclo de las diferentes estaciones estén balanceados y que sean bajos, ya que esto permite disminuir los inventarios de producto en proceso, sin em-bargo, hacer esto conlleva a aumentar el número de esta-ciones, lo que no es favorable ya que eleva los costos fijos asociados a las estaciones, en tal sentido es necesario definir estrategias que permitan lograr un equilibrio entre estos requerimientos.Objetivo− En este artículo se propone la formulación de un modelo para el balanceo de línea, utilizando la técnica de programación multi-objetivo por metas, aplicada a la industria farmacéutica con el fin de minimizar el número de estaciones, minimizar el tiempo de ciclo y el inventario en proceso.Metodología− Se emplea la programación por metas para abordar un modelo de balance de línea, que considera al mis-mo tiempo la asignación de múltiples estaciones una opera-ción y la asignación de múltiples operaciones a una estación. Resultados− Se logra una reducción significativa del tiem-po ciclo y del tiempo ocioso a costos mínimos, además se presenta una comparación entre el modelo determinístico y estocástico.Conclusiones−A través de esta implementación del modelo en LINGO, se validó el cumplimiento de las restricciones planteadas, la precedencia de las operaciones y el buen funcionamiento del modelo mediante las soluciones óptimas obtenidas. La simulación, es una herramienta que permite ilustrar la complejidad de las operaciones del sistema de producción, las cuales requieren como en nuestro caso de una modelación más ajustada a la realidad para compren-der el comportamiento del proceso y evaluar diferentes estrategias.Introduction−In a production Line it’s important that the stations’ cycle times are balanced and that they are low since this allows to reduce the work in process. However, doing this leads to an increase in the stations’ number, that is not favorable because it raises the costs associated with the stations, therefore it is necessary to define strategies that allow achieving a balance be-tween these requirements.Objective−In this article we propose the formulation of a model for the line balancing, using the technique of multi-objective goal programming, applied to the pharmaceutical industry in order to minimize the stations’ number, minimize cycle time and inventory in process.Methodology−Goal programming is used to address a line balance model, which considers at the same time the assignment of multiple stations to one op-eration and the assignment of multiple operations to one station.Results−A significant decrease in cycle time and idle time at minimum costs is achieved, and a comparison between the deterministic and stochastic models is presented.Conclusions−Through this implementation of the LINGO model, the compliance of the proposed restric-tions, the precedence of operations and the proper func-tioning of the model were validated through the optimal solutions obtained. The simulation is a tool that illus-trates the complexity of the operations of the production system, which require, as in our case, a more realistic modeling to understand the behavior of the process and evaluate different strategies

Supply Chain Network Design of Perishable Food in Surplus Periods

Research on the design of the perishable food supply chain network has increased in recent years. However, little attention has been given to those seasonal foods that generate periods of oversupply and, particularly, to their impact on the sustainability of small producers in developing countries. This research proposes and develops a multi-objective mixed linear programming model for perishable fruits in a south American country at oversupply periods. It minimizes losses and transportation costs and maximizes the inclusion of farmers. It considers four echelons of the supply chain: farms, collection centers, distribution centers and the demand, which is represented by the agroindustry, wholesalers, shopkeepers, and hypermarkets. The Epsilon constraint method is used to solve the multi-objective model. A set of Pareto optimal solutions helped evaluate tradeoffs between the three objectives and find the location of collection and distribution centers. The proposed generic mathematical model is applicable to any food supply chain, as it allows for the improvement of the established performance measures and the distribution flows for the different echelons. The model considers the losses in perishable food from the impacts caused by changes in temperature (T0) and humidity level (RH) at different thermal floors of mountain ranges