<p>Multi-criteria decision making (MCDM) methods have evolved over the past several decades. In today’s world with rapidly growing industries, MCDM has proven to be significant in many application areas. In this study, a decision-making model is devised using reinforcement learning to carry out multi-criteria optimization problems. Learning automata algorithm is used to identify an optimal solution in the presence of single and multiple environments (criteria) using pareto optimality. The application of this model is also discussed, where the model provides an optimal solution to the food, energy, and water systems (FEWS) problem.</p>

The average global surface temperature increased by 0.85 °C since 1850 because of irrepressible increase of the concentration of greenhouse gases (GHG). Electricity generation is the primary source of GHG emissions in the United States. Hence, renewable energy sources, which produce a negligible amount of GHG emissions, have gained enormous attention, especially in the electricity generation sector over the past decade. Wind power is the second largest renewable energy source to generate electricity in the United States. Therefore, in this study, a two-stage Data Envelopment Analysis (DEA) is developed to quantitatively evaluate the relative efficiencies of the 39 state's wind power performances for the electricity generation. Both input- and output-oriented CCR (Charnes, Cooper, and Rhodes (1978)) and BCC (Banker, Charnes, and Cooper (1984)) models are applied to pre-determined four input and six output variables. The sensitivity analysis is conducted to test the robustness of the DEA models. Tobit regression models are conducted by using the DEA results for the second stage analysis. The DEA results indicate that more than half of the states operate wind power efficiently. Tobit regression indicates that early installed wind power was more expensive and less productive relative the currently installed wind power. Findings of this study shed some light on the current efficiency assessments of the states and the future of wind energy for both energy practitioners and policy makers.

No description available.

When developing a material handling system (MHS) it is crucial to master the various aspects for each developing step to increase the efficiency of the MHS. This Master’s thesis is aimed to identify conceptual solutions of a MHS and analyse the advantages and challenges for each of the developed concepts. Automation and implementation into a factory setting has taken into great consideration for this study. Two research questions (RQ) and three goals were formulated: RQ1: Which conceptual solutions of a material handling system for a high-pressure processing machine can be developed? RQ2: What are the challenges and advantages of each designed concept in terms of automation and implementation? Goal1: Develop three conceptual solutions of a material handling system. Goal2: Identify target specifications and determine their margin and ideal values for evaluation of the three conceptual solutions. Goal3: Analyse each developed material handling system and determine which concept is best out of the three in relation to the target specifications Automation has proven to be a key cause for achieving an effective MHS on the market and the two main reasons for this are, one: the elimination of ongoing labour cost and second; an increase of the overall safety factor for the system. The thesis, using the design research methodology (DRM) type two, consisting of an in-depth literature review and case study that contributed to evaluation and comparison of developed concepts. The literature review contributed to identifying the necessary steps of the structure of the MHS and required tools for the development phase. For the case study, the projects developing phase were taken from Ulrich, Eppinger and Yang (2020) ´s developing method. Identified tools and knowledge from the literature was used throughout the entire case study such as multi criteria decision making (MCDM), computer aided design (CAD), material flow patterns, automation and high-pressure processing (HPP). The findings of the case study showed that there are multiple concepts that can be developed. However, fully automated MHS are preferred when analysing the advantages and challenges for each fully developed MHS concept. The study resulted in three MHS concepts. Two fully automated ones and one semi-automated. The two fully automated MHS concepts showed more promising results than the semi-automated one. This was based on the MCDM-matrix which evaluated every MHS concept in relation to multiple criteria and failure mode effect analysis (FMEA) which investigated safety factors such as human safety and risk of MHS failure. Calculations such as capital cost and operational cost was also considered when analysing the differences between the MHS concepts. A fully automated MHS is not necessarily more costly in capital investment compared to semi-automated systems for the same HPP machine. Human labourers are, in semi-automated systems however, more precise in packaging operations, but also raises the operational costs substantially.

