fuzzy inference system
در نشریات گروه صنایع-
مدیریت پروژه به دنبال روش هایی برای پایش انحراف پروژه است. در پژوهش حاضر، انحراف پروژه با ارزیابی هم زمان انحراف هزینه، زمان، و کیفیت پایش می شود. اهمیت عوامل و اهم ریسک های پروژه در مصاحبه از خبرگان یک شرکت پیمانکاری شناسایی شده است. با روش تحلیل حالت های بالقوه ی خطا و آثار آن ها و منطق فازی، ارزیابی ریسک انجام شده است. همچنین، با استفاده از روش سیستم استنتاج فازی و شبکه های بیزی، انحراف پروژه با وجود وابستگی بین ریسک ها، کنترل و از معیار میانگین مربعات خطا برای بررسی اعتبار مدل ها استفاده شده است. با مقایسه ی درصد انحراف واقعی 15 پروژه ی اجراشده در شرکت مذکور با درصد انحراف برآوردی مدل ها، میانگین مربعات خطا در روش استنتاج فازی نسبت به روش شبکه ی بیزی کمتر به دست آمده و روش استنتاج فازی با میانگین مربعات خطا معادل با 0011/0 نسبت به روش شبکه ی بیزی کاراتر بوده است.
کلید واژگان: مدیریت پروژه، انحراف، حالت های بالقوه ی خطا و آثار آن ها، سیستم استنتاج فازی، شبکه ی بیزیProject management is always looking for ways to complete the project on time, quality, and cost according to the project contract. Due to the existence of various risks and increased uncertainty in business environments, a high percentage of the projects have deviations when compared with the base plan. The purpose of this research is to continuously monitor the deviation of the project by evaluating the deviation of cost, time, and quality simultaneously under the conditions of uncertainty. By conducting a pairwise comparison between cost, time, and quality factors and interviewing experts of a contractor company, the relative importance of these factors has been determined. The most important risks of the project have been identified by interviewing experts in the contractor company. The risk assessment has been carried out with the failure mode and effect analysis and fuzzy logic method. By using the approach of fuzzy inference system and Bayesian networks, project deviation is predicted. In the fuzzy inference system, project risks are considered as input variables in the form of triangle fuzzy number and project deviation is obtained as the output variable of the cohesive fuzzy inference system in the Matlab software. In the Bayesian network approach, the initial and conditional probabilities of the nodes have been obtained by using the experts' opinion and the project deviation has been investigated using the network between risks in AgenaRisk software. To estimate the validity of the results of the models, the mean square error criterion was used. By comparing the actual deviation percentage of projects implemented in the mentioned company with the estimated deviation percentage of the models, the mean squared error in the fuzzy inference method is less compared to the Bayesian network method, and the fuzzy inference method with the mean squared error equal to 0.0011 is more efficient than the Bayesian network method.
Keywords: Project Management, Deviation, Failure Mode, Effect Analysis, Fuzzy Inference System, Bayesian Network -
International Journal of Industrial Engineering and Productional Research, Volume:36 Issue: 1, Mar 2025, PP 195 -212
One of the most important issues regarding community health is animal health, followed by the health of animal products. Providing a sustainable environment for production facilities like livestock centers is essential. In this study, we have proposed designing four fuzzy inference systems for managing the sustainability of livestock centers. The first, second, and third systems are applied for the economic, social, and environmental dimensions. The fourth is for a system whose output is the sustainability level while its inputs are the three addressed sustainability dimensions. The data source was experts' judgment, and the major limitation of this research was access to a limited number of experts in making system rules. The validation is made by cross-checking with other experts. Considering a maximum of 10 points for each sustainability dimension and supposing that the economic dimension is 5.05, the social dimension is 7.77 and the environmental dimension is 8.12, the sustainability level turns out to be 7.92
Keywords: Sustainability Measurement, Fuzzy Inference System, Environmental Dimension, Social Dimension -
امروزه تمامی سازمان ها به نوعی در معرض تحولات فناوری اطلاعات قرار دارند و جلوه های کاربرد فناوری اطلاعات و ارتباطات در کلیه حوزه های زنجیره تامین آشکار است. این پژوهش به بررسی تاثیر میل به همکاری و زیرساخت تکنولوژی شرکای زنجیره تامین بر اشتراک اطلاعات با مطالعه موردی کارخانه های لبنیات و با استفاده از سیستم استنتاج فازی می پردازد. پژوهش حاضر کمی و پیمایشی باهدف کاربردی انجام گرفته است و بر اساس مدل مفهومی در نظر گرفته شده با شاخص های کیفی و مبهم، با استفاده از توابع عضویت فازی به صورت کمی درآمده و پس از اعمال استنتاج فازی نتیجه فازی حاصله نیز به صورت کمی دی فاز شده است. در تحلیل فازی این پژوهش از نرم افزار متلب استفاده شده است و قوانین استنتاج آن نیز با 28 قاعده حاصل از نظر خبرگان تعیین شده است. روش ارزش دهی به شاخص های هر متغیر کیفی در این پژوهش با استفاده از پرسش نامه های تایید شده در پژوهش های معتبر می باشد که بر اساس فرمول کوکران به تعداد 100 پرسش نامه در شرکت های تولیدی شهرک صنعتی شهرکرد توزیع شده است، به دست آمده است.
