Planning water-energy-food nexus system management under multilevel and uncertainty
برنامه ریزی مدیریت سیستم Nexus آب-انرژی-مواد غذایی تحت چند سطح و عدم اطمینان-2020
In this study, a multi-level interval fuzzy credibility-constrained programming (MIFCP) method is developed for planning the regional-scale water-energy-food nexus (WEFN) system. MIFCP can not only deal with uncertainties expressed as interval parameters and fuzzy sets, but also handle conflicts and hierarchical relationships among multiple decision departments. The MIFCP approach is then applied to planning the WEFN system of Henan Province, China. Solutions of three different decision targets in various hierarchy levels, five scenarios with different decision makers’ objectives and five credibility levels toward different necessity degrees are examined. Several findings in association with various planting structures, water resources demand, energy consumption, fertilizer and pesticide utilizations and system benefits are achieved. Results reveal that the future total irrigation water can decrease by 1.49% from years 2020e2025. Results also disclose that the total cultivated area can change by 1.91% owing to the variation of fertilizer and pesticide change. Compared to single level programming (SLP) and bi-level programming (BP) approaches, the MIFCP-WEFN model can help decision-makers identify the optimal agricultural water resources management schemes by means of the leadership of water resources managers as well as the feedback of two diverse followers (i.e. energy managers and agricultural managers).
Keywords: Multi-level programming | Planning | Scenario analysis | Uncertainty | Water-energy-food nexus system
A bi-objective optimization approach for selection of passive energy alternatives in retrofit projects under cost uncertainty
یک روش بهینه سازی دو هدفه برای انتخاب گزینه های انرژی منفعل در پروژه های مقاوم سازی تحت عدم اطمینان هزینه-2020
Improving energy performance of buildings is of particular importance in new construction and existing buildings. Building refurbishment is considered a practical pathway towards energy efficiency as the replacement of older buildings is at a slow pace. There are various ways of incorporating energy conservation measures in buildings through refurbishment projects. As such, we have to choose among various passive or active measures. In this study, we develop an integrated assessment model to direct energy management decisions in retrofit projects. Our focus will be on alternative passive measures that can be included in refurbishment projects to reduce overall energy consumption in buildings. We identify the relative priority of these alternatives with respect to their non- monetary (qualitative) benefits and issues using an analytic network process. Then, the above priorities will form a utility function that will be optimized along with the energy demand and retrofit costs using a multi-objective optimization model. We also explore various approaches to formulate the uncertainties that may arise in cost estimations and incorporate them into the optimization model. The applicability and authenticity of the proposed model is demonstrated through an illustrative case study application. The results reveal that the choice of the optimization approach for a retrofit project shall be done with respect to the extent of variations (uncertainties) in expected utilities (benefits) and costs for the alternative passive technologies.
Keywords: Construction technologies | assive energy measures | Building retrofit | Multi-Objective Optimization | Cost uncertainty | Fuzzy set theory
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
Z-number based earned value management (ZEVM): A novel pragmatic contribution towards a possibilistic cost-duration assessment
مدیریت ارزش به دست آمده مبتنی بر عدد Z (ZEVM): سهم عملگرا جدید نسبت به ارزیابی هزینه تمام شده احتمالی-2020
The Earned value management (EVM) is one of the simplified analytical cost-duration assessment tools which assist project managers in monitoring the status of the project undertaken. The EVM has been elaborated by both deterministic and uncertain numbers such as fuzzy logic in the light of time. Even though cost-duration analysis is so sensitive and fluctuating in projects, the adopted approaches were unable to consider the conspicuous unreliability which is always involving the decision-making data. This problem impedes project managers to trust the foreseen inferences. To help in overcoming this critical deficiency, Z-numbers were proposed to take possibilities and reliabilities into account. Applying Z-numbers and possibilistic modeling in the EVM is a challenging topic which causes the accuracy of cost-duration tracing results to be significantly enhanced. This paper presents the application of z-numbers for modeling the earned value indicators and proves the superiority of the ZEVM against traditional fuzzy EVM. This work originally adds to the state-of-the-art literature on earned value management by presenting a proposal and applications of a new as Z-Earned Value Management (ZEVM). An illustrative case is resolved to magnify the capability of the proposed framework in dealing with higher levels of uncertainty associated with decision-making data.
Keywords: Earned value management | Fuzzy sets | Project evaluation | Uncertainty | Z-number
Imprecise earned duration model for time evaluation of construction projects with risk considerations
مدل مدت زمان به دست امده برای ارزیابی زمان پروژه های ساختمانی با ملاحظات ریسکی-2020
Monitoring and estimating the time performance of projects are two crucial factors which lead the companies to be prosperous. To address the issue, the most recent time-based method is earned duration management (EDM) which is used as an efficient technique. This research presents a new triangular intuitionistic fuzzy-EDM model to improve the applicability of time performance indices and to forecast under uncertain conditions. Since traditional fuzzy sets using membership degrees cannot deal with imprecise quantities in real cases, triangular intuitionistic fuzzy numbers (TIFNs) solve this issue by non-membership and hesitation degrees. In this sake, a TIFN as a special type of intuitionistic fuzzy sets (IFSs), defined on the set of real numbers, shows high capability of modeling ill-known and imprecise information in continuous domains. Besides, the importance of risks in time estimation of EDM is less considered in the literature. In this research, a new time-based risk performance indicator (TBRPI) is developed based on novel risk performance metrics (RPMs) to improve the project time performance estimating of EDM. In this respect, the significance of each RPM could increase the accuracy of the proposed approach, in which a new triangular intuitionistic fuzzy group decision method is presented for obtaining the RPMs and constituent factors (CFs)’ weights. Finally, a real case study about seawater intake basin construction project is provided to represent the applicability and feasibility of the proposed triangular intuitionistic fuzzy risk-based EDM model.
