scholarly journals A MODEL TO REDUCE EARTHMOVING IMPACTS

2020 ◽  
Vol 26 (6) ◽  
pp. 490-512
Author(s):  
Hassanean S.H. Jassim ◽  
Jan Krantz ◽  
Weizhuo Lu ◽  
Thomas Olofsson
Keyword(s):  
Co2 Emissions ◽  
Energy Use ◽  
Construction Sites ◽  
Competing Demands ◽  
Effective Decision ◽  
Heavy Duty Diesel ◽  
Effective Decision Making ◽  
Rule Based Approach ◽  
Allocation Methods

Meeting increasingly ambitious carbon regulations in the construction industry is particularly challenging for earthmoving operations due to the extensive use of heavy-duty diesel equipment. Better planning of operations and balancing of competing demands linked to environmental concerns, costs, and duration is needed. However, existing approaches (theoretical and practical) rarely address all of these demands simultaneously, and are often limited to parts of the process, such as earth allocation methods or equipment allocation methods based on practitioners’ past experience or goals. Thus, this study proposes a method that can integrate multiple planning techniques to maximize mitigation of project impacts cost-effectively, including the noted approaches together with others developed to facilitate effective decision-making. The model is adapted for planners and contractors to optimize mass flows and allocate earthmoving equipment configurations with respect to tradeoffs between duration, cost, CO2 emissions, and energy use. Three equipment allocation approaches are proposed and demonstrated in a case study. A rule-based approach that allocates equipment configurations according to hauling distances provided the best-performing approach in terms of costs, CO2 emissions, energy use and simplicity (which facilitates practical application at construction sites). The study also indicates that trucks are major contributors to earthmoving operations’ costs and environmental impacts.

Designing a Business Analytics Culture in Organizations in India

2021 ◽  
pp. 670-678
Author(s):  
Tanushri Banerjee ◽  
Arindam Banerjee
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Data Organization ◽  
Business Analytics ◽  
Domain Specific ◽  
Consumer Lending ◽  
Effective Decision ◽  
Effective Decision Making

There are several challenges faced by decision makers while deploying Business Analytics in their organization. There may not be one resolution approach that is suitable for creating a Business Analytics culture in all organizations. However, it is easy to perceive that most India-based organizations may have similar issues of data organization that may be impeding their progression in the field of Analytics. Based on their research, the authors have proposed a framework for adoption of Analytics in Indian firms in their book “Weaving Analytics for Effective Decision Making” by SAGE. They propose to use that model for explaining certain domain specific adoption of Business Analytics in organizations in India. They have used a case study of a Global Bank which is in the process of establishing its consumer lending USA operations, an offshore captive operation, in India to describe the process of building an Analytics team in an organization in India. Data processed using R has been added as screenshots for supporting the findings.

PERFORMANCE OF SUSTAINABLE BUILDINGS IN COLDER CLIMATES

2016 ◽  
Vol 11 (4) ◽  
pp. 131-153 ◽  
Author(s):  
Mark Gorgolewski ◽  
Craig Brown ◽  
Anne-Mareike Chu ◽  
Adrian Turcato ◽  
Karen Bartlett ◽  
...  
Keyword(s):  
Construction Industry ◽  
Water Use ◽  
Energy Use ◽  
Building Performance ◽  
Design Teams ◽  
Building Management ◽  
Effective Decision ◽  
Effective Decision Making ◽  
The Individual ◽  
Occupant Satisfaction

Building performance evaluations (BPEs) were carried out for nine Canadian green buildings using a standardised assessment framework. The aim was to explore and measure the discrepancies between the operational performance of the buildings and their predicted performance, as well as to identify lessons for their owners, design teams and the construction industry. The objective of this paper is not to report individual buildings in detail (we refer the reader to the individual building reports) but to report on some general lessons that came from doing this study. Overall these buildings performed well compared to benchmarks. However, the findings suggest that occupancy is not well understood and often incorrectly predicted during design, and that this affects various aspects of performance, including energy and water use. Also energy and water use modelling is often undertaken principally for building code/green rating compliance purposes and does not necessarily represent an accurate prediction of likely operational use. Combined with variations in occupancy this can lead to considerable discrepancies in performance from the modelled values. This may be understood by experts but is often misleading to building owners and others. Water use is often not well predicted and also not carefully managed in buildings and there is a lack of understanding of what constitutes good water performance. Overall, it is important to recognise that each building has its own individual “story” that provides necessary context for effective management and improvement of the building during its ongoing life. It is proposed that a BPE process allows that context to be better understood, and enables more effective decision making about building management, improvements, occupant satisfaction, energy use, etc.

