14,811 research outputs found

    Integration of decision support systems to improve decision support performance

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    Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

    Using COTS Search Engines and Custom Query Strategies at CLEF

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    This paper presents a system for bilingual information retrieval using commercial off-the-shelf search engines (COTS). Several custom query construction, expansion and translation strategies are compared. We present the experiments and the corresponding results for the CLEF 2004 event

    One-Class Classification: Taxonomy of Study and Review of Techniques

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    One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

    Development of a decision support system for decision-based part/fixture assignment and fixture flow control = Ukusungulwa kohlelo lokuxhaswa kwezinqumo mayelana nokwabiwa kwezingxenye ezakhiwayo kanye nokuhanjiswa kwazo.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.ABSTRACT: An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach. Keywords: Decision support systems, case-based reasoning, analytic hierarchy process, fuzzy set theory, object-oriented methods, discrete-event simulation, fixtures. IQOQA LOCWANINGO : Ukuncintisana okunezinhlelo eziguquguqukayo kulesi sikhathi samanje sekwenze ukuthi kube nezidingo ezintsha ezinhlelweni zokukhiqiza. Phakathi kwakho konke lokhu izingxenye (fixtures) zingezinye zezinto ezidala izinkinga. Intengo yokwakha uhlaka lwengxenye kanye nokuyikhiqiza kubiza amaphesenti ayi-10 kuya kwangama-20 entengo yokukhiqiza. Amafemu akhiqizayo avamise ukusebenzisa izindlela ezindala zomsebenzi wokwaba izingxenye. Lezi zindlela zidla kakhulu izinsizangqangi futhi kuthatha isikhathi eside ukubala izingxenye ezikhona nokuqinisekisa ukuthi kunesibalo esanele kulokho okumele kube yikho ohlelweni lokusebenza. Inhloso yalolu cwaningo bekungukucwaninga nokusungula i-Decision Support System (DSS) ebe lusizo ekwenzeni umsebenzi wokuthatha izinqumo ngokwabiwa kwezingxenye kanye nokuhanjiswa kwazo ngezikhathi ezimiselwe ukukhiqiza. I-DSS yakhelwa ukusiza labo abayisebenzisayo ukuze basebenzise noma bazisebenzise lapho zingakaze zisetshenziswe khona lezo zingxenye ezibuyisiwe, noma kwakhiwe ezintsha kuya ngokuthi zibuyiswe zinjani lezi ezibuyisiwe nokuthi ziyafana yini nalezo ezintsha. I-DSS isebenzise amasu ahlanganise i-Case-Based Reasoning (CBR), injulalwazi echazwa ngokuthi i-fuzzy, ne-Analytic Hierarchy Process (AHP) ne-Discrete-Event Simulation (DES). I-Artificial Intelligence (AI) eyingxenye ye-DSS isebenzise kakhulu uhlelo lwe-fuzzy CBR luhlangene ne-fuzzy AHP kulandelwa imithetho yolwazi olumayelana nohlobo lomsebenzi. I-CBR isetshenziswe ukumelela lezo zimo zamanani ezingaqondakali nezingaphelele kulezo zingxenye. I-AHP e-fuzzy yasetshenziswa ukuze kutholakale ulwazi kochwepheshe olubeka phambili lezo zingxenye. Ama-oda ezingxenye ezintsha kanye namasampuli asetshenziselwa ukuqeqesha avezwe njengamasha kanye nabekade evele ekhona ngokulandelana kusetshenziswa indlela eyaziwa ngokuthi yi-Object-Oriented (OO) method lapho kubuyiswa izinto noma kunezinqumo eziphakanyiswayo. Izindlela ezijwayelekile zokulandelanisa nokufanisa zisetshenziswe ohlelweni lokubuyisa izinto. Kusetshenziswe isu eliyi-DES ukuhlaziya ukusebenza kwezisombululo eziphakanyiswe yindlela ye-CBR e-fuzzy. Le ndlela iphinde yaveza izimo ezintathu eziphakanyiswe ukuba zibe yisisombululo esibalweni sezingxenye ezihlongozwayo. Ukusebenza kwalezi zimo kuhlungwe ngokusebenzisa indlela ye-DES kwase kuvela inqubo engcono. Ukungajwayeleki kwalolu cwaningo kusebenzise ingxube yezindlela ze-fuzzy CBR ne-DES ngoba lolu hlobo lwengxube belungakaze lusetshenziswe. Kusetshenziswe isibonelo sezibalo ekwethuleni ukusebenza kwale nqubo yokusebenza ehlongozwayo

    Development of a decision support system for decision-based part/fixture assignment and fixture flow control.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach.Please refer to the PDF for author's keywords

    CBR and MBR techniques: review for an application in the emergencies domain

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    The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
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