A fuzzy approach to using expert knowledge for tuning paper machines

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A fuzzy approach to using expert knowledge for tuning paper machines. / Mezei, Jozsef; Brunelli, Matteo; Carlsson, Christer.

In: Journal of the Operational Research Society, Vol. 68, No. 6, 06.2017, p. 605-616.

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Mezei, Jozsef ; Brunelli, Matteo ; Carlsson, Christer. / A fuzzy approach to using expert knowledge for tuning paper machines. In: Journal of the Operational Research Society. 2017 ; Vol. 68, No. 6. pp. 605-616.

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@article{63fa572d7b9a4158874bdb16843a9431,
title = "A fuzzy approach to using expert knowledge for tuning paper machines",
abstract = "Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.",
keywords = "paper machines, fuzzy ontology, multiobjective optimization, goal programming, possibilistic chance programming, soft computing, OPTIMIZATION, INTEGRATION, PREFERENCE, INDUSTRY, SYSTEMS, DESIGN",
author = "Jozsef Mezei and Matteo Brunelli and Christer Carlsson",
year = "2017",
month = "6",
doi = "10.1057/s41274-016-0105-3",
language = "English",
volume = "68",
pages = "605--616",
journal = "Journal of the Operational Research Society",
issn = "0160-5682",
number = "6",

}

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TY - JOUR

T1 - A fuzzy approach to using expert knowledge for tuning paper machines

AU - Mezei, Jozsef

AU - Brunelli, Matteo

AU - Carlsson, Christer

PY - 2017/6

Y1 - 2017/6

N2 - Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.

AB - Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.

KW - paper machines

KW - fuzzy ontology

KW - multiobjective optimization

KW - goal programming

KW - possibilistic chance programming

KW - soft computing

KW - OPTIMIZATION

KW - INTEGRATION

KW - PREFERENCE

KW - INDUSTRY

KW - SYSTEMS

KW - DESIGN

U2 - 10.1057/s41274-016-0105-3

DO - 10.1057/s41274-016-0105-3

M3 - Article

VL - 68

SP - 605

EP - 616

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

IS - 6

ER -

ID: 13679872