Evaluation of performance of European cities with the aim of increasing quality of life

Tese de Doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Multi-Dimensional Assessment of Transit System Efficiency and Incentive-based Subsidy Allocation

Over the past several decades, contending with traffic congestion and air pollution has emerged as one of the imperative issues across the world. Development of a transit-oriented urban transport system has been realized by an increasing number of countries and administrations as one of the most effective strategies for mitigating congestion and pollution problems. Despite the rapid development of public transportation system, doubts regarding the efficiency of the system and financing sustainability have arisen. Significant amount of public resources have been invested into public transport; however complaints about low service quality and unreliable transit system performance have increasingly arisen from all walks of life. Evaluating transit operational efficiency from various levels and designing incentive-based mechanisms to allocate limited subsidies/resources have become one of the most imperative challenges faced by responsible authorities to sustain the public transport system development and improve its performance and levels of service. After a comprehensive review of existing literature, this dissertation aims to develop a multi-dimensional framework composed of a series of robust multi-criteria evaluation models to assess the operational and financial performance of transit systems at various levels of application (i.e. region/city level, operator level, and route level). It further contributes to bridging the gap between transit efficiency evaluation and the subsequent subsidy allocation by developing a set of incentive-based resource allocation models taking various levels of operational and financial efficiencies into consideration. Case studies using real-world transit data will be performed to validate the performance and applicability of the proposed models

Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. Kekurangan keupayaan mendiskriminasi dan kelemahan pengagihan pemberat kekal sebagai isu utama dalam Analisis Penyampulan Data (DEA). Semenjak model DEA berbilang kriteria (MCDEA) pertama yang dibentuk pada akhir tahun 1990an, hanya pendekatan pengaturcaraangol; yakni, GPDEA-CCR dan GPDEA-BCC telah diperkenalkan bagi menyelesaikan masalah berkenaan dalam konteks berbilang kriteria

Evaluation of Performance Based Appraisal System in Higher Education Sector using DEA and AHP

There is a broad interest in the study of schemes for the measurement of the efficiency of the higher education sector, which generates demand but at the same time is controversial because of the complexity of the problem. Performance evaluation in Higher Education institutions is one of the essential activities in teaching and learning procedure. This problem is associated with the highly combinatorial characteristics that occur when facing the selection of the proper combination of the attributes, namely inputs and outputs. This study proposes an integrated approach to measure performance based appraisal system (PBAS) in higher educational institutions combining Analytic Hierarchy Process (AHP) with Data Envelopment Analysis (DEA).The AHP allows consideration of the varying importance of each criterion of teaching performance, while DEA enables to the comparison of teachers on teaching as perceived by students with a view to identifying the scope for improvement by each teacher

Transit Agencies Performance Assessment and Implications

Although most transit systems operate in small urban and rural areas in the United States, these systems have rarely received the same attention as their urban counterparts, both in terms of ensuring the efficiency and effectiveness of their operations and understanding the factors that affect their performance. This thesis\u27s main goals are to assess the performance of rural and small urban public transit agencies and help them evaluate adopting a ridehailing program, thereby improving their performance. We applied operations research and decision-making tools to two public transit projects in small urban and rural areas. The first project focuses on three models developed to evaluate the efficiency, effectiveness, and combined efficiency-effectiveness of rural transit agencies using data envelopment analysis. The models were estimated for the case study of transit systems in rural Appalachia and measured the agencies\u27 performance relative to their peers. Besides, the returns to scale were explored in the context of rural transit management. The second project focused on employing ridehailing programs in small urban and rural areas to improve agencies’ performance and reach. The most relevant criteria were identified to evaluate the performance of different ridehailing programs using multi-criteria decision analysis methodology. To perform a set of MCDA methods, we used the perceived rating of each ridehailing program according to the stakeholders\u27 opinions with respect to each criterion. The framework was estimated for the case study of Mountain Line Transit Authority in Morgantown, WV

Performance evaluation of petrochemical firms accepted in Tehran stock exchange using DEA (window analysis)

