ABSTRACT Cities throughout Asia have experienced unprecedented economic development over the past decades. In many cases this has contributed to their rapid and uncontrolled growth, which has resulted in a multiplicity of problems, including rapid population increase, enhanced environmental pollution, collapsing traffic systems, dysfunctional waste management, and rapid increases in the consumption of energy, water and other resources. The significant energy use in cities is not very well perceived in Asian countries. Although a number of studies into energy consumption across various sectors have been conducted, most are from the national point of view. Energy demand analysis is not considered important at the level of the city. The thesis is focused on the dynamics of energy utilization in Asian mega-cities, and ultimately aims at providing strategies for maximizing the use of renewable energy in large urban systems. The study aims at providing an in-depth understanding of the complex dynamics of energy utilization in urban mega-centers. An initial general analysis is complemented by a detailed study of the current situation and future outlook for the city of Bangkok, Thailand. An integrated approach applied to the study includes identification of the parameters that affect the utilization of energy in mega-cities and a detailed analysis of energy flows and their various subsystems, including commercial, industrial, residential and that of transportation. The study investigates and evaluates the energy models most commonly used for analyzing and simulating energy utilization. Its purpose is to provide a user-friendly tool suitable for decision-makers in developing an energy model for large cities. In addition, a Multi-Criteria Decision-Making (MCDM) process has been developed to assess whether or not the energy systems meet the sustainability criteria. A metabolic approach has been employed to analyze the energy flow and utilization in selected Asian mega-cities, including Bangkok, Beijing, Shanghai, and Tokyo. The approach is applied to measure the majority of indirect energy flows or the energy embodied in the flows of goods and services involving the residents of those cities. Since the function of cities is to serve the lives of the residents, indirect energy consumption could be regarded as being of equal importance as that of direct energy use. The essence of embodied energy is that an indirect reflection upon behavior following direct energy consumption. It can illustrate how a city relies on the outside, for example other cities, countries, etc. and provides some interesting information that cannot be easily drawn from the direct energy demand. The study reveals that the indirect energy demand is more significant than the direct energy demand in Bangkok, Shanghai, and Tokyo, while direct energy demand is greater than the indirect energy demand in Beijing. This can be explained by the fact that Bangkok, Shanghai, and Tokyo have a greater reliance upon the outside in terms of energy demand. The Long-range Energy Alternative Planning (LEAP) system has been selected to perform Bangkok energy modeling. In a Bangkok case study a range of policy interventions are selected and how these would change the energy development in Bangkok by the year 2025 is examined. Different policies can be grouped by the sectors analyzed. The only supply-side policy considered meets an existing target of having 10% of electricity generated from renewable sources. The study period for the model started in 2005 and ends in 2025, with the year 2000 taken as the base year. The proposed scenarios were evaluated using the MCDM approach to rate their sustainability. Team members found that this method provided a methodology to help decision-makers to systematically identify management objectives and priorities. / QC 20101123

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第23591号 / 地環博第218号 / 新制||地環||42(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 宇佐美 誠, 教授 諸富 徹, 准教授 吉野 章 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DGAM

<p dir="ltr">Most road fatalities are caused by human error. To help mitigate this issue and enhance overall transportation safety, companies are turning to advanced driver assistance systems and autonomous vehicle development. Perception, a key module of these systems, mostly uses light detection and ranging (LiDAR) sensors and enables efficient obstacle detection and environment mapping. Extensive research on the use of LiDAR for autonomous driving has been documented in the literature. Yet still, several researchers and practitioners have advocated continued investigation of LiDAR placement alternatives. To address this research need, this thesis research begins with a comprehensive review of different sensor technologies – camera, radio detection and ranging, global positioning system, and inertial measurement units – and exploring their inherent strengths and limitations. Next, the thesis research developed a methodological multiple criteria framework and implemented it in the context of LiDAR placement optimization. Given the numerous criteria and placement alternatives associated with LiDAR placement, multi-criteria decision analysis (MCDA) was identified as an effective tool for LiDAR placement optimization. MCDA has been applied to some extent in decision-making regarding autonomous vehicle development. However, its application in LiDAR placement optimization remains unexplored. In evaluating the LiDAR placement alternatives, the research first established the placement alternatives and then developed a comprehensive yet diverse set of criteria – point density, blind spot regions, sensor cost, power consumption, sensor redundancy, ease of installation, and aesthetics. The data collection methods included the CARLA simulator, sensor datasheets, and questionnaire surveys. The relative importance among the evaluation criteria was established using weighting techniques such as respondent-assigned weighting, equal weighting, and randomly generated weighting. Then, to standardize the different measurement units, scaling was carried out using value functions developed for each criterion using data from the respondents. Finally, the weighted and scaled criteria measures were amalgamated to obtain the overall evaluation score for each alternative LiDAR placement design. This enabled the ranking of the placement designs and the identification of the best-performing and worst-performing designs. Hence, the optimization method used is the enumeration technique. The findings of this study serve as a reference for future similar efforts that seek to optimize LiDAR placements based on select criteria. Further, it is expected that the thesis’s framework will contribute to an enhanced understanding of the overall impact of LiDAR placement on autonomous vehicles, thus enabling the cost-effective design of their placement and, ultimately, improving AV operational outcomes, including traffic safety.</p>

