'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

Activity-based life cycle costing model for cost estimation and monitoring – Case: manned and autonomous dry bulk carrier

Recent developments in automation, robotics and artificial intelligence have given a push to a wider usage of these technologies in recent years, and nowadays, driverless transport systems are already state-of-the-art on certain legs of transportation. This has given a push for the maritime industry to join the advancement. The case organisation, AAWA initiative, is a joint industry-academia research consortium with the objective of developing readiness for the first commercial autonomous solutions, exploiting state-of-the-art autonomous and remote technology. The initiative develops both autonomous and remote operation technology for navigation, machinery, and all on-board operating systems. The aim of this study is to develop a model with which to estimate and forecast the operational costs, and thus enable comparisons between manned and autonomous cargo vessels. The building process of the model is also described and discussed. Furthermore, the model’s aim is to track and identify the critical success factors of the chosen ship design, and to enable monitoring and tracking of the incurred operational costs as the life cycle of the vessel progresses. The study adopts the constructive research approach, as the aim is to develop a construct to meet the needs of a case organisation. Data has been collected through discussions and meeting with consortium members and researchers, as well as through written and internal communications material. The model itself is built using activity-based life cycle costing, which enables both realistic cost estimation and forecasting, as well as the identification of critical success factors due to the process-orientation adopted from activity-based costing and the statistical nature of Monte Carlo simulation techniques. As the model was able to meet the multiple aims set for it, and the case organisation was satisfied with it, it could be argued that activity-based life cycle costing is the method with which to conduct cost estimation and forecasting in the case of autonomous cargo vessels. The model was able to perform the cost analysis and forecasting, as well as to trace the critical success factors. Later on, it also enabled, albeit hypothetically, monitoring and tracking of the incurred costs. By collecting costs this way, it was argued that the activity-based LCC model is able facilitate learning from and continuous improvement of the autonomous vessel. As with the building process of the model, an individual approach was chosen, while still using the implementation and model building steps presented in existing literature. This was due to two factors: the nature of the model and – perhaps even more importantly – the nature of the case organisation. Furthermore, the loosely organised network structure means that knowing the case organisation and its aims is of great importance when conducting a constructive research.

Selbstgesteuertes Lernen mit Kompetenzrastern. Ein theoretischer Blick auf das Potenzial eines pädagogischen Instruments zum individualisierten Lernen

Kompetenzraster sind pädagogische Instrumente, die zum kompetenzorientierten, individualisierten und selbstgesteuerten Lernen in beruflichen Schulen eingesetzt werden. Sie werden üblicherweise im Rahmen eines pädagogischen Gesamtkonzeptes genutzt, indem die Raster oft ein zentrales Instrument in einem komplexen Gefüge schulischer Lern- und Lehrprozesse sind. Kompetenzraster sind häufig der Fixpunkt, an dem sich andere Instrumente (wie Checklisten und Lernjobs) orientieren und sie definieren die Ausgangs- und Zielpunkte der Lernprozesse. Dabei werden den Schülern üblicherweise Freiheitsgrade eingeräumt, so dass sie (mit-) entscheiden ob, was, wann, wie und woraufhin sie lernen. Die schulische Arbeit mit den Rastern kann als ein Versuch angesehen werden, die Lernenden in den Mittelpunkt pädagogischen Denkens und Handelns zu stellen. Dieser Beitrag hat das Ziel, selbstgesteuertes Lernen aus einer distanzierten, vom einzelnen pragmatischen Modell abstrahierenden und eher theoretischen Perspektive auf das individualisierte Lernen mit Kompetenzrastern zu beziehen. Im Kern wird ein Systematisierungsansatz entwickelt, in dem die komplexen Zusammenhänge des Lernens mit Kompetenzrastern im Kontext von selbstgesteuertem Lernen dargestellt werden. Damit soll ein Beitrag zur Elaboration des Lernens mit Kompetenzrastern in beruflichen Schulen geleistet werden. Konkret wird die folgende Frage fokussiert: Was können Kompetenzraster im Rahmen selbstgesteuerten Lernens leisten? (DIPF/Orig.)

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

La relación de ayuda en el campo de la enseñanza-aprendizaje

A través de un análisis retrospectivo acerca de las características educativas de la enseñanza de enfermería, se plantea una reflexión acerca del tipo de aprendizaje promovido desde una práctica docente universitaria ad hoc y cómo ello ha influido en la formación de enfermería. Se esbozan algunos planteamientos sobre el cambio de mentalidad que ha estado ocurriendo dentro de las escuelas de enfermería, mediante la promoción de nuevas formas de enseñar y de aprender, que dan valor al protagonismo del estudiante, con el fin de lograr su desarrollo integral. Se plantea la construcción del aprendizaje facilitado a través de la relación de ayuda, concepción ética y creativa del proceso de construcción del conocimiento profesional.

