Thesis (Ed.M.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2031-01-01

Ingeniera Civil Industrial / El presente estudio tiene por objetivo desarrollar e implementar una metodología de priorización de infraestructura hidráulica de riego, de forma que los proyectos de embalses sean estudiados y evaluados en su contexto. Para la elaboración de la herramienta de priorización, se utilizó el método MCDM, el cual corresponde a un enfoque que permite comparar alternativas en base a múltiples atributos y características. Entre los métodos estudiados, se optó por la metodología AHP para determinar los ponderadores, ya que permite incluir, de forma simultánea, características cuantitativas y cualitativas dentro de un mismo modelo. Se desarrolló un modelo con 5 niveles de criterios y subcriterios. En el primer nivel se especifica el objetivo del estudio. En el segundo nivel jerárquico, se consideran los criterios macro, los cuales corresponden a factores de desarrollo y estratégicos. Los criterios de desarrollo están asociados directamente con las características de los proyectos en sí y los criterios estratégicos corresponden a aspectos políticos y administrativos. En el tercer nivel se identifican los factores económico, social y ambiental. El subcriterio económico busca medir las rentabilidades y riesgos del modelo, el subcriterio social incluye los impactos que este tipo de obras tienen sobre las comunidades y el subcriterio ambiental buscar medir el impacto que tienen los embalses en el territorio donde estos se localizan. Este modelo fue aplicado en cuatro proyectos de embalses, a través del cual se obtuvo un Índice Multi-Criterio (IMC), donde a mayor IMC, mayor prioridad tiene el proyecto. Los resultados obtenidos son: en primer lugar el Embalse A, seguido del Embalse C, luego se encuentra el Embalse B y, en último lugar se encuentra el Embalse D. Los resultados muestran que los métodos multicriterio de priorización permiten incluir características cualitativas y cuantitativas de forma simultánea en un modelo. Sin embargo, es necesario perfeccionar este tipo de metodologías en aspectos tales como la selección de criterios, subcriterios e indicadores, de forma que los proyectos de embalses no sean sub o sobre valorados. Adicional a esto, para la correcta aplicación de la metodología multicriterio, es necesario disponer de información actualizada tanto de los proyectos, como de las comunidades y territorio donde estos se localizan.

PhDRDV / Institute for Rural Development / In South Africa, various government and private sector-led initiatives have been directed towards promoting youth involvement in small retail businesses. This was designed to counter the high unemployment and poverty rates among youth. However, high failure rates of the initiatives consistently frustrate these noble efforts. Even though this is the case, neither attributes of youth-run small retail business resilience nor the factors that predispose them to the high failure rates are well-known. This situation demands taking urgent action to foster resilience in the youth-run small retail business sector. Thus, the current study focused on identifying the major threats and strengths to business and determining a set of objective criteria and indices for use in measuring resilience. Potential resilience strategies were also sought. The study was conducted in Vhembe District of Limpopo Province in South Africa. An explorative mixed research approach was employed. Participants were selected using both snowball and cluster sampling procedures. Data were collected using semi-structured interview guides and questionnaires. Qualitative data were analysed using Atlas ti version 8 software techniques such as network diagrams and code primary document tables. For each objective, in-depth results were obtained, further interrogated in a survey and analysed using the Statistical Package for Social Sciences software (IBM SPSS; version 25) in the subsequent phase. The main statistical techniques utilised were Principal Component Analysis (PCA) and Kruskal-Wallis tests. Significance was determined at P< 0.5. Results from PCA test reviewed three major threats to small retail business resilience that included poor infrastructure (28.54 %), financial infrastructure (20.97 %) and competition (14.94 %). The three factor structure accounted for a total variance of 64.46 %. Poor infrastructure and financial inadequacy threats did not vary with distance from the urban area (P > 0.05) while competition significantly varied with distance from the urban area (P< 0.05). With regard to strengths, PCA analysis produced a four factor structure that explained a total variance of 54.59 %. The four major strengths included marketing ability (16.97 %), good customer care (14.42 %), business knowledge (12.08 %) and commitment (11.13 %). A six dimension criteria for measuring small retail business resilience was established using PCA. The six dimensions encompassed security measures (18.01 %), outsourcing abilities (13. 70 %), marketing strategies (10.07), risk management (8.54 %), financial management (8.43 %) and innovation (7.89 %). The six factor structure explained a total variance of 66.67 %. These resilience pillars were related to threat detection, prevention and adaptation business mechanisms. Four resilience dimensions (security measures, marketing abilities, risk management and innovation) were similar across distance variations from the urban area (P> 0.05). However, significant differences between urban and rural areas were observed in two variables, that is, joining business alliances (P=0.012) linked to outsourcing abilities and keeping money away from the business premise (P=0.034) associated with financial management. Resilience indices were further developed utilising the six building blocks of the criteria. The indices for measuring small retail business resilience were expressed in the formula: R1= ƒ (SM1, OA1, MS1, RM1, FM1, I1, S1) + e where SM=Security Measures; OA= Outsourcing Abilities; MS= Marketing Strategies; OM=Risk Management; FM= Financial Management; I= Innovation; S= Subjective resilience dimensions and 1= particular time; e= error. The assumption underpinning these indices was that, small retail business resilience is not observable and thus it can be measured through assessing each dimension separately at a particular time. The outcomes reflected that, measuring youth-owned small retail businesses resilience encompasses a clear understanding of area specific threats and the subsequent customised performance measures. Resilience dimensions may change with time due to socio-economic changes, government policies and local conditions. As such, it is crucial to constantly assess youth small retail businesses in order to determine their current status and changes in resilience components. Current strategies and potential interventions for promoting small business resilience were also reviewed. Small retailers were currently utilising strategies such as business collaboration, specialisation and stock diversification. To, address the weaknesses associated with presently utilised strategies, potential interventions that encompassed financial support, provision of cheap stands, need for financial assistance and provision of business training and infrastructure upgrades were proposed. The present study provided a criteria and resilience indices that can be used by policy implementers, development agencies and funders to determine resilience drivers, monitor changes in resilience attributes over time and identify necessary interventions in the small retail sector. This assists decision makers to make pre-informed decisions before providing support to youth small retailers. The use of participatory research methods in the present study helped to ground the work in the youth small retail sector and thus, contributing to community engagement practices. The use of mixed study approaches has been consistently recommended in studies related to resilience measurement methods. As a result, the mixed research methods utilised in the present study provides directions for future replication in studies aimed at developing approaches for measuring resilience in the small business sector. Lastly, the simplicity of the criteria and indices make it easier for small retail business owners and other practitioners to use in future. / NRF