A descriptive case study of effective decision-making of a local government organization in the United States of America

2020 ◽  
Vol 16 (3) ◽  
pp. 279-297
Author(s):  
Jennifer Capler
Keyword(s):  
United States ◽  
Decision Making ◽  
Local Government ◽  
United States Of America ◽  
The United States ◽  
Content Type ◽  
Government Organizations ◽  
Effective Decision ◽  
Effective Decision Making

PurposeThis article details a qualitative descriptive case study of affective factors of effective decision-making of one local government organization in the United States of America. The specific problem was that many elected American local government representatives lack effective decision-making strategies. This research focus indicated a lack of qualitative research on the real-world experience of factors that were taken into consideration during decision-making within American local government organizations.Design/methodology/approachUsing a local government organization in southwest Illinois, elected representatives were interviewed and observed. The interviews and observations surfaced how the representatives made decisions. Data were analyzed using manual coding and theming to determine themes and patterns.FindingsThe results produced six themes about factors, including emotional intelligence, which impacted decision-making. They are: (1) remembering the past, (2) communication and respect, (3) spurring economic growth and development, (4) fairness, (5) recognizing and removing emotions and bias and (6) accountability.Research limitations/implicationsBeing a single case study, this research is limited in generalization. The research was limited to the identification of current, real-world experience of elected local government representatives.Practical implicationsThe findings of this research can be used to create more effective decision-making practices for local government organizations of similar size.Originality/valueThis is the first study to review, in-depth, the decision-making and emotional intelligence factors of local government organizations in the United States of America. The conceptual background, discussion, implications to local government organizations, limitations and recommendations for future studies are discussed.

scholarly journals Urban Vitality Evaluation and Spatial Correlation Research: A Case Study from Shanghai, China

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1195
Author(s):  
Jiangang Shi ◽  
Wei Miao ◽  
Hongyun Si ◽  
Ting Liu
Keyword(s):  
Spatial Distribution ◽  
Urban Development ◽  
Central Area ◽  
Spatial Distribution Characteristics ◽  
Primary Driver ◽  
Effective Decision ◽  
Development Evaluation ◽  
Effective Decision Making ◽  
The City

Urban vitality is the primary driver of urban development. However, assessing urban vitality has always been a challenge. This paper builds on the research framework of sustainable development evaluation and selects evaluation indicators from the three systems of urban operation: economy, society, and environment. The deviation maximization (DM) method is used to evaluate urban vitality. Shanghai is then used as a case study for evaluation, and the comprehensive index of urban vitality is calculated for the city from 2010 to 2019. The evaluation results indicate that the urban vitality of Shanghai experienced a significant upward trend over ten years (2010–2019), which shows that the urban competitiveness of Shanghai is constantly strengthening. Next, the study focuses on the administrative region of Shanghai, to calculate the regional vitality level of Shanghai from 2010 to 2019 and to explore its spatial distribution characteristics. Then, a spatial autocorrelation analysis is used to explore the mechanism that affects the spatial distribution of urban vitality. The results demonstrate that the urban vitality in Shanghai shows a significant positive correlation in space. Moreover, there is a “High–High” gathering area, which includes Huangpu, Xuhui, Hongkou, and Changning in central area of Shanghai. This research provides a theoretical reference to support effective decision-making with respect to high-quality urban development.

scholarly journals Decision-Making Enhancement in a Big Data Environment: Application of the K-Means Algorithm to Mixed Data

2019 ◽  
Vol 9 (4) ◽  
pp. 293-302
Author(s):  
Oded Koren ◽  
Carina Antonia Hallin ◽  
Nir Perel ◽  
Dror Bendet
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Mixed Data ◽  
Systems Research ◽  
Information Systems Research ◽  
Effective Decision ◽  
Data Ecosystem ◽  
Effective Decision Making ◽  
Business Requirements

Abstract Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.

Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

2012 ◽  
pp. 242-261 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Control ◽  
Future Research ◽  
Manufacturing Environment ◽  
Planning And Scheduling ◽  
Future Research Directions ◽  
Effective Decision ◽  
Effective Decision Making

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

2012 ◽  
pp. 171-192 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Control ◽  
Future Research ◽  
Manufacturing Environment ◽  
Planning And Scheduling ◽  
Future Research Directions ◽  
Effective Decision ◽  
Effective Decision Making

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

Decision Making Support Model for the Incident Emergency Management of Migrant’s Group Based on CBR

2014 ◽  
Vol 989-994 ◽  
pp. 1704-1711
Author(s):  
Ding Hua Zhang ◽  
Liang Cheng ◽  
Ren Wei Wu ◽  
Jing Wang ◽  
Jing Yi Liu
Keyword(s):  
Decision Making ◽  
Emergency Management ◽  
Guangdong Province ◽  
Management Problem ◽  
Ahp Model ◽  
Factors Associated ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Support Model

This paper presents the findings of a study on decision making models for the migrant’s worker group incident emergency management based on Analytic Hierarchic Process (AHP). Analytic Hierarchic Process (AHP) helps to quantitate and layer the complex migrant’s worker incident emergency management problem, the aim of the work is to improve the support for analysis and decision through the importance comparing of various factors associated layer by layer.A case study conduct in Wukan village Shantou city of Guangdong province revealed that AHP model can do effective decision making

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