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

Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach

This dissertation explores the interdisciplinary applications of computational methods in quantitative economics. Particularly, this thesis focuses on problems in productive efficiency analysis and benchmarking that are hardly approachable or solvable using conventional methods. In productive efficiency analysis, null or zero values are often produced due to the wrong skewness or low kurtosis of the inefficiency distribution as against the distributional assumption on the inefficiency term. This thesis uses the deconvolution technique, which is traditionally used in image processing for noise removal, to develop a fully non-parametric method for efficiency estimation. Publications 1 and 2 are devoted to this topic, with focus being laid on the cross-sectional case and panel case, respectively. Through Monte-Carlo simulations and empirical applications to Finnish electricity distribution network data and Finnish banking data, the results show that the Richardson-Lucy blind deconvolution method is insensitive to the distributio-nal assumptions, robust to the data noise levels and heteroscedasticity on efficiency estimation. In benchmarking, which could be the next step of productive efficiency analysis, the 'best practice' target may not perform under the same operational environment with the DMU under study. This would render the benchmarks impractical to follow and adversely affects the managers to make the correct decisions on performance improvement of a DMU. This dissertation proposes a clustering-based benchmarking framework in Publication 3. The empirical study on Finnish electricity distribution network reveals that the proposed framework novels not only in its consideration on the differences of the operational environment among DMUs, but also its extreme flexibility. We conducted a comparison analysis on the different combinations of the clustering and efficiency estimation techniques using computational simulations and empirical applications to Finnish electricity distribution network data, based on which Publication 4 specifies an efficient combination for benchmarking in energy regulation.  This dissertation endeavors to solve problems in quantitative economics using interdisciplinary approaches. The methods developed benefit this field and the way how we approach the problems open a new perspective

Data envelopment analysis in service quality evaluation: an empirical study

Service quality is often conceptualized as the comparison between service expectations and the actual performance perceptions. It enhances customer satisfaction, decreases customer defection, and promotes customer loyalty. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. We introduce the perceived service quality index (PSQI) as a single measure for evaluating the multiple-item service quality construct based on the SERVQUAL model. A slack-based measure (SBM) of efficiency with constant inputs is used to calculate the PSQI. In addition, a non-linear programming model based on the SBM is proposed to delineate an improvement guideline and improve service quality. An empirical study is conducted to assess the applicability of the method proposed in this study. A large number of studies have used DEA as a benchmarking tool to measure service quality. These models do not propose a coherent performance evaluation construct and consequently fail to deliver improvement guidelines for improving service quality. The DEA models proposed in this study are designed to evaluate and improve service quality within a comprehensive framework and without any dependency on external data

A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and ‎expected values of the objective functions. In the initial proposed†â€DRF-DEA model, the inputs and outputs are assumed to be ‎characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this ‎assumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-‎objective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective ‎functions are the expected values of functions of normal random variables. In order to improve in computational time, we then ‎convert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives ‎programming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic ‎Algorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results ‎show that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of ‎runtime and solution quality. â€

