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مدیریت فرآیند کسب و کار: سیر تکاملی یک رشته
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 16 مدیریت فرآیند کسب و کار (BPM) یک فلسفه مدیریت را در بر می گیرد که توسط طیف وسیعی از روش ها، تکنیک ها و ابزار پشتیبانی می شود. دانشگاهیان به طور مداوم در حال گسترش این رپرتوار هستند. در این مقاله مروری، مضامینی که توسعه رشته BPM را در طی سالها مشخص میکنند ترسیم میکنند: سیستمهای BPM، مدلسازی فرآیند، طراحی فرآیند، هماهنگی و قابلیت همکاری، مدیریت مدل، استخراج فرآیند، و فناوریهای جدید. هر یک از موضوعات در این بررسی اجمالی بر اساس مقالاتی که از زمان پیدایش آن، در حال حاضر 40 سال پیش، در کامپیوتر در صنعت ظاهر شده است، مشخص می شود. این مضامین با هم چشم اندازی از یک رشته پر رونق و در حال تکامل ارائه می دهند.
کلید واژه ها: مدیریت فرآیند کسب و کار | مدل سازی فرآیند | طراحی فرآیند | استخراج فرآیندی | BPMS |
مقاله ترجمه شده |
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Optimal process design for integrated municipal waste management with energy recovery in Argentina
طراحی فرآیند بهینه برای مدیریت یکپارچه زباله شهری با بازیابی انرژی در آرژانتین-2020 This work presents a comprehensive mathematical model for the optimal selection of municipal waste
treatment alternatives, accounting for co-digestion of sludge and municipal solid waste. The superstructure
of alternatives includes anaerobic digestion under mesophilic or thermophilic conditions,
composting, recycling, and final disposal in a landfill. Anaerobic digesters can be fed with different
mixing ratios of sewage sludge (SS) and the organic fraction of municipal solid waste (OF). A mixedinteger
mathematical programming formulation is proposed to find the optimal process design. It
comprises nonlinear equations to estimate digestion yields according to substrate mixing ratios. Results
for cities of different sizes show that the joint treatment can increase profitability, especially in small
populations. In all cases, co-digestion of the full stream of SS and OF leads to an integrated waste-toenergy
process that maximizes the economic value and reduces environmental impacts of waste by
producing electricity, heat and fertilizer. Keywords: Co-digestion | Waste-to-Energy | Optimization | Superstructure | Process design |
مقاله انگلیسی |
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Circular economy in Italian SMEs: A multi-method study
اقتصاد دایره ای در SME های ایتالیایی: یک مطالعه چند روشه-2020 Climate change, population growth, and current rate of consumption at global scale have prompted academic and business communities to challenge the current models of production towards more circular approaches. This study aims at understanding what actions small and medium-sized enterprises (SMEs) are taking to meet the challenges and opportunities of the circular economy (CE), analysing actions, barriers, enablers and the connection between CE, business strategy and performance. This research involved 254 Italian SMEs through a multi-method approach, including interviews, surveys, and focus groups. Twenty different CE practices related to waste management, packaging, supply chain and product/process design have been explored. The results show that several CE practices are simultaneously implemented by SMEs, thus supporting the notion that CE implies a systemic approach to company’s value creation. In particular, waste management was widely applied (e.g. separated waste collection was carried out by 84% of the companies surveyed), while resource saving practices were implemented by only 14% of the sample. Higher costs are the main barrier to CE for early adopters (5.13 on a 7-point Likert-type scale). However, companies implementing CE practices perceive them as a business opportunity rather than a cost, thus showing that CE may represent a source of value creation for companies, particularly SMEs.© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Safety management efficiency | Coal enterprise | Technical efficiency | DEA-Tobit |
مقاله انگلیسی |
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Process intensification education contributes to sustainable development goals: Part 1
آموزش تشدید فرآیند به اهداف توسعه پایدار کمک می کند: قسمت 1-2020 In 2015 all the United Nations (UN) member states adopted 17 sustainable development goals (UN-SDG)
as part of the 2030 Agenda, which is a 15-year plan to meet ambitious targets to eradicate poverty, protect
the environment, and improve the quality of life around the world. Although the global community
has progressed, the pace of implementation must accelerate to reach the UN-SDG time-line. For this to happen, professionals, institutions, companies, governments and the general public must become cognizant of the challenges that our world faces and the potential technological solutions at hand, including
those provided by chemical engineering. Process intensification (PI) is a recent engineering approach
with demonstrated potential to significantly improve process efficiency and safety while reducing cost.
