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.

Diploma thesis describes the slection of supplier of testing services for Everest project at Raiffeisenbank a. s. with aid of methods of multi-criteria decision and operation research. Thesis compares achieved results with the decision made in real tender realised in year 2010. Solution deals with two approaches via operation research. For the first round of tender were used three methods of multi-criteria evaluation of alternatives, WSA, TOPSIS and ELECTRE III. According to the results of each method were chosen suppliers who advanced to the next round. Second round operates with prices of particular phases of project and discounts offered by suppliers. Solution uses modified model of Reverse Auction. Calculations were supported by program packages SANNA and LINGO. Diploma thesis proved usability of mathematical modelling of multi-criteria decision task as support for management decision in real life business.

The aim of this thesis is to find suitable mortgage, describe important criteria for comparison of mortgages and what types of mortgages the banks offer. The theoretical part of the thesis is divided into four parts. At first, I describe methods of determining the weights and methods of multi-criteria decision making. Then I focus on information about mortgage loan and about loan application process. In the third part I present variants and criteria and in the fourth I detect the best mortgage. I used WSA, TOPSIS, ELECTRE III and MAPPAC and add-on of Microsoft Excel - Sanna.

In recent years, the popularity of wind energy has grown. It is starting to play a large role in generating renewable, clean energy around the world. In New Zealand, there is increasing recognition and awareness of global warming and the pollution caused by burning fossil fuels, as well as the increased difficulty of obtaining oil from foreign sources, and the fluctuating price of non-renewable energy products. This makes wind energy a very attractive alternative to keep New Zealand clean and green. There are many issues involved in wind farm development. These issues can be grouped into two categories - economic issues and environmental issues. Wind farm developers often use site selection process to minimise the impact of these issues. This thesis aims to develop GIS based models that provide effective decision support tool for evaluating, at a regional scale, potential wind farm locations. This thesis firstly identifies common issues involved in wind farm development. Then, by reviewing previous research on wind farm site selection, methods and models used by academic and corporate sector to solve issues are listed. Criteria for an effective decision support tool are also discussed. In this case, an effective decision support tool needs to be flexible, easy to implement and easy to use. More specifically, an effective decision support tool needs to provide users the ability to identify areas that are suitable for wind farm development based on different criteria. Having established the structure and criteria for a wind farm analysis model, a GIS based tool was implemented using AML code using a Boolean logic model approach. This method uses binary maps for the final analysis. There are a total of 3645 output maps produced based on different combination of criteria. These maps can be used to conduct sensitivity analysis. This research concludes that an effective GIS analysis tool can be developed for provide effective decision support for evaluating wind farm sites.

Abstract The decrement of Nitrogen Oxide (NOx) emission can slow down the impaction to the natural environment as well as avoiding the global warming become worse continuously. The three coal fired boilers in CSC were designed as capable of multi-fuel burning but its primary fuel is coal. The original designed coal is PCI coal from Australia. The NOx emission generating while using PCI coal is around 320 ppm and can,t meet the local EPA emission requirement set for year 2001, which is 300ppm. To cope with more stringent environmental requirement, the first stage strategy is to evaluate the NOx removal technologies and selecting the optimum one of them to fight against NOx emission. By means of MCDM (Multi-Criteria Decision-Making) methodologies, SNCR is chosen due to its simplicity and acceptable NOx-out ability. The 45% NOx reduction rate can be achieved and the control NOx final output will be lower than 200ppm, which is the promised level to the local EPA. In compromising the NOx reduction requirement and the practical running cost factor, the 240ppm NOx level has been determined for operating the SNCR system. The second stage of NOx reduction evaluation is focused on the coal brands selection. It¡As believed that through the right choose of suitable coal can offer the contribution to both environment and economic. After surveying the different coal composition analysis and carrying out real trial burn, the ADARO coal from Indonesia was picked up from competition and deem as the most environment friendly coal in terms of owning the optimum coal compositions and the lowest pollutants generation including NOx, SO2, as well as ash. As a result, the ADARO coal application is highly successful in the aspects of less pollutant generation and saving cost expenditure caused by the related environment equipment systems operation and maintenance. Obviously, the positive and close relationship between environment and economic has been proved. Base on the real evidence, this paper proves that by choosing the suitable methodologies, the NOx reduction can be accomplished to some extent. The whole environment can be benefited by this action and meanwhile the air pollution tax plus operation and maintenance cost can be reduced further. It¡As hoped that by announcing this paper, the outstanding performance of remarkable NOX reduction outcome and its coat saving advantage can be applied to the whole industrial field. Let¡As work together for the purpose of decreasing the pollutants emission, lightening the harms to the natural environment and fulfilling the duties of protection the all eco system. Key words: Nitrogen Oxide, Selective Non-Catalytic Reduction(SNCR), environmental coal, Multi-criteria decision-making(MCDM)