Keywords: Earned duration management | Construction projects | Risk performance indicator | Triangular intuitionistic fuzzy sets | Uncertainty
Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities
هوش مصنوعی در صنعت AEC: تجزیه و تحلیل ساینومتریک و تجسم فعالیتهای تحقیقاتی-2020
The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/ issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry.
Keywords: Architecture-engineering-construction | Artificial intelligence | Machine intelligence | Industry 4.0 | Automation | Digital transformation | Scientometric | Review
Veracity handling and instance reduction in big data using interval type-2 fuzzy sets
کنترل صحت و کاهش نمونه در داده های بزرگ با استفاده از بازها های مجموعه های فازی نوع 2-2020
Within the aspect of big data, veracity refers to the existing uncertainty in the dataset. The continuous flow of unstructured data with unwanted noise may bring abnormality in the dataset making them unusable. In this paper, we propose a novel method to handle the veracity characteristic of the big data using the concept of footprint of uncertainty (FOU) in interval type-2 fuzzy sets (IT2 FSs). The proposed method helps in handling the veracity issue in big data and reduces the instances to a manageable extent. We have compared the results with the existing clustering based methods and examined the relationship between the clusters and the FOUs by comparing their centroids and defuzzified values. To scrutinize the validity of our results, we have also performed a number of additional experiments by appending extra instances to the datasets. To check its consistency and efficacy, the proposed methodology is assessed from three different aspects. Experimental result validates that the proposed method can suitably handle the veracity issue in big datasets and is efficient in reducing the instances.
Keywords: Instance reduction | Big data veracity | Interval type-2 fuzzy sets | Cluster centroid | Footprint of uncertainty
Estimating construction waste generation in residential buildings: A fuzzy set theory approach in the Brazilian Amazon
تخمین تولید زباله ساخت و ساز در ساختمانهای مسکونی: یک رویکرد تئوری مجموعه فازی در آمازون برزیل-2020
The estimate of construction waste generation is the key decision-making information for policy-makers, construction managers, and the like to devise informed waste management strategies. However, estimating construction waste generated from projects is particularly onerous, as numerous factors related to design, site, and construction are largely in a fuzzy nature when the estimating job is conducted. Built upon previous studies, this paper seeks to develop a model that can be used to estimate construction waste generation based on fuzzy set theory. It follows a trilogy of methodology, including model development, sensitivity analysis, and model validation. A set of IF-THEN rules are developed based on two independent variables, built area and number of floors. A sensitive analysis was conducted to evaluate the influence of the independent variables on waste generation. The model is further calibrated and verified through a case study of 23 residential buildings constructed in the Brazilian Amazon. The model obtained an accuracy of 64.29% in the development phase and 66.67% in the validation phase, showing that the results are largely acceptable. By using this model, it is possible for a waste manager to draw up a baseline graph to indicate the volume of construction waste generation as his/her building project as it progresses. The research is also of novelty by using fuzzy set theory to deal with the fuzzy nature of waste generation in construction projects. Further studies are recommended to enhance the accuracy level of the model by engaging more factors and more quality data.
Keywords: Construction waste | Building | Waste quantification | Fuzzy set theory | Brazil
Driving factors for having visibility of sustainability contents in university degree titles
عوامل محرک جهت مشاهده مطالب پایداری در عناوین مدرک دانشگاهی-2020
Given the increasing concern about sustainability issues in society, graduates and postgraduates going into the labour market benefit from the inclusion of sustainability contents in university studies. Visibility of these contents is gained towards a potential employer if they are explicitly mentioned in the university degree title. This is the first paper to explore this trend. More precisely, the purpose of this study is to find explanatory causal combinations of factors for this trend in the Spanish setting. The methodological approach of this paper is based on fuzzy sets. More precisely, it follows fuzzy-sets Qualitative Comparative Analysis, which is an innovative approach in the field of Higher Education yet a consolidated method in Sociology, Marketing or Management studies. This new methodology is well suited for rather limited sample sizes. The findings obtained reveal several causal combinations of internal and strategic factors leading to innovation in university degree catalogues by means of making visible the sustainable focus in the title of the university degrees. According to the results obtained, the public status, prestige and the sustainable profile adequately combined with some other characteristics (for instance large size or age) explain this phenomenon. This article uses an innovative methodological approach and makes a valuable contribution as it identifies the factors behind the early adopters, i.e. universities covering this “green niche” in education.
Keywords: Higher education | Sustainability | Degrees | Fuzzy-sets | fsQCA | Innovation
Representation by levels: An alternative to fuzzy sets for fuzzy data mining
نمایندگی براساس سطوح: جایگزینی برای مجموعه های فازی برای داده کاوی فازی-2019
In this paper we describe and discuss the main contributions of the representation by levels approach to fuzzy data mining. Representation by levels is an alternative representation of fuzziness in information and data, which is complementary to fuzzy sets in the sense that it provides tools and algebraic structures beyond the capabilities of fuzzy set theories, based on t-norms, t-conorms and fuzzy negations. Our approach allows to extend any crisp mining technique to the fuzzy case in a simple way, keeping all the properties of the crisp technique. We illustrate our discussion with examples and existing approaches based on representation by levels to fuzzy association rules and the related issues of mining exception/anomalous rules and mining fuzzy bag databases.
Keywords:Representation by levels | Fuzzy data mining | Assessment measures | Fuzzy association rules | Mining bag databases