Magister Philosophiae (Land and Agrarian Studies) - MPhil(LAS) / As a means to assuage historical land inequities, resultant socio-economic disparities and poverty alleviation, the Namibian Government undertook to reform the land sector. Guided by the Constitution and the Resolutions of the 1991 Land Conference policy and legal framework, a fractured consensus is built on the rationale to redistribute land to a targeted group. Parallel to the reform agenda, systemic challenges to the resettlement process are growing amid questions on Government’s ability to respond to sustainable programme objectives embedded within land reforms. Literature coalesces on the issues of land-reform programmes having lost direction, being skewed in favour of a few, being biased towards commercial agriculture, and requiring review and re-configuration to be inclusive and to satisfy equity and povertyalleviation concerns. This study seeks to understand who has been benefitting from land reform by analysing the processes and procedures of identifying beneficiaries prioritised for land allocation, and institutional structures for implementation, while analysing how they produce and reproduce class differentiation and the attendent livelihood trajectories. Using qualitative research conducted in one case study site (Okarukambe constituency) the views of the smallscale farmers who benefitted were solicited. Additionally, the experiences and views of institutions and officials involved in land allocation at regional and national levels are taken into account. Theoretically the study draws from the livelihoods approach to find out the different categories of the small-scale farmers who have benefitted.

The primary objective of the project was to survey recognized “experts” in the field of neurodiagnostic audiology and practicing audiologists regarding their referral criteria and referral patterns for administering an auditory brainstem response test (ABR). For purposes of this study, “expert” was defined as any recognized audiologist with at least two or more publications and/or seminars in the field of auditory evoked potentials. Responses of experts and practicing audiologists were compared and contrasted to establish: a) if there was a standard referral pattern; b) what, if any, were the apparent critical components of referral patterns; and, c) whether or not current practice reflected the utilization of such critical components. The survey was designed to establish whether the respondent was practicing, in what type of practice setting, and how often ABRs were performed. Specificity and sensitivity of ABR outcomes was also requested. The survey was administered verbally, via telephone, to 3 experts and was sent via e-mail to 178 randomly selected audiologists in the United States. Of the latter 53 returned, 38 reported conducting ABRs. Thus, data analysis was reported on 38 respondents. The survey results did not reveal a consistent standard referral pattern. Critical components for referral were hypothesized based on the “expert” majority response. These include ABR referral based on the presence of: (1) asymmetric sensorineural hearing loss; (2) unilateral tinnitus; (3) positive reflex decay; and, (4) word recognition rollover. The majority of “non-expert” practitioners surveyed reported that these symptoms warranted consideration for referral, thus reflecting utilization of apparent critical components.

