Soft Computing in decision modeling

Torra, Vicenç; Narukawa, Yasuo

doi:10.1007/s00500-009-0456-7

Cited by 13 publications

(2 citation statements)

References 15 publications

(10 reference statements)

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“…Soft computing [6,19,21,32,34,61,80,111] is a collection or set of computational techniques in machine learning [1,4,42,86], such as artificial neural networks [3,7,8,11,12,13,15,40,43,47,48,49,31,55,56,57,64,77,78,79,83,101,104,109], genetic algorithms [9,10,16,22,23,26,29,60,67,70,72,73,95,96,97,98],...…”

Section: Introductionmentioning

confidence: 99%

Optimising operational costs using Soft Computing techniques

Sedano

Berzosa

Villar

et al. 2011

ICA

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A Manufacturing Execution System (MES) consists of high-cost, large-scale, multi-task software systems. Companies and factories apply these complex applications for the purposes of production management to monitor and track all aspects of factory-based manufacturing processes. Nevertheless, companies seek to control the production process with even greater rigour. Improvements associated with an MES involve the identification of new knowledge within the data set and its integration in the system, which implies a step forward to Business Process Management (BPM) systems, from which the users of an MES may gain relevant information, not only on execution procedures but to decide on the best scheduled arrangement. This work studies the data gathered from a real MES that is used in a plastic products factory. Several Artificial Intelligence and Soft Computing modelling methods based on fuzzy rules assist in the calculation of manufacturing costs and decisions over shift work rotas: two decisions that are of relevance for the improvement of the execution system. The results of the study, which identify the most suitable models to facilitate execution-related decision-making, are presented and discussed

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Section: Introductionmentioning

confidence: 99%

Optimising operational costs using Soft Computing techniques

Sedano

Berzosa

Villar

et al. 2011

ICA

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“…In this interdisciplinary study, such a model is obtained using a combination of conventional and soft computing models. Soft computing (Cruz & Pelta, ; Corchado et al ., ; Torra & Narukawa, ) is a collection or set of computational techniques in machine learning (Abraham et al ., ), such as artificial neural networks (Kohonen, ; Herrero et al ., ), genetic algorithms (Lorena & de Carvalho, ; Naldi et al ., ), fuzzy systems (Zadeh, ; Berlanga et al ., ) and swarm intelligence (Das et al ., ), which investigate, simulate and analyse very complex issues and phenomena.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

et al. 2011

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This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, copper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques.

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Introduction

Sirbiladze

2012

Extremal Fuzzy Dynamic Systems

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Soft Computing in decision modeling

Cited by 13 publications

References 15 publications

Optimising operational costs using Soft Computing techniques

Optimising operational costs using Soft Computing techniques

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

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