In today’s digital and mobile world it is more important than ever that companies offer a mobile solution for their costumers. Deciding which mobile solution to implement can be difficult. The purpose of this study is therefore to make this decision easier. Two mobile solutions, web apps and native apps, were compared in order to identify the most important factors that differentiate the two. By assigning the two mobile solutions one score each for every factor, we created a multi-criteria decision-making model. The model was then evaluated, improved and implemented as a decision-making tool. The second evaluation showed that the majority of the respondents agreed with the decision-making tool, suggesting that it could be applied as a recommendation for companies choosing between developing a native app and a web app. / I dagens digitala och mobila värld är det viktigare än någonsin för företag att erbjuda en mobil lösning för sina kunder. Att bestämma vilken mobil lösning som ska implementeras kan vara svårt och därför har denna studie för avsikt att underlätta detta beslut. Två mobila lösningar, webb-appar och nativ-appar, jämfördes för att kunna identifiera de viktigaste faktorerna som skiljer dem emellan. Genom att ge de två mobila lösningarna ett betyg var för varje faktor så kunde en multikriteriebeslutsmodell skapas, utvärderas och implementeras som ett beslutsverktyg. En andra utvärdering visade att majoriteten av de tillfrågade höll med beslutsverktyget vilket tyder på att det kan användas som rekommendation åt företag som väljer mellan att utveckla en nativ-app och en webb-app.

Although there are many studies focusing on the environmental impacts of alternative vehicle options, social and economic dimensions and trade-off relationships among all of these impacts were not investigated sufficiently. Moreover, most economic analyses are limited to life cycle cost analyses and do not consider macro-level economic impacts. Therefore, this thesis aims to advance the Life Cycle Sustainability Assessment literature and electric vehicle sustainability research by presenting a novel combined application of Multi Criteria Decision Making techniques with Life Cycle Sustainability Assessment for decision analysis. With this motivation in mind, this research will construct a compromise-programming model (multi-objective optimization method) in order to calculate the optimum vehicle distribution in the U.S. passenger car fleet while considering the trade-offs between environmental, economic, and social dimensions of the sustainability. The findings of this research provide important insights for policy makers when developing strategies to estimate optimum vehicle distribution strategies based on various environmental and socio-economic priorities. For instance, compromise programming results can present practical policy conclusions for different states which might have different priorities for environmental impact mitigation and socio-economic development. Therefore, the conceptual framework presented in this work can be applicable for different regions in U.S. and decision makers can generate balanced policy conclusions and recommendations based on their environmental, economic and social constraints. The compromise programming results provide vital guidance for policy makers when optimizing the use of alternative vehicle technologies based on different environmental and socio-economic priorities. This research also effort aims to increase awareness of the inherent benefits of Input-Output based a Life Cycle Sustainability Assessment and multi-criteria optimization.

Due to increasing concerns for global climate change, onshore and offshore wind energy technologies have stimulated a tremendous interest worldwide, and are considered as a viable solution to mitigate the environmental impacts related to electricity generation. Although wind energy technologies have been considered as one of the cleanest energy sources, they have a wide range of direct and indirect environmental impacts when the whole supply chain is considered. This study aims to quantify the direct and indirect environmental impacts of onshore and offshore wind power technologies by tracing all of the economy-wide supply chain requirements. To accomplish this goal, we developed a comprehensive hybrid life cycle assessment (LCA) model in which process-based LCA model is combined with the economic input-output (EIO) analysis. The analysis results show that on average, concrete and steel and their supply chains are responsible for 37% and 24% of carbon footprint, consequently. On average, offshore wind turbines produce 48% less greenhouse gas emissions per kWh produced electricity than onshore wind turbines. For the onshore wind turbines, concrete, aggregates, and crushed stone approximately consume 95% of total water in this construction phase. On the other hand, concrete, lead, copper, and aggregate are responsible for around 90% of total water for the offshore wind turbines. It is also found that the more capacity the wind turbine has, the less environmental impact the wind turbine generates per kWh electricity. Moreover, based on the economic and environmental impacts of studied wind turbines and also three more nonrenewable energy sources, this study develops a decision making framework to understand the best energy source mix for a building in the state of Florida. This framework accounts for the uncertainty in the input material by deploying a Monte Carlo iii simulation approach. The results of decision making framework show that natural gas is a better option among nonrenewable sources. On the other hand, V90-3.0 MW offshore wind turbine is the best source of energy among renewable energy sources for a building. The findings of this research are critical for policy makers to understand the direct and indirect environmental impacts of different onshore and offshore wind energy systems. Also this study furnishes the decision maker with a range of possible energy mixes based on different economic and environmental weights.

