دانلود و نمایش مقالات مرتبط با Network optimization::صفحه 1
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نتیجه جستجو - Network optimization

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Conceptual MINLP approach to the development of a CO2 supply chain network – Simultaneous consideration of capture and utilization process flowsheets
رویکرد مفهومی MINLP برای توسعه یک شبکه زنجیره تامین CO2 - در نظر گرفتن همزمان صفحه های جریان فرآیند ضبط و استفاده-2021
A large fraction of anthropogenic CO2 emissions comes from large point sources such as power plants, petroleum refineries, and large industrial facilities. A significant decrease of these CO2 emissions can be achieved with CO2 capture, utilization, and storage (CCUS) technologies. This study proposes a conceptually simplified model for the optimization of combined CO2 supply networks and capture and utilization technologies by the mixed-integer non-linear programming (MINLP) approach. The objective is to maximize the profit of CCUS technologies, considering chemisorption using methyl-diethanolamine (MDEA) as a capture technology and conversion of CO2 to CH3OH as a utilization technology. Additionally, avoided tax from reduced CO2 emissions is considered as a revenue. A hypothetical case study of five larger point sources of CO2 was investigated, namely coal power plants, biogas plant, aluminium production plant and two cement plants. Two scenarios were considered: i) Scenario A considering different values of the CO2 tax, and ii) Scenario B considering different flue gas flowrates at different values of the CO2 tax. The results show the potential of model-based optimization in reducing the amount of CO2 in the atmosphere by CCUS technology. Furthermore, the results in Scenario A show that CCUStechnology is only profitable if the price of CO2 emissions is higher than 110 €/t emitted CO2. Moreover, the results in Scenario B show that both the profit and the production of CH3OH depend to a large extent on the flue gas flow.
KEYWORDS: Point sources of CO2 | Carbon capture | Storage and utilization (CCUS) | Supply network optimization | Process optimization | MINLP approach
مقاله انگلیسی
2 Optimization of supply chain networks with inclusion of labor: Applications to COVID-19 pandemic disruptions
بهینه سازی شبکه های زنجیره تامین با گنجاندن نیروی کار: برنامه های کاربردی برای اختلالات همه گیر COVID-19-2021
In this paper, we respond to the COVID-19 pandemic by constructing supply chain network optimization models, which explicitly include labor as an important variable in the network economic activity links, along with associated capacities. Labor is a critical resource in supply chains from production to transportation, storage, and distribution. In a pandemic, the availability of labor for different supply chain network activities may be disrupted due to illness, fear of contagion, morbidity, necessity of social/physical distancing, etc. The modeling framework considers first elastic demands for a product and then fixed demands, coupled with distinct types of labor capacities in order to capture the availability of this valuable resource in a pandemic, as well as possible flexibility. The supply chain network framework, which includes electronic commerce, is relevant to many different supply chain applications including protective personal and medical equipment, as well as to particular food items. Theoretical results as well as computed numerical examples are presented.
Keywords: Pandemic | Supply chains | Labor resources | Disruptions | Network optimization | Healthcare
مقاله انگلیسی
3 Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems
یادگیری تقویتی عمیق یک شات برای طبقه بندی هوشمندانه مصنوعی در سیستم های خبره-2020
In recent years there has been a sharp rise in applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. By contrast, the focus in this paper is on rigorously optimizing the resource consumption in the system which applies to broad application domains, and is of a significant interest for academic research, industrial developments, as well as society and citizens benefit. The contribution of this paper is threefold. First, a novel Deep Reinforcement One-shot Learning (DeROL) framework is developed to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, the first open-source software for practical artificially intelligent one-shot classification systems with limited resources is developed for the benefit of researchers and developers in related fields. Third, an extensive experimental study is presented using the OMNIGLOT dataset for computer vision tasks, the UNSW-NB15 dataset for intrusion detection tasks, and the Cleveland Heart Disease Dataset for medical monitoring tasks that demonstrates the versatility and efficiency of the DeROL framework.
