Improving Transition Outcomes: CASE (Career And Self Awareness) Prototype: Success Stories

Success Stories from the CASE (Career And Self Awareness) prototype.

Improving Transition Outcomes: CASE (Career And Self Awareness) Prototype: General Information

General information on the CASE (Career And Self Awareness) prototype.

Improving Transition Outcomes: CASE (Career And Self Awareness) Prototype: Contact Information

Contact information for the CASE (Career And Self Awareness) prototype.

Improving Transition Outcomes: CASE (Career And Self Awareness) Prototype: Basic Points

Basic Points about the CASE (Career And Self Awareness) prototype.

Improving Transition Outcomes: CASE (Career And Self Awareness) Prototype: Conference Demonstration

Promotional article recognizing a CASE (Career And Self Awareness) conference demonstration.

Parallel scheduling of multiclass M/M/m queues: Approximate and heavy-traffic optimization of achievable performance

We address the problem of scheduling a multiclass $M/M/m$ queue with Bernoulli feedback on $m$ parallel servers to minimize time-average linear holding costs. We analyze the performance of a heuristic priority-index rule, which extends Klimov's optimal solution to the single-server case: servers select preemptively customers with larger Klimov indices. We present closed-form suboptimality bounds (approximate optimality) for Klimov's rule, which imply that its suboptimality gap is uniformly bounded above with respect to (i) external arrival rates, as long as they stay within system capacity;and (ii) the number of servers. It follows that its relativesuboptimality gap vanishes in a heavy-traffic limit, as external arrival rates approach system capacity (heavy-traffic optimality). We obtain simpler expressions for the special no-feedback case, where the heuristic reduces to the classical $c \mu$ rule. Our analysis is based on comparing the expected cost of Klimov's ruleto the value of a strong linear programming (LP) relaxation of the system's region of achievable performance of mean queue lengths. In order to obtain this relaxation, we derive and exploit a new set ofwork decomposition laws for the parallel-server system. We further report on the results of a computational study on the quality of the $c \mu$ rule for parallel scheduling.

The crew-scheduling module in the GIST system

The public transportation is gaining importance every year basically duethe population growth, environmental policies and, route and streetcongestion. Too able an efficient management of all the resources relatedto public transportation, several techniques from different areas are beingapplied and several projects in Transportation Planning Systems, indifferent countries, are being developed. In this work, we present theGIST Planning Transportation Systems, a Portuguese project involving twouniversities and six public transportation companies. We describe indetail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and geneticalgorithm to solve the bus-driver-scheduling problem. The metaheuristicshave been successfully incorporated in the GIST Planning TransportationSystems and are actually used by several companies.

Suivi du patient oncologique, garant de la permanence de soi [Follow-up of the cancer patient, maintaining self-identity]

L'individu confronté au diagnostic de cancer subit un bouleversement brutal de ses repères et de ses habitudes. La maladie représente une menace pour son équilibre de vie et sa stabilité sociale. Sa capacité à faire face et à opérer différents remaniements dans sa façon d'être au monde et d'envisager l'avenir est en partie déterminée par ses ressources personnelles. Toutefois, le soutien émotionnel peut également représenter un moyen privilégié de donner du sens à cette expérience singulière qu'est la maladie. La reconstruction narrative dans un cadre soutenant, caractérisé par une écoute bienveillante, offre au patient la possibilité de reconnaître sa souffrance comme partie intégrante de lui-même. Un récit de vie qui intègre la maladie lui permet de se réapproprier son histoire. Cette démarche nécessite de la part du thérapeute une disponibilité psychique et temporelle et la capacité de soutenir le patient dans un processus de liaison à travers les différentes étapes de la maladie. The individual facing the diagnosis of cancer is subjected to abrupt changes with regard to his inner world, his life, habits and social relationships. The patient's capacity to cope, to integrate changes in the way of living and to face the future is determined by his personal resources. However, psychological support may also be an important mean to search for and find sense to the singular experience of the illness. The narrative reconstruction within a supportive setting provides the patient a possibility to recognise his sufferance as an integral part of himself. A life narrative, which integrates the illness, allows the patient to re-appropriate his history again. Such a therapeutic project necessitates from the therapist a psychological and temporal disponibility and a capacity to create links all along the different stages of the disease.

On certain greedoid polyhedra, partially indexable scheduling problems and extended restless bandit allocation indices

We present a polyhedral framework for establishing general structural properties on optimal solutions of stochastic scheduling problems, where multiple job classes vie for service resources: the existence of an optimal priority policy in a given family, characterized by a greedoid(whose feasible class subsets may receive higher priority), where optimal priorities are determined by class-ranking indices, under restricted linear performance objectives (partial indexability). This framework extends that of Bertsimas and Niño-Mora (1996), which explained the optimality of priority-index policies under all linear objectives (general indexability). We show that, if performance measures satisfy partial conservation laws (with respect to the greedoid), which extend previous generalized conservation laws, then theproblem admits a strong LP relaxation over a so-called extended greedoid polytope, which has strong structural and algorithmic properties. We present an adaptive-greedy algorithm (which extends Klimov's) taking as input the linear objective coefficients, which (1) determines whether the optimal LP solution is achievable by a policy in the given family; and (2) if so, computes a set of class-ranking indices that characterize optimal priority policies in the family. In the special case of project scheduling, we show that, under additional conditions, the optimal indices can be computed separately for each project (index decomposition). We further apply the framework to the important restless bandit model (two-action Markov decision chains), obtaining new index policies, that extend Whittle's (1988), and simple sufficient conditions for their validity. These results highlight the power of polyhedral methods (the so-called achievable region approach) in dynamic and stochastic optimization.