The efficiency of a best management practice (BMP) is defined simply as a measure of how well the practice or series of practices removes targeted pollutants. While this concept is relatively simple, mathematical attempts to quantify BMP efficiency are numerous and complex. Intuitively, the pollutant removal capability of a BMP should be fundamental to the BMP selection process. However, as evidenced by the absence of removal efficiency as an influential criterion in many BMP selection procedures, it is typically not at the forefront of the BMP selection and design process. Additionally, of particular interest to any developer or municipal agency is the financial impact of implementing a BMP. Not only does the implementation cost exist, but there are long-term maintenance costs associated with almost any BMP. Much like pollutant removal efficiency, implementation and maintenance costs seem as though they should be integral considerations in the BMP selection process. However, selection flow charts and matrices employed by many localities neglect these considerations. Among the categories of criteria to consider in selecting a BMP for a particular site or objective are site-specific characteristics; local, state, and federal ordinances; and implementation and long-term maintenance costs. A consideration such as long-term maintenance cost may manifest itself in a very subjective fashion during the selection process. For example, a BMPs cost may be of very limited interest to the reviewing locality, whereas cost may be the dominant selection criterion in the eyes of a developer. By contrast, the pollutant removal efficiency of a BMP may be necessarily prioritized in the selection process because of the required adherence to governing legislation. These are merely two possible criteria influencing selection. As more and more selection criteria are considered, the task of objectively and optimally selecting a BMP becomes increasingly complex. One mathematical approach for optimization in the face of multiple influential criteria is the Analytic Hierarchy Process. "The analytic hierarchy process (AHP) provides the objective mathematics to process the inescapably subjective and personal preferences of an individual or a group in making a decision" (Schmoldt, 2001, pg. 15). This paper details the development of two categories of comprehensive BMP selection matrices expressing long-term pollutant removal performance and annual maintenance and operations cost respectively. Additionally, the AHP is applied in multiple scenarios to demonstrate the optimized selection of a single BMP among multiple competing BMP alternatives. Pairwise rankings of competing BMP alternatives are founded on a detailed literature review of the most popular BMPs presently implemented throughout the United States. / Master of Science

Yes / Performance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies.

No / In recent decades, sustainable investing has caught on with investors, and it has now become the norm. In the age of start-ups, with scant information on the sustainability aspects of an asset, it becomes harder to pursue sustainable investing. To this end, this paper proposes a sustainable financial portfolio selection approach in an intuitionistic fuzzy framework. We present a comprehensive three-stage methodology in which the assets under consideration are ethically screened in Stage-I. Stage-II is concerned with cal- culating the sustainability scores, based on various social, environmental, and economic (SEE) criteria and an evaluation of the return and risk of the ethical assets. Intuitionistic fuzzy set theory is used to gauge the linguistic assessment of the assets on several SEE criteria from multiple decision-makers. A novel intuitionistic fuzzy multi-criteria group decision-making technique is applied to calculate the sustainability score of each asset. Finally, in Stage-III, an intuitionistic fuzzy multi-objective financial portfolio selection model is developed with maximization of the satisfaction degrees of the sustainabil- ity score, return, and risk of the portfolio, subject to several constraints. The ε-constraint method is used to solve this model, which yields various efficient, sustainable financial portfolios. Subsequently, investors can choose the portfolio best suited to their preferences from this pool of efficient, sustainable financial portfolios. A detailed empirical illustration and a comparison with existing works are given to substantiate and validate the proposed approach. / Institution of Eminence, University of Delhi, Delhi-110007 under Faculty Research Program