Mass customization (MC) was developed to capitalize on the combined benefits of economies of scale and economies of scope. Balancing the tradeoffs involved in an MC system warrants the determination of the degree or the extent of customization. Most of the literature views the degree of customization as how early or how far the customer is integrated in the production cycle, which is defined as the order decoupling point. In this study we are addressing the degree of customization from a product structural perspective. There are two objectives in this research. The first is to develop a unit of measurement for the degree of customization of a product in an MC system. The second is to construct an optimization model to determine the level of customization that would best satisfy the organizational goals. The term "Magnitude of Customization" (MOC) has been introduced as a measuring unit for the degree of customization on a customization scale (CS). The MOC is based on the number of module options or the extent of customizable features per component in a product. To satisfy the second objective, an analytical model based on preemptive goal programming was developed. The model optimizes the solution as to how far an organization should customize a product to best satisfy its strategic goals. The model considers goals such as increasing the market share, and attaining a higher level of customer satisfaction, while keeping the risk or budget below a certain amount. A step-by-step algorithm is developed for the model application. A case study of an aluminum windows and doors company is used to verify and validate the model. A double panel sliding window is selected as the subject of our study. Information related to company goals and objectives vis-a-vis customization is gathered, through interviews and questionnaires, from the upper management including Operations, Marketing, and Finance Departments. The Window design and technical information are collected from the Manufacturing Department. The model and its solution provided specific recommendations on what to customize and to what degree to best satisfy primary strategic goals for the organization. Results from the model application shows that the company is able to meet the five goals that they had identified with two goals having a deviation of 4.7% and 6.6% from the targets. To achieve the stated goals, the model recommends an overall degree of customization of approximately 32.23% and delineates that to the component and feature levels. For validation, the model results are compared to the actual status of the company and the manufacturer's recommendation without prior information about the model outcome. The average difference, for attaining the same goals, is found to be 6.05%, at a standard deviation of 6.02% and variance of 36.29%, which is considered adequately close. The proposed model presents a framework that combines various research efforts into a flexible but encompassing method that can provide decision-makers with essential production planning guidelines in an MC setup. Finally, suggestions are provided as to how the model can be expanded and refined to include goal formulations that accommodate potential MC systems and technology advances. To the best of our knowledge, this research is a pioneer in quantifying customization in an MC environment and relating it to the organizational goals through modeling and optimization.

A systematic customer value assessment process (CVAP) was developed to give system engineering teams the capability to qualitatively and quantitatively assess customer values. It also provides processes and techniques used to create and identify alternatives, evaluate alternatives in terms of effectiveness, cost, and risk. The ultimate goal is to provide customers (or decision makers) with objective and traceable procurement recommendations. The creation of CVAP was driven by an industry need to provide ballistic missile defense (BMD) customers with a value proposition of contractors’ BMD systems. The information that outputs from CVAP can be used to guide BMD contractors in formulating a value proposition, which is used to steer customers to procure their BMD system(s) instead of competing system(s). The outputs from CVAP also illuminate areas where systems can be improved to stay relevant with customer values by identifying capability gaps. CVAP incorporates proven approaches and techniques appropriate for military applications. However, CVAP is adaptable and may be applied to business, engineering, and even personal every-day decision problems and opportunities. CVAP is based on the systems decision process (SDP) developed by Gregory S. Parnell and other systems engineering faculty at the Unites States Military Academy (USMA). SDP combines Value-Focused Thinking (VFT) decision analysis philosophy with Multi-Objective Decision Analysis (MODA) quantitative analysis of alternatives. CVAP improves SDP’s qualitative value model by implementing Quality Function Deployment (QFD), solution design implements creative problem solving techniques, and the qualitative value model by adding cost analysis and risk assessment processes practiced by the U.S DoD and industry. CVAP and SDP fundamentally differ from other decision making approaches, like the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), by distinctly separating the value/utility function assessment process with the ranking of alternatives. This explicit value assessment allows for straightforward traceability of the specific factors that influence decisions, which illuminates the tradeoffs involved in making decisions with multiple objectives. CVAP is intended to be a decision support tool with the ultimate purpose of helping decision makers attain the best solution and understanding the differences between the alternatives. CVAP does not include any processes for implementation of the alternative that the customer selects. CVAP is applied to ballistic missile defense (BMD) to give contractors ideas on how to use it. An introduction of BMD, unique BMD challenges, and how CVAP can improve the BMD decision making process is presented. Each phase of CVAP is applied to the BMD decision environment. CVAP is applied to a fictitious BMD example.

