سیستم تخصصی فازی برای مدیریت کسب و کار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|11886||2010||11 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||12 روز بعد از پرداخت||708,300 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||6 روز بعد از پرداخت||1,416,600 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 12, December 2010, Pages 7570–7580
Nowadays firms are required to reach high levels of specialisation in order to increase their competitiveness in complex markets. Knowledge management plays a fundamental role in this process as the correct implementation of strategies is determined by the information transfer and dissemination within the organisation. In this paper, a fuzzy expert system focused on increasing accuracy and quality of the knowledge for decision making is designed. A model based on fuzzy rules to simulate the behavior of the firms, is presented under the assumption of determined input parameters previously detected and an algorithm is developed to achieve the minimal structure of the model. The result is a fuzzy expert system tool, called ESROM, which gives valuable information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be adapted to different scenarios.
Nowadays, firm managers are becoming aware of the need for information analysis tools in order to support business decisions in the current complex and turbulent business environment (Handfield & Melnyk, 1998). Competition in changing environments due to fast progression of technical advances turns competition on information into the main competitive parameter in order to prevent and anticipate changes in customer needs, technology, industry trends and other competition parameters (Wacker, 1998). Therefore, the evolution of business computing networking and client/server architectures are impelling utilization of shared information in a decision support context. In this context, the use of Decision Support Systems (DSS) is increasing and becoming generalized (Hasuike and Ishii, 2009, Inaad, 2009, Sharma et al., 2010 and Vigier and Terceño, 2008). Even though, the development of new algorithms involves a fast progression in accuracy of DSS (Demoulin, 2007 and Wen et al., 2008), the use of new DSS techniques has been scarcely applied in the field of Operations Management (OM) (Garbolino and Taroni, 2002 and Lotfi and Pegels, 1996). In fact, even though management information systems literature has broadly dealt with tools to assist in managerial decisions, the wide utility these systems generate for specific Operations Management (OM) decisions is not still generalized (Stenforsa et al., 2007). However, the use of surveys based on questionnaires in OM research is widely extended for academics and practitioners in order to define constructs, dimensions and variables to enhance understanding of OM issues (Wacker, 1998). Statistical multivariable techniques have been intensively applied in empirical studies with different levels of reliability and validity (O’Leary-Kelly & Vokurka, 1998). Different studies have analyzed the relationship between operations strategy and performance through the use of statistical analysis, as we can see in Arias-Aranda, 2002, Arias-Aranda, 2003 and Arias-Aranda et al., 2001. These studies analyze the relationship between operations strategy and performance through flexibility as a moderating variable within the service setting of engineering consulting firms in Spain. Artificial Intelligence (AI) techniques are also used in this field, like Bayesian classifiers (Abad-Grau & Arias-Aranda, 2006), expert systems (Miah et al., 2009 and Shiue et al., 2008), case-base reasoning (Li and Ho, 2009 and Lin et al., 2007) and so on. Under these conditions, the aim of this research is to combine two different approaches: the use of surveys based on questionnaires in OM research with current techniques of AI. In this paper we develop a fuzzy modelling mechanism which is capable of implementing four objectives: (i) representing the knowledge obtained in terms of natural language, (ii) expressing the results obtained from the questionnaires analysis in a way that can be easily understood by non-experts users through fuzzy logic, (iii) generating a rule base automatically from numeric and linguistic data, (iv) acting as simulator of output results according to different input conditions controlled by the user. In order to achieve these objectives, this paper proposes a fuzzy expert systems, called ESROM, which will help to manager to make decisions about the company by means of simulating actual situations. The rest of the paper is organized as follows: Section 2 provides some preliminaries on the fundamental theoretical aspects underlying this paper: Operations Management and Expert Systems. In Section 3, the case study is presented. In Section 4, an automatic learning algorithm applied to a determined relationship of OM variables will be suggested. In Section 5, an algorithm to obtain the minimal sets of rules to make more understandable and efficient the system will be presented. In Section 6, the expert system tool (ESROM) will be described and a simulation of the previously generated based system fuzzy rules will be done. Finally, in Section 7 presents the conclusions and future research.
نتیجه گیری انگلیسی
In this paper, a new tool (ESROM) based on fuzzy logic to improve the knowledge of the relationships between variables has been developed. This tool has been applied to a real Operations Management research and it can help managers to simulate strategic environments to obtain valuable information about levels of strategy, flexibility and performance required in the operations management area. ESROM allows to the users modify the generated rules when new knowledge is acquired in the firm. In addition, linguistic interpretation of the knowledge let users easily comprehend relationship especially compared to statistical methods. Therefore, a high number of input variables can be easily interpreted through the visual information provided by ESROM. ESROM, in this first version still has some limitations. The results obtained through the simulation depend on the data set for learning. Hence, the more real management data are included, the more accurate the results will be. Rules are generated on the basis of learned examples. When new non-previously learned samples are introduced, sometimes not any rule is fired, so an interpolated result is obtained as the input space of those data is not covered by the samples used to build the model. When the rules are fired producing a direct result. In any case, ESROM allows experts to add or remove rules in order to increase accuracy of the results. In order to avoid a big number of rules generated by ESROM, in this paper we also develop a minimal algorithm called, the consequent driven algorithm. This algorithm minimizes the original rules set into a equivalent minimal one. For future research: (i) we will improve the learning algorithm, (ii) techniques based on soft computing such as genetic algorithms or neurofuzzy systems will be included to adjust or learn some parameters related to the learning process as belonging functions, structure of rules and so on, in order to make less dependent to the system of expert’s knowledge. These parameters are especially important in the final stages of development of the system.