Driver scheduling problem modelling

The Drivers Scheduling Problem (DSP) consists of selecting a set of duties for vehicle drivers, for example buses, trains, plane or boat drivers or pilots, for the transportation of passengers or goods. This is a complex problem because it involves several constraints related to labour and company rules and can also present different evaluation criteria and objectives. Being able to develop an adequate model for this problem that can represent the real problem as close as possible is an important research area.The main objective of this research work is to present new mathematical models to the DSP problem that represent all the complexity of the drivers scheduling problem, and also demonstrate that the solutions of these models can be easily implemented in real situations. This issue has been recognized by several authors and as important problem in Public Transportation. The most well-known and general formulation for the DSP is a Set Partition/Set Covering Model (SPP/SCP). However, to a large extend these models simplify some of the specific business aspects and issues of real problems. This makes it difficult to use these models as automatic planning systems because the schedules obtained must be modified manually to be implemented in real situations. Based on extensive passenger transportation experience in bus companies in Portugal, we propose new alternative models to formulate the DSP problem. These models are also based on Set Partitioning/Covering Models; however, they take into account the bus operator issues and the perspective opinions and environment of the user.We follow the steps of the Operations Research Methodology which consist of: Identify the Problem; Understand the System; Formulate a Mathematical Model; Verify the Model; Select the Best Alternative; Present the Results of theAnalysis and Implement and Evaluate. All the processes are done with close participation and involvement of the final users from different transportation companies. The planner s opinion and main criticisms are used to improve the proposed model in a continuous enrichment process. The final objective is to have a model that can be incorporated into an information system to be used as an automatic tool to produce driver schedules. Therefore, the criteria for evaluating the models is the capacity to generate real and useful schedules that can be implemented without many manual adjustments or modifications. We have considered the following as measures of the quality of the model: simplicity, solution quality and applicability. We tested the alternative models with a set of real data obtained from several different transportation companies and analyzed the optimal schedules obtained with respect to the applicability of the solution to the real situation. To do this, the schedules were analyzed by the planners to determine their quality and applicability. The main result of this work is the proposition of new mathematical models for the DSP that better represent the realities of the passenger transportation operators and lead to better schedules that can be implemented directly in real situations.

The gain-loss asymmetry and single-self preferences

Kahneman and Tversky asserted a fundamental asymmetry between gains and losses, namely a reflection effect which occurs when an individual prefers a sure gain of $ pz to anuncertain gain of $ z with probability p, while preferring an uncertain loss of $z with probability p to a certain loss of $ pz.We focus on this class of choices (actuarially fair), and explore the extent to which thereflection effect, understood as occurring at a range of wealth levels, is compatible with single-self preferences.We decompose the reflection effect into two components, a probability switch effect,which is compatible with single-self preferences, and a translation effect, which is not. To argue the first point, we analyze two classes of single-self, nonexpected utility preferences, which we label homothetic and weakly homothetic. In both cases, we characterize the switch effect as well as the dependence of risk attitudes on wealth.We also discuss two types of utility functions of a form reminiscent of expected utility but with distorted probabilities. Type I always distorts the probability of the worst outcome downwards, yielding attraction to small risks for all probabilities. Type II distorts low probabilities upwards, and high probabilities downwards, implying risk aversion when the probability of the worst outcome is low. By combining homothetic or weak homothetic preferences with Type I or Type II distortion functions, we present four explicit examples: All four display a switch effect and, hence, a form of reflection effect consistent a single self preferences.

Are tax subsidies for private medical insurance self-financing? Evidence from a microsimulation model for outpatient and inpatient episodes

This paper analyses whether or not tax subsidies to private medicalinsurance are self-financing by means of a structural approach. Weconstruct a simulation routine based on a microeconometric discretechoice model that allows us to evaluate the impact of premium changeson the utilisation of outpatient and inpatient health care services. Wesimulate the 1999 Spanish tax reform that abolished the tax deductionfor expenditures on private health insurance using a representativesample of the Catalan population. Prior to this reform, foregone taxrevenue arising from deductions after the purchase of private insuranceamounted to 69.2 M. per year. In contrast, the elimination of thesubsidies to private policies is estimated to generate an extra costfor the public sector of about 8.9 M. per year.

Training in flexible intensive insulin therapy improves quality of life, decreases the risk of hypoglycaemia and ameliorates poor metabolic control in patients with type 1 diabetes.

AIM: Intensified insulin therapy has evolved to be the standard treatment of type 1 diabetes. However, it has been reported to increase significantly the risk of hypoglycaemia. We studied the effect of structured group teaching courses in flexible insulin therapy (FIT) on psychological and metabolic parameters in patients with type 1 diabetes. METHODS: We prospectively followed 45 type 1 diabetic patients of our outpatient clinic participating in 5 consecutive FIT teaching courses at the University Hospital of Basel. These courses consist of 7 weekly ambulatory evening group sessions. Patients were studied before and 1, 6, and 18 months after the course. Main outcome measures were glycated haemoglobin (HbA1c), severe hypoglycaemic events, quality of life (DQoL), diabetes self-control (IPC-9) and diabetes knowledge (DWT). RESULTS: Quality of life, self-control and diabetes knowledge improved after the FIT courses (all p<0.001). The frequency of severe hypoglycaemic events decreased ten-fold from 0.33 episodes/6 months at baseline to 0.03 episodes/6 months after 18 months (p<0.05). Baseline HbA1c was 7.2+/-1.1% and decreased in the subgroup with HbA1c > or = 8% from 8.4% to 7.8% (p<0.05). CONCLUSIONS: In an unselected, but relatively well-controlled population of type 1 diabetes, a structured, but not very time consuming FIT teaching programme in the outpatient setting improves psychological well-being and metabolic parameters.