Selection of the right machine tools is an important decision for most manufacturing companies. The quality and efficiency of manufacture are affected by the correct adoption of machine and equipment, and it will result in reducing the cost, increasing the production, and enhancing the flexibility for the diverse type of machining parts in this competitive globalized environment. On the contrary, an incorrect selection can retard the productivity of company. This research presents the procedures of AHP method for selecting 4-axis CNC machining centers including searching the literature of selecting machines, consulting experts with abundant experience of machine tool, translating the unstructured and biased commercial specification to the objective and hierarchical evaluation criteria for selecting machines. In order to experiment the applicability of the AHP model as well as to realize the relative importance of the criteria for the mold design and manufacturing company, a company was invited as the survey objective. This method proposed in this research is general in form and can be applied to the selections of other machine tools for other manufacturing industries. Also, it is hoped that the results of this experimental study will provide a reference for the domestic machinery manufacturing company to develop 4-axis CNC machining centers.

Sustainable transport is now a popular goal of transport planning. As with any aspiration, systems and mechanisms are required to assess and gauge success in achieving this policy goal. There is increasing reliance on sustainable transport indicators as appropriate tools for this purpose. The usefulness and credibility of any indicator-based assessment will undoubtedly depend on the specific indicators utilised. As such, indicators must be selected carefully to maximise their contribution to the sustainable transport decision making process. A review of current applications of sustainable transport indicators has revealed however, that they are typically selected in an ad hoc and arbitrary fashion. Development of a framework that facilitates transparent and systematic indicator selection would therefore represent a significant advance in transport research. In that regard, this thesis presents the Evaluative and Logical Approach to Sustainable Transport Indicator Compilation (ELASTIC), a methodological framework which provides a flexible, participatory and systematic mechanism for identifying and selecting key sustainable transport indicators. The output of ELASTIC is the Transport Sustainability Profile (TSP), a small un-aggregated suite of sustainable transport indicators which together can provide a snapshot of the sustainability of a transport system. Using various multi-criteria and statistical techniques, ELASTIC applies a robust process to evaluate and select indicators based on their analytical soundness and their relevance to key objectives of sustainable transport. A generic and transferable tool, ELASTIC is capable of application at different geographical scales as well as to non-transport sustainability assessment. For the purpose of this research, the framework is demonstrated through application to England, UK where the judgements of relevant Academics and Transport Planners are elicited and entered into the ELASTIC framework to systematically select a subset of 15 indicators from an initial set of 200. By disaggregating the sample of stakeholders into regional groupings, different context-specific suites of indicators for the regional groupings were also derived. The demonstration confirms ELASTIC to be an inclusive and practical approach to compiling a suite of sustainable transport indicators specific to context and which reflects the unique values of key stakeholders.

Uncertainty associated with the potential impact of climate change on supply availability, varied success with demand-side interventions such as water efficiency and changes in priority relating to hydrometric data collection and ownership, have resulted in challenges for water resources system management particularly in data-sparse regions. Consequently, the aim of this thesis is to assess the reliability, resilience and sustainability of water resources systems in both data-rich and data-sparse regions with an emphasis on robust decision-making in data-sparse regions. To achieve this aim, new resilience indicators that capture water resources system failure duration and extent of failure (i.e. failure magnitude) from a social and environmental perspective were developed. These performance indicators enabled a comprehensive assessment of a number of performance enhancing interventions, which resulted in the identification of a set of intervention strategies that showed potential to improve reliability, resilience and sustainability in the case studies examined. Finally, a multi-criteria decision analysis supported trade-off decision making when the reliability, resilience and sustainability indicators were considered in combination. Two case studies were considered in this research: Kingston and St. Andrew in Jamaica and Anyplace in the UK. The Kingston and St. Andrew case study represents the main data-sparse case study where many assumptions were introduced to fill data gaps. The intervention strategy that showed great potential to improve reliability, resilience and sustainability identified from Kingston and St. Andrew water resources assessment was the ‘Site A-east’ desalination scheme. To ameliorate uncertainty and lack of confidence associated with results, a methodology was developed that transformed a key proportion of the Anyplace water resources system from a data-rich environment to a data-sparse environment. The Anyplace water resources system was then assessed in a data-sparse environment and the performance trade-offs of the intervention strategies were analysed using four multi-criteria decision analysis (MCDA) weighting combinations. The MCDA facilitated a robust comparison of the interventions’ performances in the data-rich and data-sparse case studies. Comparisons showed consistency in the performances of the interventions across data-rich and data-sparse hydrological conditions and serve to demonstrate to decision makers a novel approach to addressing uncertainty when many assumptions have been introduced in the water resources management process due to data sparsity.