119 research outputs found

    Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

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    The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

    Fuzzy Sets, Fuzzy Logic and Their Applications 2020

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    The present book contains the 24 total articles accepted and published in the Special Issue “Fuzzy Sets, Fuzzy Logic and Their Applications, 2020” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of fuzzy sets and systems of fuzzy logic and their extensions/generalizations. These topics include, among others, elements from fuzzy graphs; fuzzy numbers; fuzzy equations; fuzzy linear spaces; intuitionistic fuzzy sets; soft sets; type-2 fuzzy sets, bipolar fuzzy sets, plithogenic sets, fuzzy decision making, fuzzy governance, fuzzy models in mathematics of finance, a philosophical treatise on the connection of the scientific reasoning with fuzzy logic, etc. It is hoped that the book will be interesting and useful for those working in the area of fuzzy sets, fuzzy systems and fuzzy logic, as well as for those with the proper mathematical background and willing to become familiar with recent advances in fuzzy mathematics, which has become prevalent in almost all sectors of the human life and activity

    INTERVAL TYPE-2 FUZZY MODEL FOR CUSTOMER COMPLAINT HANDLING

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    Complaint management system (CMS) has become increasingly important for organizations, businesses, and government in Malaysia. The interaction between customers and business provider based on complaints which referring to perceptions and wording involves uncertainties and not an easy task in complaint handling process to rank the complaint

    Sustainable Assessment in Supply Chain and Infrastructure Management

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    In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management

    Methodology to predict construction contractors’ performance using non-price measures

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    Despite being one of the largest industry sectors in the world, construction continues to suffer from underperformance. Contractors are the driving force behind built assets, and selecting high-performing contractors is crucial to the success of construction projects. However, the industry lacks a systematic and purpose-driven method of assessing contractors’ performance using objective metrics. Furthermore, contractors do not have a systematic way to gauge their own performance in the pursuit of continuous improvement. Although there are numerous approaches to the measurement of contractors’ performance, the literature suggests that most are complicated and highly dependent on data that are difficult to attain. The research presented in this thesis addresses this knowledge gap by creating a model for predicting construction contractors’ performance based on directly attributable measures that are quantitatively measurable and easily accessible. The findings of this research make a number of contributions to theory and practice. The developed performance model—the Contractors’ Performance Index (CPIx) provides a performance score based on seven non-price CMoPs. As the CPIx is based on factors that are within the control of the contractor, it provides a fair and independent assessment of performance that is not influenced by other factors. In an industry significantly driven by pricebased decisions that are solely based on non-price measures, the CPIx shifts the focus towards other aspects such as quality, health and safety, sustainability and productivity when evaluating performance, leaving price based measures for commercial considerations. Contractors can use the CPIx to self-evaluate their levels of project and organisational performance. If implemented as a sector-based performance evaluator, it can then be used to develop industry benchmarks for different categories of construction. The CPIx is presented as a prototype mobile application that can be conveniently used by various stakeholders to track performance within the construction industry

    Three Essays on “Energy , Environment, and Developmental Economics”

