Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

A Political Economy Model of Regulation Explained Through Fuzzy Logics

The basic problem of environmental regulation involves the government trying to induce a polluter to take socially desirable actions, which ostensibly are not in the best interest of the polluter. But the government may not always be able to precisely control the polluter. To further complicate matters the government faces a complex problem of determining exactly what level of pollution is best for society. In reality the government faces pressures from consumers and polluters. There are some important lessons to gather from the analysis of current models of regulation. One is that there are many imperfect links between the legislature and the pollution-generating process. In this case regulation may be excessively costly, may result in considerable cheating, and may result in excessive pollution. Another lesson is that legislature does not necessarily act as an efficient benevolent maximizer of social well-being. The authors intend in this paper to explain the current view of political models of regulation, analysing them for their complexity, and attempt to provide a reasonable explanation of their functioning recurring to fuzzy logics. Understanding how the browns and greens interact with the legislature and regulatory agencies can to some extent explain the current environmental regulations. The fuzzy approach, intends to allow for easier understanding of these interactions, and provide an answer for more effective decision making. Keywords: Environmental Regulation, Environmental Economics, Fuzzy Logics, Models, Pollution Control, Sustainability

FUZZY COMPARATIVE CONCORDANCE ANALYSIS. Proposal and evaluation by a case study

In this paper it is proposed a fuzzy multiple attribute analysis, that we have called comparative concordance, as a help instrument to the decision-making process in an environment of lack of precise information as it generally is the decision-making in regional planning. Through an application to the selection of proceeding programs of the Environmental Plan of Andalusia, 1995-2000, it will be compared to other methods.fuzzy sets, multiple attribute decision, environmental planning

Life Cycle Based Sustainability Assessment And Decision Making For Industrial Systems

Increasing concern with the environmental impact resulted from human activities has led to a rising interest in sustainable development that will not only meet the needs of current development but also protect the natural environment without compromising the needs of future generations. This leads to the necessity of a systems approach to decision-making in which economic, environmental and social factors are integrated together to ensure the triple bottom lines of sustainability. Although current studies provide a variety of different methodologies to address sustainability assessment and decision-making, the increasing size and complexity of industrial systems results in the necessity to develop more comprehensive systems approaches to ensure the sustainable development over a long time period for industrial systems. What\u27s more, current research may conduct results based on one or only a few stages of the manufacturing process without considering all the stages of a product’s life. Therefore, the results could be bias and sometimes not feasible for the whole life-cycle. In the meanwhile, life cycle analysis (LCA) which has been widely adopted in a variety of industries does provide an effective approach to evaluate the environmental impact. The lack of life-cycle based economic and social sustainability assessment results in the difficult to conduct more comprehensive sustainability assessment. To address these challenges, three fundamental frameworks are developed in this dissertation, that is, life cycle based sustainability assessment (LCBSA) framework, life cycle based decision-making (LCBDM) framework, and fuzzy dynamic programming (FDP) based long-term multistage sustainable development framework. LCBSA can offer a profound insight of status quo of the sustainability performance over the whole life cycle. LCSA is then applied to assess the industrial system of automotive coating manufacturing process from raw material extraction, material manufacturing, product manufacturing to the recycle and disposal stage. The following LCBDM framework could then prioritize the sustainability improvement urgency and achieve comprehensive sustainable development by employing a two-phase decision-making methodology. In addition, FDP based long-term multistage sustainable development framework offers a comprehensive way to ascertain the achievement of long time sustainable development goal of complex and dynamic industrial systems by combining decision-making and sustainability assessment together