Yes / Purpose - The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and ser- vices. To mitigate these disruptions, it is essential to identify the barriers that have im- peded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN). Design/methodology/approach - To determine the relative importance of different bar- riers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pan- demic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers. Findings - The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%). Research limitation/implications - This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions. Originality/value - This study enhances our understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

Information technology (IT) investment decision makers are required to process large volumes of complex data. An existing body of knowledge relevant to IT portfolio management (PfM), decision analysis, visual comprehension of large volumes of information, and IT investment decision making suggest Multi-Criteria Decision Making (MCDM) and hypervariate display techniques can reduce cognitive load and improve decision confidence in IT PfM decisions. This dissertation investigates improving the decision confidence by reducing cognitive burden of the decision maker through greater comprehension of relevant decision information. Decision makers from across the federal government were presented with actual federal IT portfolio project lifecycle costs and durations using hypervariate displays to better comprehend IT portfolio information more quickly and make more confident decisions. Other information economics attributes were randomized for IT portfolio projects to generate Balanced Scorecard (BSC) values to support MCDM decision aids focused on IT investment alignment with specific business objectives and constraints. Both quantitative and qualitative measures of participant comprehension, confidence, and efficiency were measured to assess hypervariate display treatment and then MCDM decision aid treatment effectiveness. Morae Recorder Autopilot guided participants through scenario tasks and collected study data without researcher intervention for analysis using Morae Manager. Results showed improved comprehension and decision confidence using hypervariate displays of federal IT portfolio information over the standard displays. Both quantitative and qualitative data showed significant differences in accomplishment of assigned IT portfolio management tasks and increased confidence in decisions. MCDM techniques, incorporating IT BSC, Monte Carlo simulation, and optimization algorithms to provide cost, value, and risk optimized portfolios improved decision making efficiency. Participants did not find improved quality and reduced uncertainty from optimized IT portfolio information. However, on average participants were satisfied and confident with the portfolio optimizations. Improved and efficient methods of delivering and visualizing IT portfolio information can reduce decision maker cognitive load, improve comprehension efficiency, and improve decision making confidence. Study results contribute to knowledge in the area of comprehension and decision making cognitive processes, and demonstrate important linkages between Human-Computer Interaction (HCI) and Decision Support Systems (DSS) to support IT PfM decision making.

A dynamic decision making framework implementing multi criteria routing of multimedia services at private-public network border with access technology convergence is presented. The ingredients of the framework include information model, semantics capturing via ontology, information sharing and dissemination mechanisms and rule/policy specifications methodology. The control and management over the infrastructure is carried out by revamping the sole signaling protocols (SIP, diameter and SNMP). DEN-ng is enhanced and tagged in accordance with the requirements over the underlying framework. A dedicated language for the platform is proposed that has its deep roots inside the framework to avoid conflicts and overlapping. A dynamic decision engine is developed for routing the requests/sessions at private-public network border over the underlying multi-homed environment. Multi Criteria Decision Making (MCDM) theory is used for decision computation/calculation and the adapted methods are exploited according to the scenario and decision computation mode while keeping in view the corresponding enforcement mode. A test bed is developed to validate the proposed framework. The proposed system offers higher throughput and lowers call-dropping probability with an add-on susceptible delay.

Transport infrastructure investment decision making is typically based on a range of inputs such as social, environmental and economic factors. The benefit cost ratio (BCR), a measure of economic efficiency (“value for money”) determined through cost benefit analysis (CBA), is dependent on accurate estimates of the various option costs and net social benefits such as reductions in travel time, accidents, and vehicle operating costs. However, most evaluations are deterministic procedures using point estimates for the inputs and producing point estimates for the outputs. Transport planners have primarily focused on the cost risks and treat risk through sensitivity testing. Probabilistic risk analysis techniques are available which could provide more information about the statistical confidence of the economic evaluation outputs. This research project report investigated how risk and uncertainty are dealt with in the literature and guidelines. The treatment of uncertainty in the Nelson Arterial Traffic Study (ATS) was reviewed and an opportunity to apply risk analysis to develop probabilities of sea level rise impacting on the coastal road options was identified. A simplified transport model and economic evaluation case study based on the ATS was developed in Excel to enable the application of @RISK Monte Carlo simulation software. The simplifications mean that the results are not comparable with the ATS. Seven input variables and their likely distributions were defined for simulation based on the literature review. The simulation of seven variables, five worksheets, and 10,000 iterations takes about 30 seconds of computation time. The input variables in rank order of influence on the BCR were capital cost, car mode share, unit vehicle operating cost, basic employment forecast growth rate, and unit value of time cost. The deterministically derived BCR of 0.75 is associated with a 50% chance that the BCR will be less than 0.6, although this probability is partly based on some statistical parameters without an empirical basis. In practice, probability distribution fitting to appropriate datasets should be undertaken to better support probabilistic risk analysis conclusions. Probabilities for different confidence levels can be reported to suit the risk tolerance of the decision makers. It was determined that the risk analysis approach is feasible and can produce useful outputs, given a clear understanding of the data inputs and their associated distributions.