It offers opportunities for attaining the UN-SDG goals in a cost-effective and timely manner. However,
the pedagogical tools to educate undergraduate, graduate students, and professionals active in the field
of PI lack clarity and focus. This paper sets out the state-of-the-art, main discussion points and guidelines
for enhanced PI teaching, deliberated by experts in PI with either an academic or industrial background,
as well as representatives from government and specialists in pedagogy gathered at the Lorentz Center
(Leiden, The Netherlands) in June 2019 with the aim of uniting the efforts on education in PI and produce
guidelines. In this Part 1, we discuss the societal and industrial needs for an educational strategy in the
framework of PI. The terminology and background information on PI, related to educational implementation in industry and academia, are provided as a preamble to Part 2, which presents practical examples
that will help educating on Process Intensification. Keywords: Process intensification | Pedagogy | Chemical engineering | Process design | Education challenge | Industry challenge | Sustainability | Entrepreneurship |
مقاله انگلیسی |
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Deep Learning-Driven Particle Swarm Optimisation for Additive Manufacturing Energy Optimisation
بهینه سازی ازدحام ذرات با محوریت یادگیری عمیق برای بهینه سازی انرژی تولید افزودنی-2019 The additive manufacturing (AM) process is characterised as a high energy-consuming process, which
has a significant impact on the environment and sustainability. The topic of AM energy consumption
modelling, prediction, and optimisation has then become a research focus in both industry and academia.
This issue involves many relevant features, such as material condition, process operation, part and
process design, working environment, and so on. While existing studies reveal that AM energy
consumption modelling largely depends on the design-relevant features in practice, it has not been given
sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect
to energy modelling. These features are typically determined by part designers and process operators
before production. The AM energy consumption knowledge, hidden in the design-relevant features, is
exploited for prediction modelling through a design-relevant data analytics approach. Based on the new
modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is
proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms
of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant
data collected from a real-world AM system in production, a case study is presented to validate the
proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been
carried out to guide part designers and process operators to revise their designs and decisions in order
to reduce the energy consumption of the designated AM system under study. Keywords: Additive Manufacturing | Energy Consumption Modelling | Prediction and Optimisation | Deep Learning | Particle Swarm Optimisation |
مقاله انگلیسی |
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Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era
بهینه سازی قوی تصادفی مبتنی بر داده ها: چارچوب محاسباتی عمومی و الگوریتم استفاده از یادگیری ماشین برای بهینه سازی تحت عدم اطمینان در دوران داده های بزرگ-2018 A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization un
der uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are
often collected from various conditions, which are encoded by class labels. Machine learning methods
including Dirichlet process mixture model and maximum likelihood estimation are employed for uncer
tainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model
through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic
programming approach to optimize the expected objective across different data classes; adaptive robust
optimization is nested as the inner problem to ensure the robustness of the solution while maintaining
computational tractability. A decomposition-based algorithm is further developed to solve the resulting
multi-level optimization problem efficiently. Case studies on process network design and planning are
presented to demonstrate the applicability of the proposed framework and algorithm.
Keywords: Big data ، Optimization under uncertainty ، Bayesian model ، Machine learning ، Process design and operations |
مقاله انگلیسی |
7 |
Structuring engineers implicit knowledge of forming process design by using a graph model
مهندسان ساختار دانش ضمنی تشکیل طراحی فرایند با استفاده از مدل نمودار-2018 Forming Process Design needs simultaneous consideration of multiple factors, accuracy of products, costs and cycle time. Consequently, its
design knowledge is based on personal experience and tends to be implicit and untransferable in general. To represent and transfer such implicit
and personal design knowledge of forming process, we propose a novel knowledge mining method based on graph theory. Experienced engineers’
knowledge is transcribed as a set of statements through a series of interviews. Then, the interrelationship among these statements is clarified by
translating them to a graph-based knowledge representation model. Implicit knowledge is extracted as structural characteristics of the graph.