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    This dissertation examines topics related to renewable energy development and investment planning, energy markets, environment degradation and economic development. The substantial ecological costs of deforestation are well known and considered globally important due to biodiversity loss, land degradation, soil erosion, and contributions to climate change. The first essay focuses upon understanding the tradeoff between development and deforestation in Africa. In the second essay, spatial analysis and Geographic Information System (GIS) are applied to determine potential locations for wind farms development in the state of West Virginia. Lastly, the third essay examines the role of wind power penetration on wholesale electricity market. The first essay explores the relationship between economic growth and deforestation in African countries. During the past half-century, the continent of Africa has suffered massive losses of forested areas due to the changing structure of economies, increasing population, and expanding globalization. This research examines statistical evidence for the Environmental Kuznets Curve (EKC) hypothesis as applied to deforestation occurring within Africa from 1990 to 2016. Changes in forest cover data are explained with Generalized Method of Moments (GMM) estimators to overcome the endogeneity problems arising from reverse causality between deforestation and explanatory variables. The empirical results of a panel GMM confirm the EKC hypothesis is valid for deforestation in Africa with a turning point estimated to be US 3,000.HeterogenouspanelnoncausalityfindingssuggestthatAfricacoulddeterandreversedeforestationthroughappropriatelanduseandforestproductstradepolicies,andtheconsequencesofthesepolicieswouldnotimpacttheireconomicgrowth.Inthesecondessay,amulticriteriadecisionanalysisemployingAnalyticHierarchyProcess(AHP)andGISareusedforassessmentofpotentialsitesforfutureutilityscalewindfarmsinWestVirginia.Worldwide,demandisincreasingforrenewableenergy.Whilewindpowerisaproven,sustainableenergysource,sitingcanbechallenging.Identifyingpotentialsitesforwindturbinesisasignificantstepinrenewableenergyresourceplanning.Windturbinesitesuitabilityisprimarilydependentuponwindspeedatalocationalongwithotherenvironmental,social,andeconomicfactors.Itiscriticaltoarriveatarobustwindfarmdecisiontoimprovepublicacceptance,preservetheenvironment,andmaintainpristinehabitats.Thisresearchbuildsuponpreviousstudiesandcontributestotheliteraturebyincorporatingtwoimportantcomponentsintositing:(1)inclusionofcriticalwildlifehabitatforbirdsandbatsasaneliminationcriterionwithintheAHP,and(2)theparticipationbywindpowerexpertsintheAHPdecisionmakingprocess.Byincorporatingexpertopinionswiththeweighingoftensitingfactors,about70,000hectaresoflandareidentifiedas2˘7highlysuitable2˘7forwindpowerdevelopmentthroughoutthestateofWestVirginia.Thisarearepresentsthepotentialtoyieldanestimated29,000MWoffutureutilityscalewindpowercapacity,whichislargerthanthecurrentcoaldominatedelectricitygenerationcapacityinWestVirginia.Thethirdessayexaminesthewindpowerpenetrationimpactsonwholesaleelectricitymarkets.Usingdatafromtwowholesaleelectricitymarkets(PennsylvaniaNewJerseyMaryland(PJM)andElectricReliabilityCouncilofTexas(ERCOT)),fourenergypolicyquestionsareaddressed:(1)Howmuchdoeswindpowerintegrationimpactwholesaleelectricitypricesunderdifferentmarkets?(2)Doesthemeritordereffect(MOE)existatdifferentquantilesofwholesaleelectricityprices?(3)Whatdrivesthedayaheadmarket(DAM)andrealtimemarket(RTM)pricesatdifferentmarketconditionsinbothmarkets?(4)Doestheincreasingpenetrationofwindpowerundermineitsmarketvaluealongwiththemarketvaluesofothergeneratingtechnologies?Toanswerthesequestions,quantileregressionisusedtoobtaincoefficientestimatesthatindicatewindpenetrationhasunequalimpactsonwholesaleelectricitypricesandmarketvaluesacrossquantiles,reinforcingtheneedforthistypeofanalysis.TheempiricalanalysesconfirmedtheexistenceofthemeritordereffectacrossdifferentquantilesoftheconditionaldistributionofwholesalepricesforbothDAMandRTM,implyingthattheincreasingdeploymentofwindpowerforelectricitygenerationsignificantlysuppressesthewholesaleelectricitypricesinthePJMmarket.ContrarytothePJMestimations,meritordereffectsareconfirmedacrossquantilesofwholesalepricesforonlytheDAMintheERCOTmarket.Furthermore,thefindingsshowthataswindgenerationexpandswithinthemarket,therevenueearnedbywindpowerproducersreducesacrossallquantilesoftheconditionaldistributionofitsunitrevenue.Specifically,eachadditionalGWhincreaseinelectricityfromwindisassociatedwithafallinitsunitrevenuesacrossquantilesbyanamountthatrangesfromapproximately3,000. Heterogenous panel non-causality findings suggest that Africa could deter and reverse deforestation through appropriate land-use and forest products trade policies, and the consequences of these policies would not impact their economic growth. In the second essay, a multi-criteria decision analysis employing Analytic Hierarchy Process (AHP) and GIS are used for assessment of potential sites for future utility-scale wind farms in West Virginia. Worldwide, demand is increasing for renewable energy. While wind power is a proven, sustainable energy source, siting can be challenging. Identifying potential sites for wind turbines is a significant step in renewable energy resource planning. Wind turbine site suitability is primarily dependent upon wind speed at a location along with other environmental, social, and economic factors. It is critical to arrive at a robust wind farm decision to improve public acceptance, preserve the environment, and maintain pristine habitats. This research builds upon previous studies and contributes to the literature by incorporating two important components into siting: (1) inclusion of critical wildlife habitat for birds and bats as an elimination criterion within the AHP, and (2) the participation by wind power experts in the AHP decision-making process. By incorporating expert opinions with the weighing of ten siting factors, about 70,000 hectares of land are identified as \u27highly suitable\u27 for wind power development throughout the state of West Virginia. This area represents the potential to yield an estimated 29,000 MW of future utility-scale wind power capacity, which is larger than the current coal dominated electricity generation capacity in West Virginia. The third essay examines the wind power penetration impacts on wholesale electricity markets. Using data from two wholesale electricity markets (Pennsylvania – New Jersey – Maryland (PJM) and Electric Reliability Council of Texas (ERCOT)), four energy policy questions are addressed: (1) How much does wind power integration impact wholesale electricity prices under different markets? (2) Does the merit-order effect (MOE) exist at different quantiles of wholesale electricity prices? (3) What drives the day-ahead market (DAM) and real-time market (RTM) prices at different market conditions in both markets? (4) Does the increasing penetration of wind power undermine its market value along with the market values of other generating technologies? To answer these questions, quantile regression is used to obtain coefficient estimates that indicate wind penetration has unequal impacts on wholesale electricity prices and market values across quantiles, reinforcing the need for this type of analysis. The empirical analyses confirmed the existence of the merit-order effect across different quantiles of the conditional distribution of wholesale prices for both DAM and RTM, implying that the increasing deployment of wind power for electricity generation significantly suppresses the wholesale electricity prices in the PJM market. Contrary to the PJM estimations, merit-order effects are confirmed across quantiles of wholesale prices for only the DAM in the ERCOT market. Furthermore, the findings show that as wind generation expands within the market, the revenue earned by wind power producers reduces across all quantiles of the conditional distribution of its unit revenue. Specifically, each additional GWh increase in electricity from wind is associated with a fall in its unit revenues across quantiles by an amount that ranges from approximately 0.01/MWh to 0.06/MWh.ResultsalsoconfirmcrosscannibalizationimpactssuchthateachadditionalGWhincreasefromwindisassociatedwithdecreasedgasandbaseloadunitrevenuesacrossallquantilesrangingfrom0.06/MWh. Results also confirm cross-cannibalization impacts such that each additional GWh increase from wind is associated with decreased gas and baseload unit revenues across all quantiles ranging from 0.02/MWh to $0.06/MWh. Contrary to unit revenue results, there is weak evidence of increasing wind supply\u27s cannibalization effect for value factor as positive impacts occur below the 90% quantile and negative impacts occur at quantiles 90% and greater. The negative impacts of wind power on gas and baseload generators demonstrate the need for corrective policies