کلید واژگان: زنجیره تامین، همکاری، اشتراک اطلاعات، تکنولوژی، سیستم استنتاج فازی.Today, all organizations are somehow exposed to information technology developments, and the effects of information and communication technology application are obvious in all areas of the supply chain. This research examines the effect of cooperation willingness and technology infrastructure of supply chain partners on information sharing with a case study of dairy factories and using fuzzy inference system. The current quantitative and survey research has been carried out with an applied purpose, and based on the conceptual model considered with qualitative and vague indicators, it has been quantified using fuzzy membership functions, and after applying fuzzy inference, the resulting fuzzy result has been quantitatively de-phased. In the fuzzy analysis of this research, MATLAB software was used, and its inference rules were determined with 28 rules obtained from the opinion of experts. The method of valuing the indicators of each qualitative variable in this research is obtained by using questionnaires approved in reliable researches, based on Cochran's formula, 100 questionnaires were distributed in the manufacturing companies of Shahrekord Industrial Town.
Keywords: Supply Chain, Cooperation, Information Sharing, Technology, Fuzzy Inference System -
Journal of Industrial Engineering and Management Studies, Volume:9 Issue: 2, Summer-Autumn 2022, PP 33 -44Nowadays, not only improving service levels is not sufficient for consumer satisfaction, but also, the consumers themselves determine product or service quality. In other words, we can interpret quality as "the degree of accordance with the consumer's need." Therefore, we should look for solutions to identify consumers' needs and requirements for applying them in the design and development of the product or service. One of these methods is the Kano model. This model shows the decision maker if any of the consumers' requirements are in the product/service or not and how much it will affect their satisfaction. This tool classifies consumers' needs for converting them to design requirements. But, human mentality and behavior always are accompanied by uncertainties. Linguistic variables or fuzzy numbers have been used in the literature to overcome this defect. Researchers developed the fuzzy Kano's model using this method and enhanced the model's efficiency compared to the deterministic one. The efficiency of this model has increased compared with the deterministic one. However, the decision-makers are unsure how to classify customers' needs using this strategy. This research uses a Fuzzy Inference System (FIS) to tackle this challenge. The essential contribution is developing a fuzzy Kano's model based on FIS for consumer requirements analysis. A case study from the restaurant industry in Yazd city of Iran was considered to validate the proposed model. The results show the superior performance of the proposed model compared with fuzzy Kano's model in recognizing consumers' needs.Keywords: Kano’s model, Fuzzy theory, fuzzy Kano’s model, Fuzzy inference system
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Journal of Optimization in Industrial Engineering, Volume:13 Issue: 28, Summer and Autumn 2020, PP 267 -278Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed to design a risk management model that seeks to manage the risk of software development projects based on the key criteria of project time, cost, quality and scope. This is presented after making an extensive review of the literature and asking questions from experts in the field. In this regard, after identifying the risks and defining them based on the dimensions and indicators of software development projects, 22 features were identified to evaluate banking software projects. The data were collected for three consecutive years in the country's largest software development eco-system. According to Rough modelling, the most important variables affecting the cost, time, quality and scope of projects were identified and the amount of risk that a project may have in each of these dimensions was shown. Since traditional scales cannot provide the accurate estimation of project risk assessment under uncertainty, the indexes were fuzzy. Finally, the fuzzy expert system was designed by MATLAB software that showed the total risk of each project. To create a graphical user interface, the MATLAB software GUIDE was used. The system can predict the risks of each project before each project begins and helps project managers be prepared to deal with these risks and consider ways to prevent the project from failing. The results showed that quality and time risks were more important than cost and scope risks and had a greater impact on total project deviation.Keywords: Project Risk Management, Software Development, Expert systems, Rough Set Theory, fuzzy logic, Fuzzy Inference System
