Indiana University-Purdue University Indianapolis (IUPUI) / 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.

The idea of multi-criteria decision making has been around for quite a while. All judgement tasks are potential points of bias introduction. Each judgement task was assessed to identify common biases introduced through an extensive literature review for each task and bias. In several other studies, the distinction is made between cognitive and motivational biases. Cognitive biases are widely studied and well known with mitigations that have been validated. Motivational biases are judgements influenced by the decision maker’s desire for a specific outcome, also referred to as intentional bias, that are hard to correct and received very little testing and exploration. This study tested the techniques that are identified for reducing motivational bias and tested an instrument to identify characteristics within a decision maker that would increase the likelihood that they would be motivationally biased. The results of this study provide a methodology for assessing the susceptibility to motivational biases of the decision makers and provides a framework for reducing the motivational bias within the multi-criteria decision making (MCDM) process using the general steps applicable to all multi-criteria decision analyses. Given that the general steps are used, this methodology is generalizable to any MCDM problem or domain and was found to be reliable and consistent with previous instruments and tools. A summary of the future research to further the explore the methodology and additional techniques for reducing motivational bias is proposed.

Signal Replacement decisions are often made based on the experience of the Traffic Engineers. These decisions are made while considering the deployment time of the system, the new technology available, and the performance of the system in the given location. However, there is no set of proper guidelines or methods which can quantify the system replacement decision in large scale projects. This thesis presents a methodology that can be applied to determine optimal migration plans for traffic signal controllers. A Multi-Criteria Decision Making technique has been adopted to evaluate various traffic signal controllers. Various controller manuals were studied and information was obtained from the vendors of the controllers. In addition to that, Geographic Information System (GIS) has been used as a tool to evaluate and identify the areas where the traffic signal controllers have to be replaced first. The study considers the budget constraints and the benefits that can be obtained by the replacement of the controllers. This thesis presents the Methodology adopted for the Migration Plan and a case study implementation on the Northern Virginia Region. Finally it presents the conclusions drawn from the research with insights into the scope for further research. / Master of Science

The aim of this diploma thesis is to devise general evaluation procedure of current accounts for associations and then to compare the theoretical results with the results of the questionnaire survey. The aim of the questionnaire survey is to find out what accounts the associations use and why. The theoretical part of this thesis is focused on the scientific discipline called multi-criteria decision making, its basic terminology, the methods of setting the criterions' weights and selected multi-criteria decision making methods. Finally, the associations are described according to valid legislation. In the practical part, selected accounts for specific clients are evaluated by using three multi-criteria decision making methods and the questionnaire survey is evaluated too. The conclusion is dedicated a comparison of the theoretical evaluation results with the questionnaire survey results and the author's own view of this issue. Familiarization of the author with the associations' activities during a personal meeting with them is the main benefit of this thesis for the author. The finding that the accounts of some of them are not as advantageous as they were before is the benefit for the associations. Some of them decided to visit their bank and change the account to the account recommended by the author.