The aim of this Thesis is to consider a methodological framework suitable to support a primary and primitive investigation and evaluation of the technical applicability and energy feasibility of a specific Energy Island model in Greece. For such purpose, the general concept and the potential applications of Energy Island are presented, and the present situation of the Greek energy sector and RES technologies in Greece are outlined. In order to attempt to evaluate the technical performance of a specific Energy Island model in Greece, a theoretical and hypothetical Multi-Criteria Decision Analysis (MCDA) process is developed and conducted. The methodological framework developed and applied for the present case study pertains to a theoretical decision-making process for the selection of the optimum Energy Island scenario in Greece amongst four (4) alternatives. As derived from the extracted results of the applied MCDA model, the hypothetical scenario with the highest annual energy production and the least environmental and technological issues ranks optimal for all the implicated stakeholders and is considered the most preferred alternative. This Thesis concludes that the perspective of the various applications of the concept of the Energy Island model has the potential to contribute to more efficient utilization of the available RES technologies in Greece, in order to accelerate the decarbonization of the Greek energy system as well as to assure the security of the system, by replacing the existing conventional fossil fuel generation plants with clean offshore renewable energy.

Despite currently having only one operational offshore wind farm, Italy holds a significant potential for the future development of this technology. The Global Wind Council placed Italy second in its ranking of the world’s 30 most attractive markets regarding the potential of future offshore development, attracting the attention of numerous developers. The characteristics of the Mediterranean Sea, with its high water depth, make it possible to develop mainly floating technology. Furthermore, the absence of severe weather events, present in the Atlantic and the North Sea instead, enhanced security and contributed to the reduction of investment risks. Currently, there are many new wind farms awaiting approval, as evidenced by Terna, the Italian Transmission System Operator (TSO), which announced that by the end of October 2022 the connection requests for offshore wind projects had reached 95 GW. This thesis aims to develop an approach able to compare several projects from multiple perspectives. Specifically, the thesis applies the Multi-Criteria Decision Analysis to evaluate and compare four floating offshore wind farms which are now in the permitting phase. The projects are located off the coast of Sardinia, one of the most promising locations in the Mediterranean Sea for offshore wind installation. The evaluation of the future wind farms is conducted using eleven criteria, each assigned a different weight based on the preferences of six stakeholder groups. The tool used to rank the criteria is the PROMETHEE tool. The results demonstrate that each stakeholder group ranks the projects differently based on their respective preferences. Finally, an overall ranking of the wind farms is derived, identifying the most and least favorable projects.

<p dir="ltr">Conservation conflicts, commonly defined as “situations that occur when two or more parties with strongly held opinions clash over conservation objectives, and when one party is perceived to assert its interests at the expense of another” (Redpath et al., 2013) are expected within the realm of public land management. Conservation conflicts have been an increasing issue worldwide as the consumption of natural resources can directly oppose conservation efforts. Quantitative and qualitative approaches have been adopted in similar studies to mitigate or resolve conservation conflicts. This thesis focuses on a 2017 conflict over logging in Yellowwood State Forest in Indiana. The Social Multi-Criteria Evaluation (SMCE) framework was applied in this thesis to examine economic, ecological, and recreational criteria from multiple stakeholders' perspectives and understand how a retrospective assessment can contribute to improved conflict resolution. The study follows four steps: conducting an institutional analysis, defining criteria and potential alternative scenarios, generating an impact matrix through surveys and interviews, and aggregating results for cross-scenario comparison. The design of these steps attempts to engage stakeholders in the decision-making process and increase transparency. The ranking results reveal a clear preference for the “Shelterwood Cuts” alternative, indicating that different actions may have been a better solution. Although the methodology alone cannot make decisions, it can aid the decision-maker in creating a solution to a conservation conflict by providing guidance and bringing attention to the aspects of a conflict that require change.</p>

No description available.