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Developing a fuzzy inference system to devise proper business strategies: a study on carpet industry
The present article formulates the scenarios that the organization will be probably facing with, using the uncertain factors in business environment, and it also selects the most robust strategies of organization for dealing with the formulated scenarios using the fuzzy information expressed by the experts in fuzzy inference system. The present article aims to provide a method enabling the scenario programmers to employ robustness philosophy using the scenario planning potentials and fuzzy inference system at the decision-making stage of the general process of strategy formulation. The process helps the strategic managers of the organizations to determine their business future clearly and enables them to select their robust scenario in the current market that is uncertain. After the introduction of the robust strategic planning methodology and illustrating its different stages, the selected strategies of them will be compared at the end of the article by implementing the strategic planning method in a practical case. The results of the research have been examined as a case study for carpet industry.
Keywords: Fuzzy inference system, Robust scenario planning, Strategic management, Success factors of organization -
Natural disasters such as earthquakes have a destructive impact on urban infrastructures and their performance. Due to the existence of inherent uncertainty in natural disasters, related organizations are not able to optimally use the critical infrastructures to reduce destructive effects. Also, estimating the demand for relief commodities according to various scenarios has always been a concern for decision makers and relief organizations. The correct estimate of demand can reduce the time of relief operations and can greatly reduce human casualties. In this paper, the demand forecast for relief commodities will be as the output of the fuzzy inference system. The proposed mechanism has been tested in a case study of Tehran city. The results of this research can be useful for many decision making centers, including the fire department, the Red Cross, hospitals and so on. Estimation of demand for relief commodities using the fuzzy inference system for different scenarios and considering a case study for a possible earthquake in Tehran are the contributions of this research.
Keywords: Fuzzy inference system, Estimation of demand, relief commodities, severity of earthquake -
International Journal of Industrial Engineering and Productional Research, Volume:29 Issue: 4, Dec 2018, PP 461 -469Human-based quality control reduces the accuracy of this process. Also, the speed of decision making in some industries is very important. For removing these limitations in human-based quality control, in this paper, the design of an expert system for automatic and intelligent quality control is investigated. In fact, using an intelligent system, the accuracy in quality control is increased. It requires the knowledge of experts in quality control and design of expert systems based on the knowledge and information provided by human and equipment. For this purpose, Fuzzy Inference System (FIS) and Image Processing approach are integrated. In this expert system, the input information is the images of the products and the results of processing on images for quality control are as output. At first, they may be noisy images; the pre-processing is done and then a fuzzy system is used to be processed. In this fuzzy system, according to the images, the rules are designed to extract the specific features that are required. At second, after the required attributes are extracted, the control chart is used in terms of quality. Furthermore, the empirical case study of copper rods industry is presented to show the abilities of the proposed approach.Keywords: Image processing, Quality control, Fuzzy inference system, Features from accelerated segment test, Copper rods