The increasing global deficit of healthcare resources makes efficiency improvements in the healthcare industry a complete necessity to assure safe and available healthcare for everyone. Digitalization is expected to play a fundamental role in this transition and digital primary healthcare providers have in recent years developed into a substantial part of the Swedishprimary care sector. Several of those have built solutions for automated triage, where the role of a triage officer in traditional primary care is replaced by an automated process, in which an triage algorithm directly refers the patient to the appropriate level of care. Despite the rise of digital healthcare providers and automated primary care triage systems in particular, research on the implications of automating the triage process in primary healthcare is scarce. This study aims to assess the potential of using MCDM models for automated triage in digital primary care, by conducting a single case study at one of the leading digital healthcare providers. The study is separated into two phases. In phase one, interviews are conducted to qualitatively determine what set of factors to include in an automated MCDM triage model.In phase two, the resulting model is simulated to evaluate the performance compared to the traditional triage model in which all patient journeys start with an initial nurse meeting. The study shows that an automated MCDM triage model can improve cost efficiency in terms of clinician salary costs and productivity in terms of fewer consultations per patient, compared to the traditional triage model. However, the traditional triage model is shown to be more efficient in terms of only utilizing doctor resources for patients in absolute need of doctor care. / Det ökande underskottet av sjukvårdsresurser gör effektivitetsförbättringar i sjukvårdsbranschen nödvändigt för att säkerställa säker och tillgänglig sjukvård för alla. Digitalisering förväntas fylla en fundamental roll i denna transformation och digitala vårdgivare i primärvården har under de senaste åren växt till en betydande del av den svenska primärvårdssektorn. Flertalet av dessa har byggt lösningar för automatiserat triage, där triagefunktionärens roll ersätts av en automatiserad process där en triagealgoritm direkt hänvisar patienten till den lämpliga vårdnivån. Trots tillväxten av digitala vårdgivare i primärvården och deras automatiserade triagesystem i primärvården är forskning kring effekterna av att automatisera triageprocessen i primärvården begränsad. Denna studie strävar efter att utvärdera potentialen i att använda MCDM-modeller för automatiserat triage i den digitala primärvården genom en casestudie på en av de ledande digitala vårdgivarna i primärvården. Studien är uppdelad i två delar. I del ett genomförs intervjuer för att kvalitativt fastställa vilka faktorer som bör inkluderas i en automatiserad MCDM-modell för triage. I del två simuleras den resulterande MCDM-modellen för att utvärdera dess resultat jämfört med den traditionella triagemodellen i vilken alla patienter har ett inledande möte med en sjuksköterska. Studien visar att en automatiserad MCDM-modell för triage kan förbättra kostnadseffektiviteten i termer av lönekostnader och produktivitet i termer av färre konsultationer per patient, jämfört med den traditionella triagemodellen. Däremot visar den traditionella triagemodellen högre effektivitet i termer av att enbart utnyttja läkarresurser för patienter i absolut behov av läkarvård.

Kuwait’s per capita electrical energy consumption is among the largest in the world, reaching 13,663 kWh per person in 2011. The electricity demand in Kuwait is increasing, which requires additional investments in power generation. A particular challenge in Kuwait is the peak demand in summer, when extreme heat increases air conditioning loads. Peak demand reached 11,220 MW in 2011, with a fast growth rate averaging 5.6% over the last decade and a maximum production capacity of around 14,720 MW. It is not possible to cope with this demand simply by increasing generation capacity. Therefore, the only alternative available to Kuwait is to control electricity demand via demand-side management. The main objective of this research is to assess and select the optimal demand- side management (DSM) technologies for buildings in the governmental sector (office, religious and school buildings) and to investigate the key factors in the evaluation process. To achieve the research objective, a model was proposed using multi-criteria decision-making techniques to enable the forecasting and comparison of DSM alternatives that are suitable for buildings. The developed model includes environmental, economic, technical and social considerations. The research methodology is based on three main phases. Phase 1: Data collection through a mail questionnaire that was sent to 42 experts for the identification of criteria and demand-side management alternatives. Phase 2: Screening and narrowing of the data collected in Phase 1 using a set of questionnaires, including the identification of potential DSM alternatives and criteria suitable for further analysis. This phase was performed through the Delphi process, taking into consideration the opinions of 28 experts. Phase 3: Use of Multi-criteria Decision Making (MCDM) techniques, namely the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP), to evaluate and rank the identified DSM alternatives and criteria. This was done using pairwise comparisons of 17 experts to evaluate the criteria and alternatives for the buildings (office, religious and school). The research showed that the experts identified six alternative technologies and five criteria for the selection and evaluation of governmental buildings (office, religious and school). After performing the three phases of this research project, a set of criteria and alternatives were ranked based on the information gathered from every group of experts. It was found that reduction in consumption, capital cost and ease of implementation were the three most recommended criteria for the selection of DSM technologies in Kuwait government buildings while high efficiency lighting and programmable thermostats were identified as the most recommended DSM technologies for these buildings. An important aspect of this research is that unlike engineering approaches which sometimes depend on expensive test equipment or, often for building design, expensive computer modelling exercises, the proposed framework can be easily adopted by anyone without any significant financial cost. The FAHP approach was also tested and its results compared with those of AHP. There was a slight difference between using AHP and FAHP in terms of ranking the criteria and alternatives but that the difference barely affects the ranking. It was found that the AHP provides a convenient MCDM approach for solving the complex problem of selecting the optimal DSM options for buildings. The contributions of this thesis are the development of a novel framework for systematic selection and ranking of DSM technologies in different types of buildings using the Delphi method and AHP; the identification of the most important DSM technologies and criteria for their selection for three types of governmental buildings in Kuwait; and establishing that results from using AHP and FAHP for the selection of appropriate DSM measures in these buildings are almost identical, so use of AHP is likely to be sufficient in most building studies of this type.