Efeitos de mudanças sucessivas na definição de produto agregado em metacontingências

Dissertação (mestrado)—Universidade de Brasília, Instituto de Psicologia, Departamento de Processos Psicológicos Básicos, Programa de Pós-Graduação em Ciências do Comportamento, 2015.

Gorputz hezkuntzan egindako trebetasun sozialen inguruko behaketa

50 p. -- E-mail de la autora: amaiasilvo24@gmail.com

Beyond human and intellectual capital: Profiling the value of knowledge, skills and experience

The concept of human capital is associated mainly with the Nobel Laureate Gary Becker and, in his usage, has a clear conceptual basis as investment in the costs of formal education. By contrast, this paper suggests that ‘intellectual capital’ is a re-branding of knowledge, skills and experience rather than re-conceptualisation of resource based learning. Becker also chose not to include informal knowledge, skills or experience within his concept of human capital, which remains limited by its constrained premises. This paper submits that both human capital and intellectual capital advocates fail to identify or measure the tacit knowledge and implicit learning which increasingly is recognised as a key to the competitive advantage of organisations. It first focuses on the conceptual basis of claims made for human capital and intellectual capital, outlines limits in their methodology, and contrasts these with insights from theories of tacit knowledge and implicit learning and the central role within them of informal or non-formal skill acquisition. It develops and illustrates instances of interfacing tacit and explicit knowledge before introducing a methodology for profiling the acquisition of knowledge, ability and skills. It does so by introducing the concepts of non-formal learningfrom- work (LfW) and informal learning-from-life (LfL), with evidence from a four country EU case study commissioned within the lifelong learning remit of the Lisbon Agenda.

Propuesta para un viraje en el estudio de los medios en Las Márgenes

Este artículo explora diferentes tipos de apropiación de tecnologías mediáticas en las márgenes y propone un cambio en el acercamiento investigativo en diferentes niveles: 1) en lugar de centrarse en tecnologías individuales, la investigación sobre medios en las márgenes debe examinar cómo los/as comunicadores locales se desenvuelven en ecologías mediáticas que ofrecen recursos y retos específicos en cada situación histórica; 2) en lugar de tratar de determinar si las tecnologías mediáticas usadas en las márgenes son nuevas u obsoletas, digitales o no, es urgente comprender cómo los/as comunicadores asentados en lo local detectan necesidades de información y comunicación específicas y usan las tecnologías disponibles para abordar tales necesidades; 3) la investigación sobre medios en las márgenes debe esclarecer cómo las/los protagonistas de este tipo de comunicación ciudadana y comunitaria reinventan, hibridan, reciclan y tienden lazos entre plataformas tecnológicas. En resumen, para entender las tecnologías mediáticas en las márgenes la investigación debe asumir altos niveles de complejidad, debe mantener la noción de ecologías mediáticas y entender cómo, a nivel local, comunicadores comunitarios profundamente inmersos en lo cotidiano e histórico, ajustan las tecnologías mediáticas a las necesidades de sus comunidades.

Hazard Mitigation Element Quality in Coastal Comprehensive Plans in a State with Strong Requirements for Hazard Mitigation Plan Elements

This dissertation examines the quality of hazard mitigation elements in a coastal, hazard prone state. I answer two questions. First, in a state with a strong mandate for hazard mitigation elements in comprehensive plans, does plan quality differ among county governments? Second, if such variation exists, what drives this variation? My research focuses primarily on Florida’s 35 coastal counties, which are all at risk for hurricane and flood hazards, and all fall under Florida’s mandate to have a comprehensive plan that includes a hazard mitigation element. Research methods included document review to rate the hazard mitigation elements of all 35 coastal county plans and subsequent analysis against demographic and hazard history factors. Following this, I conducted an electronic, nationwide survey of planning professionals and academics, informed by interviews of planning leaders in Florida counties. I found that hazard mitigation element quality varied widely among the 35 Florida coastal counties, but were close to a normal distribution. No plans were of exceptionally high quality. Overall, historical hazard effects did not correlate with hazard mitigation element quality, but some demographic variables that are associated with urban populations did. The variance in hazard mitigation element quality indicates that while state law may mandate, and even prescribe, hazard mitigation in local comprehensive plans, not all plans will result in equal, or even adequate, protection for people. Furthermore, the mixed correlations with demographic variables representing social and disaster vulnerability shows that, at least at the county level, vulnerability to hazards does not have a strong effect on hazard mitigation element quality. From a theory perspective, my research is significant because it compares assumptions about vulnerability based on hazard history and demographics to plan quality. The only vulnerability-related variables that appeared to correlate, and at that mildly so, with hazard mitigation element quality, were those typically representing more urban areas. In terms of the theory of Neo-Institutionalism and theories related to learning organizations, my research shows that planning departments appear to have set norms and rules of operating that preclude both significant public involvement and learning from prior hazard events.