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Journal of Optimization in Industrial Engineering, Volume:11 Issue: 23, Winter and Spring 2018, PP 191 -200The emergence of sustainability paradigm has influenced many research disciplines including supply chain management. It has drawn the attention of manufacturing companies CEOs to incorporate sustainability in their supply chain and manufacturing activities. Supplier selection problem, as one of the main problems in supply chain activities, is also combined with sustainable development where traditional procedures are now transformed to sustainable initiatives. Moreover, allocating optimal order quantities to sustainable suppliers has also attracted attention of many scholars and industrial practitioners, which has not been comprehensively addressed. Therefore, a practical model of supplier selection and order allocation based on the sustainability Triple Bottom Line (TBL) approach is presented in this research article. The proposed approach utilizes Fuzzy Analytical Hierarchy Process combined with Quality Function Deployment (FAHP-QFD) for reflecting buyers sustainability requirements into the preference weights that are then exerted by an efficient Fuzzy Assessment Method (FAM) to assess the suppliers to obtain their sustainability scores. Thereupon, these scores are utilized in a fuzzy multi-objective mix-integer non-linear programming model (MINLP) for allocating orders to suppliers based on the manufacturers sustainability preference. A real-world application of food industry is presented to show the practicality of the proposed approach.Keywords: Sustainability, Sustainable supplier selection, Fuzzy inference system, Order allocation, Fuzzy multi-objective non-linear programming
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خانه ی کیفیت ابزاری کارآمد در کلیه ی مراحل طراحی و توسعه ی یک محصولاست که وظیفه ی اصلی آن، ترجمه ی ندای مشتری به زبان قابل فهم برای تیم طراحی است تا طراحان بتوانند خواسته های مشتریان را شناسایی و نسبت به ارضاء آنها اقدام کنند. از آنجا که هنوز روش منسجمی برای ارزیابی خانه ی کیفیت ارائه نشده، در این نوشتار سعی شده تا سیستمی هوشمند برای ارزیابی خانه ی کیفیت ارائه شود. این سیستم به طراحان کمک می کند تا بتوانند خواسته های واقعی مشتریان را در محصول اعمال کنند و به این طریق رضایت مندی آنان را افزایش دهند. نوشتار حاضر شامل پیشینه یی از خانه ی کیفیت، مروری بر کارهای گذشته در این زمینه، و ارائه ی سیستم هوشمند مورد نظر است. همچنین نمونه یی موردی ارائه شده و نتایج آن مورد ارزیابی قرار گرفته است.کلید واژگان: خانه ی کیفیت، سیستم هوشمند، ندای مشتری، سیستم استنتاج فازی، تیم طراحیGlobal competitiveness has recently become a big challenge for many companies around the world, which are forced to seek lower costs and higher quality for what they produce. The prosperity of manufacturing firms depends on selecting and producing products which provide customer satisfaction to meet multiple objectives. If a company is able to produce customer-oriented products at a low price and in minimum time, it can be successful. So, customer need analysis should be paid attention to in product development and the design phase. Also, the technical capabilities of a manufacturing firm and the restrictions of a company should be considered. In this way, they have one big challenge: How can they respond effectively to different and easily changing customer demands? By focusing on customer opinion, Quality Function Deployment (QFD) has been developed. Quality Function Deployment (QFD) is a robust, efficient and powerful tool in the design, development and planning of products. QFD has been used in many industries and companies over the last few decades. The main function of QFD is conversion of the voice of the customer (VOC) to Technical Characteristics(TCs). However, it is not always easy to prioritize and assess TCs during the total mass of information from the different customer attitudes. This paper provides a methodology for the development of an intelligent Quality Function Deployment (IQFD) and points for developing an intelligent system based on a fuzzy inference system, in order to capture information through the House of Quality (HOQ) matrix. The paper describes the need for development of intelligent QFD to make it easier for engineers and managers to choose between TCs and improve the quality of products and systems. This paper is composed of a background of QFD, a review of related research work, and representation ofan intelligent system for its analysis. Then, it applies the proposed methodology to a case study of House of Quality for the design of a new undergraduate curriculum in the mechanical engineering department of the university of Wisconsin-Madison.Keywords: Quality function deployment, house of quality, expert system, fuzzy inference system, technical characteristics
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