Prioritization of patients' access to health care services

L'accès aux services de santé et les longs délais d'attente sont l’un des principaux problèmes dans la plupart des pays du monde, dont le Canada et les États-Unis. Les organismes de soins de santé ne peuvent pas augmenter leurs ressources limitées, ni traiter tous les patients simultanément. C'est pourquoi une attention particulière doit être portée à la priorisation d'accès des patients aux services, afin d’optimiser l’utilisation de ces ressources limitées et d’assurer la sécurité des patients. En fait, la priorisation des patients est une pratique essentielle, mais oubliée dans les systèmes de soins de santé à l'échelle internationale. Les principales problématiques que l’on retrouve dans la priorisation des patients sont: la prise en considération de plusieurs critères conflictuels, les données incomplètes et imprécises, les risques associés qui peuvent menacer la vie des patients durant leur mise sur les listes d'attente, les incertitudes présentes dans les décisions des cliniciens et patients, impliquant l'opinion des groupes de décideurs, et le comportement dynamique du système. La priorisation inappropriée des patients en attente de traitement a une incidence directe sur l’inefficacité des prestations de soins de santé, la qualité des soins, et surtout sur la sécurité des patients et leur satisfaction. Inspirés par ces faits, dans cette thèse, nous proposons de nouveaux cadres hybrides pour prioriser les patients en abordant un certain nombre de principales lacunes aux méthodes proposées et utilisées dans la littérature et dans la pratique. Plus précisément, nous considérons tout d'abord la prise de décision collective incluant les multiples critères de priorité, le degré d'importance de chacun de ces critères et de leurs interdépendances dans la procédure d'établissement des priorités pour la priorisation des patients. Puis, nous travaillons sur l'implication des risques associés et des incertitudes présentes dans la procédure de priorisation, dans le but d'améliorer la sécurité des patients. Enfin, nous présentons un cadre global en se concentrant sur tous les aspects mentionnés précédemment, ainsi que l'implication des patients dans la priorisation, et la considération des aspects dynamiques du système dans la priorisation. À travers l'application du cadre global proposé dans le service de chirurgie orthopédique à l'hôpital universitaire de Shohada, et dans un programme clinique de communication augmentative et alternative appelé PACEC à l'Institut de réadaptation en déficience physique de Québec (IRDPQ), nous montrons l'efficacité de nos approches en les comparant avec celles actuellement utilisées. Les résultats prouvent que ce cadre peut être adopté facilement et efficacement dans différents organismes de santé. Notamment, les cliniciens qui ont participé à l'étude ont conclu que le cadre produit une priorisation précise et fiable qui est plus efficace que la méthode de priorisation actuellement utilisée. En résumé, les résultats de cette thèse pourraient être bénéfiques pour les professionnels de la santé afin de les aider à: i) évaluer la priorité des patients plus facilement et précisément, ii) déterminer les politiques et les lignes directrices pour la priorisation et planification des patients, iii) gérer les listes d'attente plus adéquatement, vi) diminuer le temps nécessaire pour la priorisation des patients, v) accroître l'équité et la justice entre les patients, vi) diminuer les risques associés à l’attente sur les listes pour les patients, vii) envisager l'opinion de groupe de décideurs dans la procédure de priorisation pour éviter les biais possibles dans la prise de décision, viii) impliquer les patients et leurs familles dans la procédure de priorisation, ix) gérer les incertitudes présentes dans la procédure de prise de décision, et finalement x) améliorer la qualité des soins.Access to health care services and long waiting times are one of the main issues in most of the countries including Canada and the United States. Health care organizations cannot increase their limited resources nor treat all patients simultaneously. Then, patients’ access to these services should be prioritized in a way that best uses the scarce resources, and to ensure patients’ safety. In fact, patients’ prioritization is an essential but forgotten practice in health care systems internationally. Some challenging aspects in patients’ prioritization problem are: considering multiple conflicting criteria, incomplete and imprecise data, associated risks that threaten patients on waiting lists, uncertainties in clinicians’ decisions, involving a group of decision makers’ opinions, and health system’s dynamic behavior. Inappropriate prioritization of patients waiting for treatment, affects directly on inefficiencies in health care delivery, quality of care, and most importantly on patients’ safety and their satisfaction. Inspired by these facts, in this thesis, we propose novel hybrid frameworks to prioritize patients by addressing a number of main shortcomings of current prioritization methods in the literature and in practice. Specifically, we first consider group decision-making, multiple prioritization criteria, these criteria’s importance weights and their interdependencies in the patients’ prioritization procedure. Then, we work on involving associated risks that threaten patients on waiting lists and handling existing uncertainties in the prioritization procedure with the aim of improving patients’ safety. Finally, we introduce a comprehensive framework focusing on all previously mentioned aspects plus involving patients in the prioritization, and considering dynamic aspects of the system in the patients’ prioritization. Through the application of the proposed comprehensive framework in the orthopedic surgery ward at Shohada University Hospital, and in an augmentative and alternative communication (AAC) clinical program called PACEC at the Institute for Disability Rehabilitation in Physics of Québec (IRDPQ), we show the effectiveness of our approaches comparing the currently used ones. The implementation results prove that this framework could be adopted easily and effectively in different health care organizations. Notably, clinicians that participated in the study concluded that the framework produces a precise and reliable prioritization that is more effective than the currently in use prioritization methods. In brief, the results of this thesis could be beneficial for health care professionals to: i) evaluate patients’ priority more accurately and easily, ii) determine policies and guidelines for patients’ prioritization and scheduling, iii) manage waiting lists properly, vi) decrease the time required for patients’ prioritization, v) increase equity and justice among patients, vi) diminish risks that could threaten patients during waiting time, vii) consider all of the decision makers’ opinions in the prioritization procedure to prevent possible biases in the decision-making procedure, viii) involve patients and their families in the prioritization procedure, ix) handle available uncertainties in the decision-making procedure, and x) increase quality of care