In the past decades, the world has noticed complex changes in its climate. The resources available now as well as in the future could be said to be analogous to the decreasing circumference of a funnel. The wealthy population with the means to elect what goods they wish to consume are becoming aware of their impact both on nature as well as on the less fortunate people of the planet. As a consequence environmental and human-centered factors are of higher priority than ever before in the decisions made by companies, which in turn will decide the future. This thesis aims to investigate what set of criteria can be seen as most relevant for sustainable material selection at a manufacturing company. To be relevant for the future these design criteria are decided with a base in modern research from the past decade in the field of material selection. The company chosen as a case to study and collaborate with was IKEA Components AB. The research was conducted on-site at the company’s facilities in Älmhult, Småland, as well as at the Blekinge Institute of Technology in Karlskrona, Blekinge. The company vision for the future which IKEA is striving towards is focused on lowering carbon dioxide equivalents, from which a lion share of the emissions is a result of the material they are selecting for their products. Experts within various fields of the chosen company were consulted to gain perspective and knowledge while designing and testing prototypes of a tool to facilitate a sustainable material selection. The metal alloys were analyzed for toxicity based on the percentages of all elements they contained and scored based on chosen sustainability criteria. The plastics were judged on recyclability, renewability, and degradability apart from available numbers for emission factors. After this, the materials were placed in the excel tool which was then tested by engineers and evaluated in semi-structured interviews. Updates were made to make the tool as user-friendly as was possible and new tests were conducted. Overall, the tool was appreciated by the users who tested it and more improvements were planned to finalize the prototype. Results are detailed in the latter part of the report, discussing designs the engineers preferred over others, the current scale of sustainability tools used in the company and how the testers scored the tool. In the discussion, criteria are evaluated based on their multi-criteria compliance with sustainability factors. Drawing conclusions about the subject of sustainability criteria was accomplished by conducting literature studies in material criticality, making use of the methods for sustainable product development taught in the master of science, investigating material toxicity, testing solutions for sustainable material selection at a typical furniture component manufacturing company, receiving feedback from constructors and exploring connections for the selected methods to the principles of sustainability.

Municipalities increasingly have less financial resources to spend on implementation of transport strategies and plans. This situation is putting pressure on transport professionals to minimize wasteful expenditure on projects that do not deliver high transport service improvements. As such, the need for efficient, pragmatic decision making on policy direction, infrastructure expenditure, or any transport interventions is becoming very critical. Thus, transport professionals are increasingly in need of tools to help them predict with increased accuracy the outcomes of their intended transport interventions. The City of Cape Town has a Bus Rapid Transport system called MyCiTi. Current MyCiTi operations are incurring losses. The service is kept running on the back of subsidies from the federal government. There is a need for rationalization of the system. However, with strained resources, the interventions on the system have to guarantee improvements. Overemphasis on the ability of MyCiTi BRT service to support transportation during the 2010 soccer world cup event heavily influenced the design of the network. As a result, network appraisal is one area that can be done on the system to identify areas of improvement. In this thesis, decision making support will be demonstrated using a network design appraisal process for the MyCiTi BRT system in Cape Town. The existing MyCiTi network will undergo network improvement using heuristic node insertion technique leading to multiple network scenarios in a modeling environment. Agent-Based demand mobility behavior simulation will be used on each of the network scenarios to come up with network performance indicators. These network performance indicators will be used in the multi-criteria decision analysis (MCDA) model to come up with a ranking of the network scenarios and help in deciding on the optimum network improvement intervention. Overall, findings of this research show the importance of weighting of the performance indicators. Where networks that score well in the performance indicator with the high weights also rank high. In conclusion, the study has demonstrated the importance of decision making support in interventions on complex systems like bus systems. Recommendations on the possible avenues of research stemming from this thesis have also been outlined.

Best practice in gifted and talented identification procedures involves making decisions on the basis of multiple measures. However, very little research has investigated the impact of different methods of combining multiple measures. This article examines the consequences of the conjunctive ("and"), disjunctive/complementary ("or"), and compensatory ("mean") models for combining scores from multiple assessments. It considers the impact of rule choice on the size of the student population, the ability heterogeneity of the identified students, and the psychometric performance of such systems. It also uses statistical simulation to examine the performance of the state of Georgia's mandated and complex multiple-criteria assessment system.

This article presents a conceptual benchmarking framework which applies a multiple criteria approach to assess performance. In the process, a multiple criteria procedure is used to assess the performance of three hundred and ninety two (392) Portuguese firms. Based on the results of this procedure, a conceptual framework is devised to facilitate addressing relevant benchmarking implications. The framework is designed to provide a conceptual linkage between the performance measurement methodology and the organizational benchmarking system.

