Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems

Fowler, John W.; Rose, Oliver

doi:10.1177/0037549704044324

Cited by 191 publications

(112 citation statements)

References 22 publications

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“…In their survey, Taylor and Robinson [23] state that there is a need for higher level modeling techniques that abstract away from low-level model detail to justify the development of a detailed model. Furthermore, Fowler and Rose [24] state that reducing problem solving cycles is a grand challenge in modeling and simulation of complex manufacturing systems. Abstract models such as the aggregate model proposed in the current paper may be helpful in these respects.…”

Section: Discussionmentioning

confidence: 99%

Predicting Cycle Time Distributions for Integrated Processing Workstations: An Aggregate Modeling Approach

Veeger

Etman

Lefeber

et al. 2011

IEEE Trans. Semicond. Manufact.

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Predicting the cycle time distribution as a function of throughput is helpful in making a trade-off between workstation productivity and meeting due dates. To predict cycle time distributions, detailed models are almost exclusively used, which require considerable development and maintenance effort. Instead, we propose a so-called aggregate model to predict cycle time distributions, which is a lumped-parameter representation of the queueing system. The lumped parameters of the model are determined directly from arrival and departure events measured at the workstation. The paper demonstrates that the aggregate model can accurately predict the cycle time distribution of workstations in semiconductor manufacturing, in particular the tail of the distribution.

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Section: Discussionmentioning

confidence: 99%

Predicting Cycle Time Distributions for Integrated Processing Workstations: An Aggregate Modeling Approach

Veeger

Etman

Lefeber

et al. 2011

IEEE Trans. Semicond. Manufact.

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“…The objective function could be composed of many criteria as the respect of the date for each manufacturing order, minimization of the number of pallets used, load-balancing of workstations, etc. [Valckenaers et al, 2005, Fowler andRose, 2004].…”

Section: Research Applicationsmentioning

confidence: 99%

An Experimental Platform for e-Manufacturing and Advanced Control

Fontanili¹,

Oudenhove²

2008

IFAC Proceedings Volumes

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In this paper, we present an experimental platform for education and research purposes. The physical system of this platform is composed of a manufacturing workshop (physical plant) remotely controlled based upon Internet. The information system is composed of three Web applications allowing to order products (ecommerce), plan production campaign (e-plan) and control the workshop in order to release production (e-mes). We first describe an education application for engineer students to learn them how to control several production units (eventually on different sites). This platform would also be used as a demo for research works in the field of industrial engineering.

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“…Relatively, ontology-based modelling is a well-known approach to support both knowledge integration and interoperability between information technology systems during collaborative business processes [96,98]. Moreover, to cope with the complexity of engineering knowledge [96,99] in the sustainability assessment domain, ontology design is proposed as a promising approach. Modular ontology development implies that rather than having an enormous ontology to cover a given domain, it is necessary to abstract and generalize concepts into separate ontologies.…”

Section: Knowledge Engineering For the Sustainability Assessment Domainmentioning

confidence: 99%

An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain

Konys

2018

Sustainability

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Sustainability assessment has received more and more attention from researchers and it offers a large number of opportunities to measure and evaluate the level of its accomplishment. However, proper selection of a particular sustainability assessment approach, reflecting problem properties and the evaluator's preferences, is a complex and important issue. Due to an existing number of different approaches dedicated to assessing, supporting, or measuring the level of sustainability and their structure oriented on the particular domain usage, problems with accurate matching frequently occur. On the other hand, the efficiency of sustainability assessment depends on the available knowledge of the ongoing capabilities. Additionally, actual research trends confirm that knowledge engineering gives a method to handle domain knowledge practically and effectively. Unfortunately, literature studies confirm that there is a lack of knowledge systematization in the sustainability assessment domain, however. The practical application of knowledge-based mechanisms may cover this gap. In this paper, we provide formal, practical and technological guidance to a knowledge management-based approach to sustainability assessment. We propose ontology as a form of knowledge conceptualization and using knowledge engineering, we make gathered knowledge publicly available and reusable, especially in terms of interoperability of collected knowledge.

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Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems

Cited by 191 publications

References 22 publications

Predicting Cycle Time Distributions for Integrated Processing Workstations: An Aggregate Modeling Approach

Predicting Cycle Time Distributions for Integrated Processing Workstations: An Aggregate Modeling Approach

An Experimental Platform for e-Manufacturing and Advanced Control

An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain

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