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نتیجه جستجو - سیستم های پشتیبانی تصمیم گیری

تعداد مقالات یافته شده: 11
ردیف عنوان نوع
1 An analytic infrastructure for harvesting big data to enhance supply chain performance
یک زیرساخت تحلیلی برای برداشت داده های بزرگ به منظور افزایش عملکرد زنجیره تأمین-2020
Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this pa- per, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infras- tructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based opera- tional decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s op- erations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development.
Keywords: Decision support systems | Big data | Analytic infrastructure | Competence set | Deduction graph
مقاله انگلیسی
2 PROGRAMS project approach to maintenance management
رویکرد پروژه PROGRAMS برای مدیریت تعمیر و نگهداری-2020
Maintenance management is a vital part of the business of a production company. It contributes to determining the long-term success of the company because poorly maintained resources can stop production activities, causing delays, loss of profit and even personal injuries. While the predictive maintenance approach is nowadays a mainstay of modern factory management, usually only big companies with dedicated research department can deploy it, since its application involves scientific knowledge that is not available in smaller production environments. This paper describes how the EU project PROGRAMS answers the needs of small and medium companies that wish to apply an Industrial Internet of Things (IIoT) approach to maintenance management.
Keywords: Maintenance engineering | Preventive maintenance | Decision support systems | Optimization | problems | Sensors systems.
مقاله انگلیسی
3 Decision support systems for agriculture 4.0: Survey and challenges
سیستم های پشتیبانی تصمیم گیری برای کشاورزی 4.0: بررسی و چالش ها-2020
Undoubtedly, high demands for food from the world-wide growing population are impacting the environment and putting many pressures on agricultural productivity. Agriculture 4.0, as the fourth evolution in the farming technology, puts forward four essential requirements: increasing productivity, allocating resources reasonably, adapting to climate change, and avoiding food waste. As advanced information systems and Internet technologies are adopted in Agriculture 4.0, enormous farming data, such as meteorological information, soil conditions, marketing demands, and land uses, can be collected, analyzed, and processed for assisting farmers in making appropriate decisions and obtaining higher profits. Therefore, agricultural decision support systems for Agriculture 4.0 has become a very attractive topic for the research community. The objective of this paper aims at exploring the upcoming challenges of employing agricultural decision support systems in Agriculture 4.0. Future researchers may improve the decision support systems by overcoming these detected challenges. In this paper, the systematic literature review technique is used to survey thirteen representative decision support systems, including their applications for agricultural mission planning, water resources management, climate change adaptation, and food waste control. Each decision support system is analyzed under a systematic manner. A comprehensive evaluation is conducted from the aspects of interoperability, scalability, accessibility, usability, etc. Based on the evaluation result, upcoming challenges are detected and summarized, suggesting the development trends and demonstrating potential improvements for future research.
Keywords: Agriculture | Smart farming | Decision-making | Decision support systems
مقاله انگلیسی
4 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
5 Police staffing and workload assignment in law enforcement using multi-server queueing models
کارکنان پلیس و تخصیص بار کاری در اجرای قانون با استفاده از مدل های صف بندی چند سروری-2019
Criminal activities have been posing threat to human societies. In many countries, police officers have been serving as one major solution in addressing crime. However, some countries suffer from a scarcity of police officers and the unbalanced distribution of police forces. In this research, we study the law enforcement problem to address the aforementioned situation by dividing it into two sub-problems, i.e., the police staffing problem and the workload assignment problem. To improve staffing efficiency and service quality, we propose a double-resource queueing model (DRQM) with referral and a single-resource queueing model (SRQM) with inner classification. We solve the problems of police staffing and workload assignment by optimizing the referral threshold in the DRQM and the inner classification criterion in the SRQM. Results show that the SRQM with inner classification can always achieve higher staffing efficiency than the DRQM with referral. On service quality, dependent on the optimal referral threshold in DRQM or the optimal inner classification criterion in SRQM, either DRQM or SRQM is preferred.
