Data-Mining Approach to Production Control in the Computer-Integrated Testing Cell

Kwak, Choonjong; Yih, Yuehwern

doi:10.1109/tra.2003.819595

Cited by 37 publications

(36 citation statements)

References 28 publications

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“…Table 4 lists the predictors used in the experiments. The selection of these predictors is generally on the basis of earlier research (see for example, [35,54,55]). It is acknowledged though that a simple attribute-selection model is not capable to generate and guarantee even near-optimal subset of predictors.…”

Section: Methodsmentioning

confidence: 99%

“…The entire learning process significantly suffers with the poor selection and inappropriate creation of the predictors. It is the task of finding the most reasonable subset of predictors for a classifier to seek fewer predictors and maximum class separability [35]. This process is also critical for the effectiveness of the subsequent model induction by eliminating certain redundant and irrelevant predictors.…”

Section: Learning Modulementioning

confidence: 99%

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Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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Abstract:A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

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Section: Methodsmentioning

confidence: 99%

Section: Learning Modulementioning

confidence: 99%

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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“…Traditional machine learning (ML) approaches for knowledge acquisition in manufacturing started to gain much attention only in recent years [3][4][5][6][7][8][9][10], mostly because the majority of the ML algorithms and tools require skilled individuals to understand the output of ML process [3]. However there has been some work on using traditional ML techniques for specific areas (such as fault detection, quality control, maintenance, engineering design, etc.)…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

“…However there has been some work on using traditional ML techniques for specific areas (such as fault detection, quality control, maintenance, engineering design, etc.) employing classification [6,7], clustering [8] and association rule mining [9,10] algorithms [3][4][5]. Classification algorithms were used for categorising data into different classes, for example classifying defects in the semi-conductor industry [5].…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

A Knowledge Dashboard for Manufacturing Industries

Mazumdar

Varga

Lanfranchi

et al. 2012

Lecture Notes in Computer Science

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Abstract. The manufacturing industry offers a huge range of opportunities and challenges for exploiting semantic web technologies. Collating heterogeneous data into semantic knowledge repositories can provide immense benefits to companies, however the power of such knowledge can only be realised if end users are provided visual means to explore and analyse their datasets in a flexible and efficient way. This paper presents a high level approach to unify, structure and visualise document collections using semantic web and information extraction technologies.

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“…Liao et al [64] discussed a multi-layer perceptron neural network to model radiographic welding data. Kwak and Yin [65] presented a data-mining based production-control system for testing and rework in dynamic CIM. Their system analyses the present situation and suggests dispatching rules to be followed and also how data mining can be used to evaluate the effect of those decisions.…”

Section: False Recognition/incorrect Classification Is a Frequently Ementioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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Data-Mining Approach to Production Control in the Computer-Integrated Testing Cell

Cited by 37 publications

References 28 publications

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

A Knowledge Dashboard for Manufacturing Industries

Data mining in manufacturing: a review based on the kind of knowledge

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