    Comparison of Correlation for Asian Shariah Indices Using DCC-GARCH and Rolling Window Correlation.

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    This paper aims to compare the capability of correlation in capturing the volatility using rolling window correlation and Dynamic Conditional Correlation - Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) approach. This study will perform a DCC-GARCH to estimate the dynamic conditional correlation between the Asian Shariah indices. The Asian Shariah index comprises FTSE SGX Asia Shariah 100, FTSE Bursa Malaysia Emas Shariah Index, FTSE Greater China Shariah Index, and FTSE Stock Exchange of Thailand (SET) Shariah Index. The correlation estimation considers the FTSE SGX Asia Shariah 100 as a proxy. The World Health Organization (WHO) declared the Coronavirus 2019 (COVID-19) as pandemic on 11th March 2020. Therefore, the data used covers six months before and after 11th March 2020, from 11th September 2019 until 11th September 2020. The output of both effected correlations towards the Covid-19 will be evaluated based on their ability to capture the time-varying changes through graph plotting. The empirical findings show that the DCC-GARCH is better at capturing the highly changes volatility than the rolling window correlation

    Advances in Theoretical and Computational Energy Optimization Processes

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    The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

    INTERVAL TYPE-2 FUZZY MODEL FOR CUSTOMER COMPLAINT HANDLING

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    Complaint management system (CMS) has become increasingly important for organizations, businesses, and government in Malaysia. The interaction between customers and business provider based on complaints which referring to perceptions and wording involves uncertainties and not an easy task in complaint handling process to rank the complaint
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