L’aide multicritère à la décision (AMCD) vise à faciliter et améliorer la qualité du processus de prise de décision. Les méthodes d’AMCD permettent de traiter les problèmes de choix, rangement et classification. Ces méthodes impliquent généralement la construction d’un modèle. Déterminer les valeurs des paramètres de ces modèles n’est pas aisé. Les méthodes d’apprentissage indirectes permettent de simplifier cette tâche en apprenant les paramètres du modèle de décision à partir de jugements émis par un décideur tels que “l’alternative a est préférée à l’alternative b” ou “l’alternative a doit être classifiée dans la meilleure catégorie”. Les informations données par le décideur sont généralement parcimonieuses. Le modèle d’AMCD est appris au cours d’un processus interactif entre le décideur et l’analyste. L’analyste aide le décideur à formuler et revoir ses jugements si nécessaire. Le processus s’arrête une fois qu’un modèle satisfaisant les préférences du décideur a été trouvé. Le “preference learning” (PL) est un sous domaine du “machine learning” qui s’intéresse à l’apprentissage des préférences. Les algorithmes de ce domaine sont capables de traiter de grands jeux de données et sont validés au moyen de jeux de données artificiels et réels. Les jeux de données traités en PL sont généralement collectés de différentes sources et sont entachés de bruit.Contrairement à l’AMCD, il existe peu ou pas d’interaction avec l’utilisateur en PL. Le jeu de données fourni en entrée à l’algorithme est considéré comme un échantillon éventuellement bruité d’une “réalité” ou “vérité de terrain”. Les algorithmes utilisés dans ce domaine ont des propriétés statistiques fortes leur permettant de s’affranchir du bruit dans ces jeux de données. Dans cette thèse, nous développons des algorithmes d’apprentissage permettant d’apprendre lesparamètres de modèles d’AMCD. Plus précisément, nous développons une métaheuristique afin d’apprendre les paramètres d’un modèle appelé MR-Sort (“majority rule sorting”). Cette métaheuristique est testée sur des jeux de donnéesartificiels et réels utilisés dans le domaine du PL. Nous utilisons cet algorithme afin de traiter un problème concret dans le domaine médical. Ensuite nous modifions la métaheuristique afin d’apprendre les paramètres d’un modèle plus expressif appelé NCS (“non-compensatory sorting”). Finalement, nous développons un nouveau type de règle de veto pour les modèles MR-Sort et NCS qui permet de prendre les coalitions de critères en compte. La dernière partie de la thèse introduit les méthodes d’optimisation semi-définie positive (SDP) dans le contexte de l’aide multicritère à la décision. Précisément, nous utilisons l’optimisation SDP afin d’apprendre les paramètres d’un modèle de fonction de valeur additive. / Multiple-criteria decision analysis (MCDA) aims at providing support in order to make a decision. MCDA methods allow to handle choice, ranking and sorting problems. These methods usually involve the elicitation of models. Eliciting the parameters of these models is not trivial. Indirect elicitation methods simplify this task by learning the parameters of the decision model from preference statements issued by the decision maker (DM) such as “alternative a is preferred to alternative b” or “alternative a should be classified in the best category”. The information provided by the decision maker are usually parsimonious. The MCDA model is learned through an interactive process between the DM and the decision analyst. The analyst helps the DM to modify and revise his/her statements if needed. The process ends once a model satisfying the preferences of the DM is found. Preference learning (PL) is a subfield of machine learning which focuses on the elicitation of preferences. Algorithms in this subfield are able to deal with large data sets and are validated withartificial and real data sets. Data sets used in PL are usually collected from different sources and aresubject to noise. Unlike in MCDA, there is little or no interaction with the user in PL. The input data set is considered as a noisy sample of a “ground truth”. Algorithms used in this field have strong statistical properties that allow them to filter noise in the data sets.In this thesis, we develop learning algorithms to infer the parameters of MCDA models. Precisely, we develop a metaheuristic designed for learning the parameters of a MCDA sorting model called majority rule sorting (MR-Sort) model. This metaheuristic is assessed with artificial and real data sets issued from the PL field. We use the algorithm to deal with a real application in the medical domain. Then we modify the metaheuristic to learn the parameters of a more expressive model called the non-compensatory sorting (NCS) model. After that, we develop a new type of veto rule for MR-Sort and NCS models which allows to take criteria coalitions into account. The last part of the thesis introduces semidefinite programming (SDP) in the context of multiple-criteria decision analysis. We use SDP to learn the parameters of an additive value function model.