Agile Supplier Selection In Sanitation Supply Chain Using Fuzzy VIKOR Method

Regarding to the diversified needs of domestic and global customers and various products of domestic and global competitors, importance of agility in supply chain management becomes more important. Suppliers have to provide materials and essential resources of manufacturers in a short time without any lead time.In this research, we identified many criteria for agility in sanitation supply chain. Then with utilization of Fuzzy Delphi, ideas of experts about criteria have been gathered in 8 final criteria. Next step is devoted to prioritization of five suppliers in sanitation industry based on the final criteria with fuzzy VIKORRegarding to the diversified needs of domestic and global customers and various products of domestic and global competitors, importance of agility in supply chain management becomes more important. Suppliers have to provide materials and essential resources of manufacturers in a short time without any lead time.In this research, we identified many criteria for agility in sanitation supply chain. Then with utilization of Fuzzy Delphi, ideas of experts about criteria have been gathered in 8 final criteria. Next step is devoted to prioritization of five suppliers in sanitation industry based on the final criteria with fuzzy VIKORRegarding to the diversified needs of domestic and global customers and various products of domestic and global competitors, importance of agility in supply chain management becomes more important. Suppliers have to provide materials and essential resources of manufacturers in a short time without any lead time.In this research, we identified many criteria for agility in sanitation supply chain. Then with utilization of Fuzzy Delphi, ideas of experts about criteria have been gathered in 8 final criteria. Next step is devoted to prioritization of five suppliers in sanitation industry based on the final criteria with fuzzy VIKO

Identifying and ranking key performance indicators in football clubs

Key performance indicators are actually measurable variables based on which we can measure the success rate of an organization in reaching defined key goals. In order to create key performance indicators, steps, and standards must be passed, each of which is of great importance. Based on how the key performance indicator (KPI) is defined and determined, it is possible to measure the performance of a person, department, process, campaign, or strategic goals of a brand. In fact, KPIs can be considered for different industries and for different levels of each business. Considering the importance of football clubs and their high social impact, the purpose of this research is to investigate these key performance indicators in order to grow and improve their comprehensive performance. In order to extract data, a literature review was used. Data refinement and prioritization were done using the fuzzy decision-making method, and the opinions of active experts in clubs and football players were used. The results show that indicators based on infrastructure development are among the most important indicators and should be given special attention

A hybrid Kano-fuzzy AHP method for measuring customer satisfaction: A case study of transportation system

An increase competition on today's economy has created motivation for many organizations to look for different alternatives on better serving the customers. There are always some budget limitations on any customer relationship method, which leads us to prioritize different alternatives. In this paper, we present an empirical method based on an integrated Kano and fuzzy analytical hierarchy procedure to rank suitable alternatives. The proposed model of this paper uses a questionnaire survey to gather customer's opinions and implements the method for a real-world case study of transportation planning. The questionnaire includes 37 questions distributed among 976 passengers for two trips in Iran. The results indicate that driver's physical and mental health, buss equipments with GPS functionality and familiarity of drivers with road and road's conditions play important role on choosing a transportation company

CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES

Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic programs