A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions

Petrov́ić, Dobrila; Duenas, Alejandra

doi:10.1016/j.fss.2006.04.009

Cited by 78 publications

(33 citation statements)

References 28 publications

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“…The paper by Bonfatti et al (2006) argues that most approaches to scheduling falsely assume that parameters, such as quantity and due date, are deterministic and precise. Hence, they develop a model for representing these parameters with fuzzy parameters Petrovic & Duenas (2006) go further and actually develops a predictive and reactive scheduling system based on fuzzy reasoning. Their system includes a set of fuzzy rules for determining when rescheduling is needed and another set recommends which scheduling heuristics should be utilised for a given situation.…”

Section: Fuzzy Logic In Schedulingmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Fuzzy Logic In Schedulingmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Los parámetros temporales imprecisos involucrados en el proyecto fueron representados por conjuntos difusos. En [26] se hace un diseño de un sistema de ayuda a la decisión (SSD) para la programación de máquinas con la presencia de incertidumbre disruptiva utilizando conjuntos difusos. E. Análisis sensitivo El análisis de sensibilidad (SA) se utiliza para determinar, en qué medida, la salida de un modelo dado, depende de los parámetros de entrada.…”

Section: Métodos Para Tratar La Incertidumbreunclassified

Scheduling With Uncertainty

Lujan¹,

Segura²,

Moreno³

et al. 2015

Sel.mat

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1. Introducción. La incertidumbre es un tema de estudio en los diversos tipos de sistemas, debido a que siempre se producen cambios que alteran su comportamiento, en particular en los sistemas relacionados con problemas de scheduling, la incertidumbre en este tipo de problemas se puede presentar por ejemplo en: interrupciones o caídas de máquinas, cancelación de pedidos, etc., los cuales hacen que la programación de tareas sea más compleja. Con el fin de solucionar o al menos traten de mitigar los efectos producidos por estos cambios, se proponen diferentes métodos.Los dos elementos clave de la programación son: la generación de schedules y la revisión del schedule [8,30]. La generación de schedules actúa como un mecanismo de predicción que determina la programación de los tiempos de inicio y de finalización de las tareas de producción en base a los requisitos y restricciones dadas antes del proceso de producción. La revisión de scheduling es una parte reactiva, que supervisa la ejecución de lo previsto y se ocupa de acontecimientos inesperados. Los enfoques de programación se pueden dividir en: scheduling On-line y scheduling Off-line, [16]. En el scheduling off-line, todos los trabajos disponibles se programan a la vez para todo el horizonte de planificación, mientras que para el scheduling On-line se toman decisiones en el momento en que se necesitan.Para tratar la incertidumbre en los problemas de scheduling, se tiene dos enfoques, el scheduling reactivo y el scheduling proactivo. El scheduling reactivo es un proceso para modificar la programación

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“…Inventory must be enough to meet the demand and be cost effective (Petrovic and Duenas, 2006). There are three main types of inventory systems : a continuous inventory system (fixed order quantity), a periodic one (fixed time period : weekly or monthly) and an inventory system that combines the first two types (Bălan, 2006).…”

Section: Introductionmentioning

confidence: 99%

An Analysis Regarding the Possibility of Using Fuzzy Logic in Inventory Management

Stoia¹

2014

ACTA Universitatis Cibiniensis

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A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions

Cited by 78 publications

References 28 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Scheduling With Uncertainty

An Analysis Regarding the Possibility of Using Fuzzy Logic in Inventory Management

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