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Circular supply chain management with large scale group decision making in the big data era: The macro-micro model
مدیریت زنجیره تأمین دایره ای با تصمیم گیری گروهی در مقیاس بزرگ در عصر داده های بزرگ: مدل خرد خرد-2021 Today, achieving the circular economy is a common goal for many enterprises and governments all around the world. In the big data era, decision making is well-supported and enhanced by a massive amount of data. In particular, large scale group decision making (LSGDM), which refers to the case in which a lot of decision makers join the decision making process, has emerged. Social network analyses are known to be relevant to LSGDM. In this paper, we examine the literature on LSGDM and highlight the current methodological advances in the area. We review the works focusing on applications of LSGDM. We study how big data can be used in circular supply chains. Based on the reviewed studies, we further construct the three-stage LSGDM CSCM micro framework as well as the five-step LSGDM CSCM macro framework (with a feedback loop) and form the Macro-Micro Model. We discuss how the Macro-Micro Model can help to support circular supply chain management (CSCM). We propose future research directions and areas. This paper contributes by being the first study uncovering systematically how LSGDM can be applied to support CSCM in the big data era using the Macro-Micro Model. Keywords: Large scale group decision making (LSGDM) | Circular supply chains | Research agenda | Literature review | Frameworks | Macro-micro model |
مقاله انگلیسی |
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Exponential operational laws and new aggregation operators for intuitionistic multiplicative set in multiple-attribute group decision making process
قوانین عملیاتی نمایی و اپراتورهای تجمیع جدید برای مجموعه چند برابر شهودی در فرایند تصمیم گیری گروهی چند صفت-2020 The intuitionistic multiplicative preference set is one of the replacements to the intuitionistic
fuzzy preference set, where the preferences related to the object is asymmetrical distribution
about 1. In it, Saaty’s 1–9 scale has been used to represent the uncertain and
imprecise information. Meanwhile, an aggregation operator by using general operational
laws for some fuzzy sets is an important task to aggregate the different numbers.
Motivated by these primary characteristics, it is interesting to present the concept of exponential
operational laws, which differs from the traditional laws by the way, in which bases
are real numbers while exponents are the intuitionistic multiplicative numbers. In this
paper, we develop a methodto solve the Multiple Attribute Group Decision Making
(MAGDM) problem under the Intuitionistic Multiplicative Sets (IMS) environment. To do
it, firstly, we define some new exponential operational laws and a score function for IMS
and studied their properties. Secondly, based on this, we develop some averaging and geometric
aggregation operators and characterize their various properties. Thirdly, a novel
approach is promoted to solve MAGDM problems with IMS information. Finally, some
numerical illustrations are given with a comparative study to verify the approach. Keywords: Intuitionistic multiplicative sets | MAGDM | Exponential operational laws | Aggregation operators | Score function |
مقاله انگلیسی |
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Artificial Intelligence in Aortic Surgery: The Rise of the Machine
هوش مصنوعی در جراحی آئورت: ظهور ماشین-2020 The first concept of Artificial Intelligence (AI) came into attention during 1920s
and currently it is rapidly being integrated in our daily clinical practice. The use
of AI has evolved from basic image-based analysis into complex decisions
related to different surgical procedure. AI has been very widely used in the cardiology
field, however the use of such machine-led decisions has been limited
and explored at slower pace in surgical practice. The use of AI in cardiac surgery
is still at its infancy but growing dramatically to reflect the changes in the
clinical decision making process for better patient outcomes. The machine-led
but human controlled algorithms will soon be taking over most of the decision
making processes in cardiac surgery. This review article focuses on the practice
of AI in aortic surgery and the future of such technology-led decision making
pathways on patient outcomes, surgeon’s learning skills and adaptability. Keywords: Big data | Machine learning | Artificial intelligence | Aortic surgery |
مقاله انگلیسی |
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A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019 Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling. Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model |
مقاله انگلیسی |
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Hybrid agent-based modeling of rooftop solar photovoltaic adoption by integrating the geographic information system and data mining technique
مدل سازی مبتنی بر عامل ترکیبی استفاده از فتوولتائیک خورشیدی پشت بام با ادغام سیستم اطلاعات جغرافیایی و تکنیک داده کاوی-2019 Modeling energy technology adoption involves heterogeneity and dynamic interactions of individuals based on
the physical, technical, and economic environments in the decision making process. In this context, this study
aims to develop a hybrid model integrating an agent-based modeling (ABM) with the geographic information
system and logistic regression for simulating rooftop solar photovoltaic (PV) adoption in the study area. Towards
this end, this study regarded “building” as an “agent” to simulate the market diffusion of rooftop solar PV
systems in the Nonhyeon neighborhood, located in the Gangnam district, Seoul, South Korea, based on various
factors affecting the adoption (i.e., physical, demographic & socioeconomic, technical, economic, and social
factors). This study considered three behavioral rules of rooftop solar PV adoption, which were determined using
panel logistic regression according to different motivators for rooftop solar PV adoption. Based on these different
behavioral rules, three hybrid ABM models were developed to simulate the market diffusion of rooftop solar PV
systems. It was shown that models including the various potential motivators for the adoption proposed in this
study better represented the reality of aggregate decision-making processes, while the model including only the
motivators proposed in the previous ABM studies failed to perform well, rarely adopting the rooftop solar PV
system during the runs. The ABM proposed in this study allows the estimation of the aggregate amount and