Interdependencies in Corporate Development: Relationship between Strategic Alliance and Acquisition Activities

A substantial amount of work in the field of strategic management has attempted to explain the antecedents and outcomes of organizational learning. Though multinational corporations simultaneously engage in various types of tasks, activities, and strategies on a regular basis, the transfer of organizational learning in a multi-task context has largely remained under-explored in the literature. To inform our understanding in this area, this dissertation aimed at synthesizing findings from two parallel research streams of corporate development activities: strategic alliances and acquisitions. Structured in the form of two empirical studies, this dissertation examines: 1) the strategic outcomes of alliance experience of previously allying partners in terms of subsequent acquisition attempts, and 2) the performance implications of prior alliance experience for acquisitions. The first study draws on the relational view of inter-organizational governance to explain how various deal-specific and dyadic characteristics of a partnership relate to partnering firms’ post-alliance acquisition attempts. This model theorizes on a variety of relational mechanisms to build a cohesive theory of inter-organizational exchanges in a multi-task setting where strategic alliances ultimately lead to a firm’s decision to commit further resources. The second study applies organizational learning theory, and specifically examines whether frequency, recency, and relatedness of different dimensions of prior alliances, beyond the dyad-level experience, relate to an acquirer’s superior post-acquisition performance. The hypotheses of the studies are tested using logistic and ordinary least square regressions, respectively. Results analyzed from a sample of cross-border alliance and acquisition deals attempted (for study I) and/or completed (for study II) during the period of 1991 to 2011 generally support the theory that relational exchange determines acquiring firms’ post alliance acquisition behavior and that organizational routines and learning from prior alliances influence a future acquirer’s financial performance. Overall, the empirical findings support our overarching theory of interdependency, and confirm the transfer effect of learning across these alternate, yet related corporate strategies of alliance and acquisition.

Pedagogical Appropriation of Information and Communication Technologies (ICT) by West African Educators = Appropriation pédagogique des technologies de l’information et de la communication (TIC) par les éducateurs ouest-africains

Œuvre dédiée à Alioune Camara; merci au Prof. Denis Dougnon de l’Université de Bamako pour le parrainage

Pedagogical Appropriation of Information and Communication Technologies (ICT) by West African Educators = Appropriation pédagogique des technologies de l’information et de la communication (TIC) par les éducateurs ouest-africains

Œuvre dédiée à Alioune Camara; merci au Prof. Denis Dougnon de l’Université de Bamako pour le parrainage

Incidencia del proyecto “ASEAN community” en el desarrollo humano de Tailandia (2004-2014)

Esta investigación se interesa en evaluar los logros y retos que ha presentado el proyecto ASEAN Community en cada una de sus tres áreas de acción (Comunidad económica, comunidad de política y seguridad, y comunidad socio-cultural) ante su aplicación en Tailandia. De esta manera, se busca analizar la incidencia que ha tenido el proyecto en el Desarrollo Humano de Tailandia durante el periodo 2004-2014. A través del análisis del estatus actual a la luz del concepto de libertades instrumentales se realiza la evaluación de los resultados de los proyectos y su conveniencia o no para el desarrollo humano de la sociedad tailandesa.

Revisión del uso de los medicamentos biosimilares vs. biológicos: implicaciones para la salud en Colombia.

Este documento tiene como objetivo describir las implicaciones para la salud con el uso de medicamentos biosimilares en comparación con los medicamentos biológicos en Colombia. Así mismo, describir el contexto normativo acerca del uso de medicamentos biosimilares, las recomendaciones y lineamientos sobre seguridad y efectividad del uso de medicamentos Biosimilares y Biológicos, partiendo de sus diferencias biomoleculares. Para esto, se desarrolló una revisión documental electrónica y manual de la literatura en bases de datos, revistas y libros limitada a términos MeSH. La selección de los artículos incluyo documentos completos publicados en revistas indexadas de los últimos 10 años, en español e inglés; la información recolectada se organizó para la construcción del presente documento. Concluyendo, se encontró que las patentes de muchos medicamentos biológicos han vencido o están próximas a caducar y varios biosimilares están desarrollándose y comercializándose incluso en países sin regulaciones estrictas. Los biosimilares nunca podrán ser iguales al original por su complejidad molecular, por ello debemos integrarlos a los sistemas de farmacovigilancia mejorando trazabilidad e identificando su origen mientras se establecen denominaciones comunes distinguibles. La evidencia actual sugiere que la regulación de medicamentos biosimilares debe ser evaluada y armonizada en todo el mundo.