<p dir="ltr">Highway safety continues to pose a serious challenge to the social sustainability of transportation systems, and initiatives are being pursued at all levels of government to reduce the high fatality count of 42,000. At the same time, it is sought to ensure higher travel efficiency in order to increase economic productivity. The emergence of automated transportation provides great promise to mitigate these ills of the transportation sector that have persisted for so many decades. With regards to safety, such promise is rooted in the capability of autonomous vehicles to self-drive some or all of the time, thus reducing the impact of inherently errant human driving to which 95% of all crashes have been attributed. With regards to mobility, such promise is guided by the capability of the autonomous vehicle to carry out path planning, navigation, and vehicle controls in ways that are far more efficient than the human brain, thereby facilitating mobility and reducing congestion-related issues such as delay, emissions, driver frustration, and so on.</p><p dir="ltr">Unfortunately, the two key outcomes (safety and mobility) are reciprocal in the sense that navigation solutions that enhance safety generally tend to reduce mobility, and vice versa. As such, there is a need to assign values explicit to these performance criteria in order to develop balanced solutions for AV decisions. Most existing machine-learning-based path planning algorithms derive these weights using a learning approach. Unfortunately, the stability of these weights across time, individuals, and trip types, is not guaranteed. It is necessary to develop weights and processes that are trip situation-specific. Secondly, user trust in automation remains a key issue, given the relatively recent emergence of this technology and a few highly-publicized crashes, which has led to reservations among potential users.</p><p dir="ltr">To address these research questions, this thesis identifies various situational contexts of the problem, identifies the alternatives (the viable trajectories by fitting curves between the vehicle maneuver’s initial and final positions), develops the decision criteria (safety, mobility, comfort), carries out weighting of the criteria to reflect their relative significance, and scales the criteria to develop dimensionless equivalents of their raw values. Finally, a process for amalgamating the overall impacts of each driving decision alternative is developed based on the weighted and scaled criteria, to identify the best decision (optimal trajectory path). This multi-criteria decision making (MCDM) problem involves the collection of data through questionnaire surveys.</p><p dir="ltr">The weights obtained early in the MCDM process could be integrated into any one of two types of planning algorithms. First, they could be incorporated into interpolating curve-based planning algorithms, to identify the optimal trajectory based on human preferences. Additionally, they can be integrated into optimization-based planning algorithms to allocate weights to the various functions used.</p><p dir="ltr">Overall, this research aims to align the behavior of autonomous vehicles closely with human-driven vehicles, serving two primary purposes: first, facilitating their seamless coexistence on mixed-traffic roads and second, enhancing public acceptance of autonomous vehicles.</p>