patterns of future market diffusion for rooftop solar PV systems, which can be widely used by governments and
electric utilities for evaluating policies and business models. Keywords: Renewable energy adoption | Rooftop solar photovoltaic system | Agent-based modeling | Geographic information system | Logistic regression | Peer effects |
مقاله انگلیسی |
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The impact of engineering students’ performance in the first three years on their graduation result using educational data mining
تأثیر عملکرد دانشجویان مهندسی در سه سال اول در نتایج فارغ التحصیلی آنها با استفاده از داده کاوی آموزشی-2019 Research studies on educational data mining are on the increase due to the benefits
obtained from the knowledge acquired from machine learning processes which help
to improve decision making processes in higher institutions of learning. In this
study, predictive analysis was carried out to determine the extent to which the
fifth year and final Cumulative Grade Point Average (CGPA) of engineering
students in a Nigerian University can be determined using the program of study,
the year of entry and the Grade Point Average (GPA) for the first three years of
study as inputs into a Konstanz Information Miner (KNIME) based data mining
model. Six data mining algorithms were considered, and a maximum accuracy of
89.15% was achieved. The result was verified using both linear and pure
quadratic regression models, and R2 values of 0.955 and 0.957 were recorded for
both cases. This creates an opportunity for identifying students that may graduate
with poor results or may not graduate at all, so that early intervention may be
deployed Keywords: Education | Information science | Computer science |
مقاله انگلیسی |
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Facilitating tourists decision making through open data analyses: A novel recommender system
تسهیل تصمیم گیری در مورد گردشگران از طریق تجزیه و تحلیل داده های باز: یک سیستم توصیه گر جدید-2019 A number of studies have recently been published reporting researchers efforts to create new, more efficient
recommender systems to support tourists decision making. This current research operationalizes a recommender
system by filtering user-generated data that is abundantly available online, based on individuals evaluation
criteria, to produce a dataset for analysis. Drawing upon an array of predictive models, this research proposes a
new recommender system able to facilitate the tourist decision making process through successful managing of
open data. It further presents a rating estimation method using ratings that pertain to online users-specified
criteria (profile). The model is able to predict consumers ratings of a certain product with high reliability
starting from open data on their profiles. Keywords: Recommender system | Tourist decision making process | Consumer information processing | Classifier systems | Open data analysis |
مقاله انگلیسی |
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Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018 Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization |
مقاله انگلیسی |
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Decisions on nuclear decommissioning strategies: Historical review
تصمیم گیری در مورد انهدام استراتژی هسته ای: مرور تاریخی-2018 Nuclear decommissioning is the final technical and administrative process in the life cycle of nuclear power
operation. As such, decommissioning must strive to ensure public safety. This goal requires many factors be
involved in the decision making process. The purpose of this research is to examine how experienced countries
have taken into account a variety of associated factors when deciding on a nuclear decommissioning strategy.
The information consisted of 162 historical cases, where the reactor was permanently shutdown. Major factors
affecting those decisions were identified and quantified; each were subsequently evaluated through logistic
regression and the Spearmans correlation analysis. The factors used in the study were operating periods, Health
Development Index (HDI), decommissioning funding, public acceptance, public tolerance, decommissioning
experience level, the reactor type, operating history, availability of radioactive waste facilities, presence of
multiple units at the site, and technological capabilities. These statistical analyses identified key factors sig
nificantly influencing nuclear decommissioning strategy decisions. These decisions were on how to perform the
decommissioning work i.e., DECON (immediate dismantling) vs. SAFESTOR (deferred dismantling) and on site
end-state, i.e., greenfield vs. brownfield. Studying the roles of these factors in their respective decisions provided
a number of related insights. Although historical data used in this study may not be sufficient to reveal nation
specific issues or detailed interactions among the factors analyzed, the observations from this study will be useful
for future efforts in nuclear decommissioning, especially for countries without any experience in decom
missioning.
Keywords: Nuclear decommissioning strategies ، Historical decommissioning cases and ، experiences ، Major factors affecting decisions |
مقاله انگلیسی |
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Analyzing nuclear expertise support to population protection decision making process during nuclear emergencies
تجزیه و تحلیل پشتیبانی از دانش هسته ای به فرایند تصمیم گیری در مورد حفاظت از جمعیت در شرایط اضطراری هسته ای-2018 Nowadays, strategies to protect population in the early phase of a nuclear crisis consist in three main actions:
sheltering, evacuation and iodine pills ingestion. These actions are supposed to be guided by two successive
decision-making strategies: triggering reflex actions in pre-planned perimeters in the near field around the ac
cident and then, achieving spatial estimation of doses received by the general public (expressed in Sievert) along
the situation development to adapt the actions. Through the observation of four nuclear exercises in France, this
paper aims to study the population protection decision making process in the early phase of a severe nuclear
accident. This study underlines the existence of a potential intermediate episode in the population protection
strategy and how it is currently managed by civilian security and nuclear experts in an emergency situation. We
argue that in case of a large nuclear accident, nuclear expertise is essential and not sufficient to take decisions for
protecting population.
Keywords: Nuclear crisis management ، Population protection ، Crisis exercises ، Decision making ، Expert |
مقاله انگلیسی |