Keywords: Decision support systems | Law enforcement | Police staffing | Workload assignment | Queueing model
مقاله انگلیسی
6 A noise correction-based approach to support a recommender system in a highly sparse rating environment
یک روش مبتنی بر تصحیح نویز برای پشتیبانی از یک سیستم توصیه گر در یک محیط رتبه بندی بسیار پراکنده-2019
Recommender systems support consumers in decision-making for selecting desired products or services from an overloaded search space. However, this decision support system faces difficulties while dealing with sparse and noisy rating data. Therefore, this research re-classifies users and items of a system into three classes, namely strong, average and weak to identify and correct noise ratings. Later, the Bhattacharya coefficient, a well-performing similarity measure for a sparse dataset, is integrated with the proposed re-classification method to predict unrated items from the obtained noise-free sparse dataset and recommend preferred products to consumers. Furthermore, the effectiveness of the proposed model is validated on two sparse and noisy datasets and compared with various published methods in terms of the mean absolute error (MAE), root mean square error (RMSE), F1-measure, precision, and recall values. The obtained results confirm that the proposed model performs better than other published relevant methods.
Keywords: Decision support systems | Recommender system | Collaborative filtering | Natural noise | Sparsity
مقاله انگلیسی
7 A fuzzy decision support system for managing maintenance activities of critical components in manufacturing systems
یک سیستم پشتیبانی تصمیم گیری فازی برای مدیریت فعالیت های نگهداری از قطعات مهم در سیستم های تولید-2019
Management of critical components in manufacturing systems aims at managing components with very low reliability or the highest risk which can cause disruptions in manufacturing. This study presents a method for identifying critical components and a decision support tool for managing maintenance activities of critical components in manufacturing systems. Unlike the traditional reliability function, the proposed method uses the duty cycle, utilization rate of capacity, safety stock effect, and redundancy effect. It also has the ability to calculate some of the costs associated with the reliability and maintenance. In addition to the proposed method, an expert system as a decision support tool has also been proposed to assist in managing maintenance activities of critical components. The proposed method and the developed decision support system have been tested with a real data set taken from an industrial company and a randomly generated data set. The results have shown that the proposed method has a more realistic impact on component reliability compared to the traditional reliability function. The experimental results have validated the credibility of the proposed decision support system to manage maintenance activities of critical components. Besides, two comparison tables have shown that the proposed decision support system outperforms some approaches such as ANN, FMEA, FMECA, and AHP.
Keywords: Critical component management | Maintenance management | Reliability | Fuzzy logic | Decision support sys
مقاله انگلیسی
8 Deep learning models for bankruptcy prediction using textual disclosures
مدل های یادگیری عمیق برای پیش بینی ورشکستگی با استفاده از افشای متن-2019
This study introduces deep learning models for corporate bankruptcy forecasting using textual disclo- sures. Although textual data are common, it is rarely considered in the financial decision support models. Deep learning uses layers of neural networks to extract features from textual data for prediction. We con- struct a comprehensive bankruptcy database of 11,827 U.S. public companies and show that deep learning models yield superior prediction performance in forecasting bankruptcy using textual disclosures. When textual data are used in conjunction with traditional accounting-based ratio and market-based variables, deep learning models can further improve the prediction accuracy. We also investigate the effectiveness of two deep learning architectures. Interestingly, our empirical results show that simpler models such as averaging embedding are more effective than convolutional neural networks. Our results provide the first large-sample evidence for the predictive power of textual disclosures.
Keywords: Decision support systems | Deep learning | Bankruptcy prediction | Machine learning | Textual data
مقاله انگلیسی
9 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
10 Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud
اهرم های قابلیت های سیستم های پشتیبانی تصمیم گیری سرویس گرا: تجزیه و تحلیل ترافیک قرار دادن و داده های بزرگ در ابر-2013
Using service-oriented decision support systems (DSS in cloud) is one of the major trends for many organizations in hopes of becoming more agile. In this paper, after defining a list of requirements for serviceoriented DSS, we propose a conceptual framework for DSS in cloud, and discus about research directions. A unique contribution of this paper is its perspective on how to servitize the product oriented DSS environment, and demonstrate the opportunities and challenges of engineering service oriented DSS in cloud. When we define data, information and analytics as services, we see that traditional measurement mechanisms, which are mainly time and cost driven, do not work well. Organizations need to consider value of service level and quality in addition to the cost and duration of delivered services. DSS in CLOUD enables scale, scope and speed economies. This article contributes new knowledge in service science by tying the information technology strategy perspectives to the database and design science perspectives for a broader audience. Keywords: Cloud computing Service orientation Service science Data-as-a-service Information-as-a-service Analytics-as-a-service Big data
مقاله انگلیسی
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