Keywords: Implicit knowledge, Graph model, Knowledge transfer, Process design, Expert |
مقاله انگلیسی |
8 |
Parallelization methods for efficient simulation of high dimensional population balance models of granulation
روش های موازی سازی برای شبیه سازی کارآمد مدل های توازن توزیع جمعیت ابعاد بزرگ دانه بندی-2017 In order to solve high resolution PBMs to simulate real systems, with high accuracy and speed, a comprehensive and robust parallelization framework is needed. In this work, parallelization using just Message Passing Interface (MPI) and a more advanced method using a hybrid MPI + OpenMP (Open MultiProcessing) technique, have been applied to simulate high resolution PBMs on the computing clusters,
SOEHPC and Stampede. We study the speed up and the scale up of these parallelization techniques for
different system sizes and different computer architectures to come up with one of the fastest ways to
solve a PBM to date. Parallel PBMs ran approximately 50–60 times faster, when using 128 cores, than
the serial PBMs ran. In this work it is found that hybrid MPI + OMP methods which account for socket
affinities led to the fastest PBM compute times and about 80% less memory than a purely MPI approach.
Keywords: MPIئ | OpenMP | Parallel computing | Population balance model | Granulation | Pharmaceutical process design |
مقاله انگلیسی |
9 |
Selecting food process designs from a supply chain perspective
انتخاب طرح های فرایند مواد غذایی از منظر زنجیره تامین-2017 The food industry can convert agro-materials into products using many alternative process designs. To
remain competitive, companies have to select the design leading to the best supply chain performance.
These designs differ in the technologies used and the product portfolio produced. Additionally, charac
teristics, such as seasonal production and quality decay of food products, lead to specific requirements
regarding processing, transportation and storage. The importance of these characteristics of the food
industry on process design selection is investigated using sugar beet processing as an illustrative case.
The characteristics are included in a multi-period, multi-product location-allocation model. The model
shows that a supply chain perspective leads to changes in process design selection. The design with the
best portfolio value and processing costs does not lead to the best supply chain performance. This shows
the importance of a chain perspective to avoid sub-optimization in food process design selection.
Keywords: Food industry | Product portfolio | Process design | Facility location | Supply chain configuration | Optimization |
مقاله انگلیسی |
10 |
فرآیند طراحی برای مدیریت تغییرات در تولید: به سوی فرآیند مدیریتی تغییرات تولید
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 24 مدیریت کارآمد و موثر تغییرات در تولید به منزله یک عامل کلیدی موفق برای شرکت های صنعتی در جهان بسیار پویا بشمار می رود. با وجود اهمیت مسلم آن، مدیریت تغییرات از تولید به عنوان یک رشته اختصاص داده شده از تحقیقات که به ندرت بر علم مهندسی تمرکز داشته، یاد می کنند. با این حال، زمینه های دیگری از پژوهش، از جمله مهندسی مدیریت تغییر (ECM) و یا برنامه ریزی مستمر کارخانه ، بررسی موضوعات مشابه (ECM) در دامنه های مختلف (توسعه محصول) یا دارای همان جسم از مشاهده (تولید / کارخانه) بر روش های مختلف (برنامه ریزی کارخانه) تمرکز کرده اند. بر اساس گستره ای از منابع موجود در این زمینه، فرایند اختصاص داده شده برای مدیریت تغییرات در تولید، فرایند MCM را طراحی کرده است. مطابق با رهنمودهای موجود طی طراحیِ روش تحقیق (DRM)، مجموع 42 فرآیند و ملزماتِ فرایند مربوطه، برای طراحی فرایند MCM تحلیل شدند. علاوه بر بیشتر تحلیل های موجود در مراحل فرایند، مقاله در رویکرد اعتبار و نتایج و همچنین عامل به دست آمده برای تحقیق بیشتر در زمینهء مدیریت تغییرات تولید (MCM) شرح و تفصیل گشت.
کلید واژه ها: مدیریت تغییرات تولید | مهندسی مدیریت تغییر | برنامه ریزی کارخانه | فرایند مرجع |
مقاله ترجمه شده |