Infrastructures are the most fundamental facilities and systems serving the society. Due to the existence of infrastructures in economic, social, and environmental contexts, all lifecycle phases of such fundamental facilities should maximize utility for the designers, occupants, and the society. With respect to the nature of the decision problem, two main types of uncertainties may exist: 1) the aleatory uncertainty associated with the nature of the built environment (i.e., the economic, social, and environmental impacts of infrastructures must be described as probabilistic); and 2) the epistemic uncertainty associated with the lack of knowledge of decision maker utilities. Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment, they do not provide a systematic framework for including aleatory and epistemic uncertainties, and decision maker utilities in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology (i.e., utility function/indifference curve) for assessing decision maker utility functions with respect to a range of alternative design configurations. Module two utilizes the First Order Reliability Method (FORM) in a systems reliability approach for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker defined utility functions (indifference curves), and provides a meaningful feedback loop for improving the reliability of the alternative design configurations. Module three provides a systematic framework to incorporate both aleatory and epistemic uncertainties in the decision analysis methodology (i.e., uncertain utility functions and group decision making). The multi-criteria, probabilistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations and four decision maker utility functions under aleatory and epistemic uncertainties are utilized. The results of the decision analysis methodology revealed that the high-performing design configurations with an initial cost of up to $3.2M (in a cost range between $1.7M and $3.2M), a building damage and business interruption cost as low as $303K (in a cost range between $303K and $6.2M), a casualty cost as low as $43K (in a cost range between $43K and $1.2M), and a CO2 emission as low as $146K (in a cost range between $133K to $150K) can be identified by having a higher probability (i.e., up to 80%) of meeting the decision makers' preferences. The modular, holistic, decision analysis framework allows decision makers to make more informed performance-based design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process. / PHD / Infrastructures, including buildings, roads, and bridges, are the most fundamental facilities and systems serving the society. Because infrastructures exist in economic, social, and environmental contexts, the design, construction, operations, and maintenance phases of such fundamental facilities should maximize value and usability for the designers, occupants, and the society. Identifying infrastructure configurations that maximize value and usability is challenged by two sources of uncertainty: 1) the nature of the built environment is variable (i.e., whether or not a natural hazard will occur during the infrastructure lifetime, or how costs might change over time); and 2) there is lack of knowledge of decision maker preferences and values (e.g., design cost versus social impact tradeoffs). Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment (e.g., natural hazard events), they do not provide a systematic framework for including the uncertainties associated with the decision analysis process (e.g., lack of knowledge about decision maker preferences), and decision maker requirements in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology for assessing decision maker values with respect to a range of alternative design configurations. Module two utilizes an algorithm for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker requirements, and provides a meaningful feedback loop for understanding the decision analysis results (i.e., improving the value and usability of the alternative design configurations). Module three provides a systematic framework to incorporate both the random uncertainty associated with the built environment and the knowledge uncertainty associated with lack of knowledge of decision maker preferences, and tests the reliability of the decision analysis results under random and knowledge uncertainties (i.e., uncertain decision maker preferences and group decision making). The holistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations, four decision makers, and random and knowledge sources of uncertainty are considered in the decision analysis methodology. Results indicate that the modular, holistic, decision analysis framework allows decision makers to make more informed design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process.

The urgent need to combat climate change is increasingly being recognized. The Paris Agreement, which aims to limit global warming, requires carbon neutrality to be achieved by the mid-21st century. Further, the energy crisis in Europe that started in 2021 highlights the importance of energy security. Universities play a crucial role in promoting the transition to neutrality. This study aims to increase universities' electricity independence to further facilitate their transition to carbon neutrality. To this end, a multi-criteria decision analysis (MCDA) method was adopted to select scenarios for increasing a campus building complex's electricity independence, considering various stakeholders' interests together with the scenarios' performances on technical, environmental, economic, and social criteria. The findings show that photovoltaic technology, despite its perceived environmental benefits, performs poorly in reducing carbon emissions when considering lifecycle emissions, particularly in countries with low-carbon electricity like Sweden. Conversely, energy conservation through behavioral changes emerges as the optimal scenario for Campus Gotland due to its economic and environmental advantages. These results challenge the common reliance on energy production technology for carbon neutrality, highlighting the greater effectiveness of demand-side measures. This work suggests that universities need a more human-centric approach to transitioning to carbon neutrality. In a broader context, this study provides universities with insights to make informed decisions to achieve carbon neutrality, emphasizing the need to consider all stakeholders. By offering a comprehensive assessment and analysis of various scenarios, this work enhances the understanding of best practices for universities aiming to lead in the global effort against climate change.

This thesis explores the strategic considerations for the international expansion of family-owned professional development companies in Central European markets, specifically Germany, Poland, Austria, and Slovakia. The study integrates multiple theoretical perspectives such as the Resource-Based View of the firm, Eclectic Paradigm, Institutional Theory, and Hofstede’s Cultural Dimensions Theory with empirical data on the markets in question to identify the most relevant factors that aid in the location choice decision-making. Multi-criteria decision Analysis was used in this research to assess various criteria such as market size, economic stability, cultural compatibility, competitors and revenue potential. Furthermore, regression analysis was carried out to test the optimal number of cultural dimensions to be included in the analysis. The results showed that Germany was the most attractive location followed by Austria, Poland, and Slovakia. Market size, economic stability, and cultural compatibility was found to be in alignment with the resource advantages strategies highlighted by the Resource Based View. The results provide a comprehensive framework for choosing the optimal criteria for country selection decisions for expansion which are robust and based on empirical market data and therefore should aid family-owned professional development companies to successfully expand into highly competitive international markets.