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Kouiss, Khalid; Pierreval, Henri; Mebarki, Nasser

doi:10.1023/a:1018540317470

Cited by 110 publications

(5 citation statements)

References 17 publications

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“…The learning module and critical module of an agent react to improve the actions and the performance standards by interacting with each other. Furthermore, the problem generator module enforces the exploratory efforts of the agent to develop a more appropriate world view (Russell and Peter, 1995;Khalid et al, 1997;Monostori, 2003;Lee and Kim, 2008;Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Tripathi

Muhr

Manuel

et al. 2021

Front. Artif. Intell.

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The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. These issues need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.

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Section: Introductionmentioning

confidence: 99%

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Tripathi

Muhr

Manuel

et al. 2021

Front. Artif. Intell.

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“…Then, a simple heuristic dispatching rule, called a shift from standard rules, was designed by Pierreval and Mebarki (1997). The same year, Kouiss K and Pierreval H (1997) first proposed a scheduling strategy based on a multi-agent for a dynamic job shop. In recent decades, more and more dynamic scheduling methods have been put forward to cope with the exceptional events in the dynamic scheduling system (Aytug et al, 2005;Ouelhadj and Petrovic, 2009).…”

Section: Real-time Schedulingmentioning

confidence: 99%

Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods

Wang

Yang

Zhang

et al. 2020

Journal of Cleaner Production

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Recently, software agents have been applied to cope with the dynamic scheduling problems [12][13][14]. Agent Technology has received a great deal of attention among the researchers and practitioners in the field of Artificial Intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Timetabling Using Reactive Constraint Agents

Alashwal¹,

Deris²

2012

Jurnal Teknologi

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Kebanyakan teknik yang diimplimentasi bagi menyelesaikan masalah penjadualan tertumpu kepada proses yang statik. Walau bagaimanapun, di dalam dunia sebenar, masalah penjadualan merupakan satu masalah yang terbuka, dinamik dan sentiasa berubah–ubah mengikut kekangan dan andaian. Oleh yang demikian, objektif utama kertas ini adalah untuk mengendalikan perubahanperubahan yang berlaku setelah jadual waktu awalan terhasil. Agen Kekangan Reaktif (AKR) telah diimplimentasi lebih khusus dan berkeupayaan membaiki dan mengubahsuai jadual waktu secara bertahap dengan komunikasi dan kerjasama di antara satu sama lain bagi mengekalkan kesauran jadual waktu tersebut. Seni bina AKR ini telah dilaksana dan diuji dengan menggunakan data sebenar iaitu data dari Fakulti Sains, Universiti Ibb, Yemen. Hasil kajian menunjukkan bahawa AKR berupaya mengendalikan perubahan–perubahan dalam masa nyata dengan pembaikan yang minimum ke atas jadual waktu asal. Kata kunci: Masalah penjadualan waktu, penjadualan waktu dinamik, pengaturcaraan terhad, agen perisian, seni bina agen terbuka Most of the approaches that have been applied to solve the timetabling problems focus on the construction of the timetable as a static process. In real world, the timetabling problems are dynamic and open problems since the initial timetable is not fixed and it is required to be changed as the constraints or assumptions on which the timetable is based on, are changed or became invalid. Therefore, the main objective of this paper is to handle the changes after generating the initial timetable. The Reactive Constraint Agents (RCA) architecture is capable of repairing and modifying the timetable gradually by communicating and cooperating with each other to maintain the timetable feasibility. This architecture has been implemented and tested using real data from Faculty of Science, University of Ibb – Yemen. The results show that the RCA can cope with the changes in real–time with minimal modification to the existing timetable. Key words: Timetabling problem, dynamic timetabling, constraints programming, software agents, open agent architecture

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Cited by 110 publications

References 17 publications

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods

Dynamic Timetabling Using Reactive Constraint Agents

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