Keywords: Deep reinforcement learning | One-shot learning | Network optimization | Online classification
مقاله انگلیسی
4 Wireless Big Data: Technologies and Applications
داده های بزرگ بی سیم: فن آوری ها و برنامه های کاربردی-2018
The thirteen papers in this special section focus on the topic of wireless Big Data applications. Powered by advanced analytics methods, big data has emerged as a promising paradigm to handle voluminous and complex data. Recently, to cope with the emerging fifth generation (5G) and Internet of Things (IoT), wireless big data affords us an unprecedented opportunity to obtain an in-depth understanding of wireless things and facilitate data-driven approaches for network optimization and operation. The papers in this section aim to tackle the challenges and consolidate timely theory and applications of wireless big data.
Keywords: Special issues and sections,Big Data,Internet of Things,Wireless communication,5G mobile communication
مقاله انگلیسی
5 Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles
مدیریت زنجیره تامین پایدار در دوران دیجیتالیزاسیون: تاثیر وسایل نقلیه راهنمایی خودکار-2017
Internationalization of markets and climate change introduce multifaceted challenges for modern supply chain (SC) management in the todays digitalisation era. On the other hand, Automated Guided Vehicle (AGV) systems have reached an age of maturity that allows for their utilization towards tackling dynamic market conditions and aligning SC management focus with sustainability considerations. However, extant research only myopically tackles the sustainability potential of AGVs, focusing more on addressing network optimization problems and less on developing integrated and systematic methodological ap proaches for promoting economic, environmental and social sustainability. To that end, the present study provides a critical taxonomy of key decisions for facilitating the adoption of AGV systems into SC design and planning, as these are mapped on the relevant strategic, tactical and operational levels of the natural hierarchy. We then propose the Sustainable Supply Chain Cube (S2C2), a conceptual tool that integrates sustainable SC management with the proposed hierarchical decision-making framework for AGVs. Market opportunities and the potential of integrating AGVs into a SC context with the use of the S2C2 tool are further discussed.
Keywords:Automated Guided Vehicles |Sustainable supply chain management| Literature taxonomy| Decision-making framework |Sustainable Supply Chain Cube (S2C2) tool
مقاله انگلیسی
6 بیشینه سازی درآمد گروه هتلداری کارلسون رزیدور با بهبود مدیریت تقاضا و بهینه سازی قیمتی
سال انتشار: 2013 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 25
در شرایط متغیر بازار در صنعت هتلداری، گروه هتلداری کارلسون رزیدور (GRHC) به همکاری با گروه نرم افزاری JDA پرداخته تا با استفاده از تحقیق در عملیات، به کسب درآمد بیشتر در هتل ها و پیشتازی در این رقابت نایل گردد. این پروژه نوآورانه بهینه سازی درآمدی با نام قیمت گذاری اتوماتیک اقامت شبانه (SNAP) با پیش بینی درخواست ها در 600 هتل در آمریکا در سال 2007 آغاز شد. این مسئله با راه حل های بهینه سازی شبکه ای در ابعاد بزرگ دنبال شد تا به صورت پویا نرخ اتاق های هتل را براساس حساسیت تقاضا به قیمت، نرخ رقبا، در امکانات در دسترس، پیش بینی میزان تقاضا و قوانین تجاریب هینه سازی کند. تمامی هتل های آمریکای شمالی تا مارس 2011 از SNAP استفاده می کردند. بهینه سازی نمونه اولیه در سال 2008 شروع شد، CRHG مداوماً برتری 2 تا 4 درصدی درآمد را در هتل های استفاده کننده از آن نسبت به سایر هتل ها اندازه گیری کرده است. تا کنون هتل های استفاده کننده از نرم افزارافزایش درآمد سالانه ای بیش از 16 میلیون دلار داشته اند. بعد از به کارگیری موفق در آمریکا، CRHG همکاری خود با JDA را برای انتشار جهانی SNAP ادامه داده است که با تمرکز بر اروپا، خاورمیانه، آفریقا و آسیا-اقیانوسیه بوده است. CRHG پیش بینی می کند که درآمد جهانی ناشی از این راهکارسالانه بیش از 30 میلیون دلار خواهد بود.
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