Energy access remains a significant challenge in nations lacking access to resources and strong infrastructure systems, creating barriers to economic development and to increased standards of living. Small scale biomass gasification energy (BGE) systems have been developed to meet energy needs in rural areas, creating synergies between agricultural and agro-forestry systems through utilization of biomass feedstock for energy generation. The sustainability of such systems requires sophisticated planning and coordination of the biomass supply chain. The goal of this thesis is to investigate and improve structural and process related characteristics of sustainability assessments for small scale bio-energy systems, specifically focusing on establishment and management of biomass supply chains through the development and dissemination of a generic sustainability assessment framework for biomass supply chains of small-scale BGE systems in rural East Africa. Building on a preliminary sustainability assessment framework (Christensen, 2013; Joerg, 2013) this research develops an assessment tool designed to capture sustainability requirements of the biomass supply chain in the ecological, social, and economic spheres through testing on three case studies in rural Uganda. Application and analysis of a preliminary framework on pilot projects in a rural east African context using Multi Criteria Decision Analysis (MCDA) methodologies contributes to development of strategies for energy system analysis and building stakeholder capacity to incorporate social, economic, and environmental considerations. The assessment process is outlined, including scoring, data collection, contextual considerations. Model application is discussed, including the impact of weighting on decision outcomes, uncertainty management, sensitivity analysis, and identification of tradeoffs among criteria. Finally, discussion of tool usefulness verses usability contributes to bridging academic research with practitioner priorities.

Data Envelopment Analysis is a multi-criteria decision making tool employing set of minimizing criteria (inputs) and set of maximizing criteria (outputs) for evaluating decision making unit efficiency. This method is accompanied by problems in field of input and output weight assignment, whereas benevolent basic model formulation enables decision making unit evaluation to be based on far unevenly distributed weight vector. Furthermore, data envelopment analysis basic model produces dichotomized dividing in form of efficient and inefficient decision making unit sets. Extensive set of efficient units occurs frequently and this causes difficulties in choosing one or less number efficient units. These phenomena appear often simultaneously. An implementation of multi-criteria decision making models into data envelopment analysis is exercised in order to reduce these undesired effects in applications.

Motivated by IT evaluation problems identified in a large public sector organization, we propose how evaluation requirements can be supported by a framework combining different models and methods from IS evaluation theory. The article extends the content, context, process (CCP) perspectives of organizational change with operations research techniques and demonstrates the approach in practice for an Enterprise Resource Planning evaluation.

The aim of this work is to create an application in VBA to compare countries using multi-criteria decision making methods. The main methods, mentioned in the theoretical part are implemented in the application. In theoretical part there are also compared different multi-criteria decision making methods. This work also describes macroeconomic criteria, weigths and the selection of countries. The aim of the final part is the analysis of country comparison results.