In the past two decades, organizational performance management has become one of the most attractive topics of study. Each organization is needed to evaluate its system to determine the appropriateness and quality of your work in dynamic environments. Data envelopment analysis provides a theoretical framework for performance analysis and performance measurement. The data envelopment analysis (DEA) is a linear programming technique, whose main purpose is to compare and evaluate a number of similar decision making units which have different amounts of used inputs and produced outputs. Dynamic method of data envelopment analysis (window analysis), is a method that enables the calculation of performance over time and can result in improved outcomes.We use the model described in this paper, the performance of listed companies in the petrochemical industry review. The evaluation results are indicated with different companies. The results showed that the six companies are 80% more efficient

Performance evaluation of petrochemical firms accepted in Tehran stock exchange using DEA (window analysis)

In the past two decades, organizational performance management has become one of the most attractive topics of study. Each organization is needed to evaluate its system to determine the appropriateness and quality of your work in dynamic environments. Data envelopment analysis provides a theoretical framework for performance analysis and performance measurement. The data envelopment analysis (DEA) is a linear programming technique, whose main purpose is to compare and evaluate a number of similar decision making units which have different amounts of used inputs and produced outputs. Dynamic method of data envelopment analysis (window analysis), is a method that enables the calculation of performance over time and can result in improved outcomes.We use the model described in this paper, the performance of listed companies in the petrochemical industry review. The evaluation results are indicated with different companies. The results showed that the six companies are 80% more efficient

Performance evaluation of petrochemical firms accepted in Tehran stock exchange using DEA (window analysis)

In the past two decades, organizational performance management has become one of the most attractive topics of study. Each organization is needed to evaluate its system to determine the appropriateness and quality of your work in dynamic environments. Data envelopment analysis provides a theoretical framework for performance analysis and performance measurement. The data envelopment analysis (DEA) is a linear programming technique, whose main purpose is to compare and evaluate a number of similar decision making units which have different amounts of used inputs and produced outputs. Dynamic method of data envelopment analysis (window analysis), is a method that enables the calculation of performance over time and can result in improved outcomes.We use the model described in this paper, the performance of listed companies in the petrochemical industry review. The evaluation results are indicated with different companies. The results showed that the six companies are 80% more efficient

THE POSITIVE POTENTIAL OF HIGH-PERFORMANCE ORGANIZATION

The paper presents an attempt to empirically answer the question about the source of success for small and medium enterprises (SME) made on the basis of two recently developed theoretical concepts namely High Performance Organization (HPO) and Positive Organizational Scholarship (POS). The aim of the study is to combine the model of HPO with the achievements of POS and to indicate positive characteristics of highly effective organizations – positive phenomena that drive an organization toward success. Data Envelopment Analysis (DEA) was used as the method for measuring relative efficiency in twelve SMEs. It appears from the data that there are different profiles of efficiency, thus there is no such thing as a universal configuration of positive characteristics always leading to high efficiency

Performance indicators and rankings in higher education

In times of austerity sectors which are publicly funded are inevitably under pressure to use their resources more efficiently. As a consequence, there is a tendency for the distribution of funds to be linked to past performance. During the 1980s, a period of funding cuts in the UK, the higher education funding bodies strove to develop performance indicators the purpose of which was to aid in the distribution of funds. In the early years, these indicators were simply ratios of one output to one input such as the proportion of students with ‘good’ degrees or the cost per student. Higher education institutions (HEIs) are multi-product firms, however, with complex production processes, and simple ratios cannot possibly capture the true picture of performance. In the ensuing thirty years, as techniques have developed to capture performance in a multi-output multi-input production framework, indicators have become more sophisticated. There is still a tendency, though, to stick with the more simple (easy to interpret) indicators as demonstrated by the interest in media rankings of universities. But the danger of these simple indicators is that they are open to manipulation and gaming. This paper examines and compares media rankings and performance indicators and proposes and evaluates a methodology which both captures performance and reduces the likelihood of gaming behaviour