<p>U radu su istražene mogućnosti za razvoj modela za izbor<br />lokacije proizvodnih sistema. Razvijen je model za izbor<br />lokacije proizvodnih sistema i verifikovan u studiji<br />slučaja na teritoriji AP Vojvodine i opštine Inđija.<br />Posebno značajan rezultat je fazi ekspertni sistem za<br />analizu kriterijuma odlučivanja, geografski informacioni<br />sistem za skrining i prostorni sistem za podršku u izboru<br />lokacije proizvodnih sistema.</p> / <p>This paper examines the possibilities of model development for<br />production systems site selection. Developed model for production<br />systems site selection is presented and tested in case study of AP<br />Vojvodina and Indjija municipality. A particularly important result is<br />developed fuzzy expert system for factor analysis, geographic<br />information system for screening and spatial decision support system.</p>

The increased demand for biofuels is expected to put additional strain on the available agricultural resources while at the same time causing environmental degradation. Hence, new energy policies need to be formulated and implemented in order to meet global energy needs while reducing the impact of biofuels farming and production. This research focuses on proving a decision support system which can aid the formulation of policies for the sustainable biofuel production. The system seeks to address policy formulation that requires reconciliation of the qualitative aspects of decision making (such as stakeholder’s viewpoints) with quantitative data, which often may be imprecise. To allow this, based on: Fuzzy logic and Multi Criteria Decision Making (MCDM) in the form of Analytical Hierarchy Process (AHP). Using these concepts, three software functionalities, “Options vs. Fuzzy Criteria Matrix”, “Analytical Hierarchy Process” and “Fuzzy AHP” were developed. These were added within the framework of pre-existing base software, Compendium (developed by the Open University, UK). A number of case study based models have been investigated using the software. These models made use of data from the Philippines and India in order to pinpoint suitable land and crop options for these countries. The models based on AHP and Fuzzy AHP were very successful in identifying suitable crop options for India by capturing both the stakeholder viewpoints and quantitative data. The software functionalities are very effective in scenario planning and selection of policies that would be beneficial in achieving a desired future scenario. The models further revealed that the newly developed software correctly identified many of the important issues in a consistent manner.

There is broad consensus that a transition to renewable energy and a low-carbon economy is crucial for future development and prosperity, yet there are differing perspectives on how such a transition should be achieved. The overarching goal of this dissertation, which is comprised of three interrelated studies, is to analyze and compare energy futures scenarios to achieve a renewable energy transition and low-carbon economy in the State of Vermont. In the first study, an analysis is presented of the role of energy pricing regimes and economic policy in the context of pursuing a renewable energy transition in the State of Vermont. Through the development and application of a system dynamics model, results address the limits to technological substitution due to path dependence on nonrenewable energy. The role of complementary economic policy is also highlighted to shift from a goal of quantitative growth to qualitative development in order to decouple economic welfare from energy consumption. In the second study, an analysis is presented of the impact of modeled energy transition scenarios to address energy development and land use trade-offs. Simulations with a spatio-temporal land cover change model find that Vermont could achieve a complete transition to renewable electricity using in-state resources through developing between 11,000 and 100,000 hectares of land for solar and wind, or up to four percent of state land area, including some environmentally sensitive land. This approach highlights the need for integration of energy policy and land use planning in order to mitigate potential energy-land use conflict. In the final study, trade-offs between energy, economic, environmental, and social dimensions of Vermont's renewable energy transition are explored through the use of a multi-criteria decision analysis. Energy transition alternatives were designed to reveal trade-offs at the intersection of economic growth and carbon price policy. While there were no optimal pathways to achieving Vermont's energy transition, some energy transition alternatives achieve a more socially desirable balance of benefits and consequences. Navigating the trade-offs inherent in the ongoing energy transition will require an adaptive approach to policymaking that incorporates iterative planning, experimentation, and learning.