Evaluating the impact of operating environments on MRP system nervousness

Ho, Chrwan-Jyh

doi:10.1080/00207548908942611

Cited by 134 publications

(67 citation statements)

References 6 publications

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“…Durieux et Pierreval (2003) indiquent que le terme stabilité est généralement mis en opposition à celui de nervosité. Stabilité et nervosité de la planification sont souvent traitées dans la littérature au niveau du calcul des besoins (MRP) (Yano et Carlson, 1987 ;Ho, 1989 ;Sridharan et Laforge, 1990 ;Jensen, 1993 ;Heisig, 1998). Calculer un programme directeur de production (PDP) revient à mettre en place un horizon glissant, ce qui implique de le recalculer avec une périodicité ∆ ; par conséquent, ce type de pratique induit des variations dans les quantités de production résultantes.…”

Section: Objectifs De L'analyse : Mesurer La Stabilitéunclassified

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Trojet¹,

H'Mida²,

López³

et al. 2016

Journal Européen des Systèmes Automatisés

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Section: Objectifs De L'analyse : Mesurer La Stabilitéunclassified

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Trojet¹,

H'Mida²,

López³

et al. 2016

Journal Européen des Systèmes Automatisés

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show abstract

“…Another similar classification has been presented by Yjin and Branke (2005) which divided uncertainties into four categories, included noise in fitness functions, search for robust solutions, approximation error in the fitness function, and fitness functions changing over time. Also, another classification was proposed by Ho (1989) for production processes that divided uncertainty into two groups. First, an environmental uncertainty which includes uncertainties related to the process of production such as demand or supply uncertainty.…”

Section: Different Sources Of Uncertaintymentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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“…In the real world, there are many forms of uncertainty that affect production processes. Ho (1989) categorizes them into two groups: (1) environmental uncertainty and (2) system uncertainty. Environmental uncertainty includes uncertainties beyond the production process, such as demand uncertainty and supply uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Determining production level under uncertainty using fuzzy simulation and bootstrap technique, a case study

et al. 2017

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In every production plant, it is necessary to have an estimation of production level. Sometimes there are many parameters affective in this estimation. In this paper, it tried to find an appropriate estimation of production level for an industrial factory called Barez in an uncertain environment. We have considered a part of production line, which has different production time for different kind of products, which means both environmental and system uncertainty. To solve the problem we have simulated the line and because of the uncertainty in the times, fuzzy simulation is considered. Required fuzzy numbers are estimated by the use of bootstrap technique. The results are used in production planning process by factory experts and have had satisfying consequences. Opinions of these experts about the efficiency of using this methodology, has been attached.

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Evaluating the impact of operating environments on MRP system nervousness

Cited by 134 publications

References 6 publications

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Determining production level under uncertainty using fuzzy simulation and bootstrap technique, a case study

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