Production and changeover control policies of a class of failure prone buffered flow-shops

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.     

Fatigue management by truck drivers in real life situations: some suggestions to improve training

Truck driver fatigue is a major safety issue for truck drivers and the public in general. Although training prepares drivers to effectively operate a truck, it tends to minimize the importance of working constraints faced daily on-the-job and thus reduces its impact on safety and effectiveness. With experience, drivers develop skills to combat fatigue. Documenting these skills can contribute to improved training of apprentices. An ethnographic approach was used to better understand the real-life fatigue management skills of truck drivers. Participant observation was used to analyze the activity of apprentices in training and the activity of truck drivers at work. Observations indicated that training focused on time management and regulations, but did not prepare trainees to manage real-life constraints. Experienced drivers were not merely managing time; rather they were managing working constraints (including time) as a whole. To do so, they used two strategies: managing psycho-physical transformations and dynamic work planning. By integrating psycho-physical preoccupations into all aspects of work and by preparing future drivers to face real-life constraints, we could better train and prepare apprentices. Drivers do develop effective skills to combat fatigue which can improve training and better prepare future drivers to face daily constraints. These improvements can have a significant impact on fatigue and safety in the transportation industry.     

An Alternative to Fixed Transition Probabilities for the Projection of Interprovincial Migration in Canada

Internal migration plays a critical role in subnational population projections. The multiregional model is often seen as a gold standard, for its capacity to project several interconnected regions simultaneously and coherently. However, undesirable effects may occur when assumptions of constant transition probabilities are used. This paper investigates these limits, explores a few solutions provided in the literature and describes the alternative methodology used by Statistics Canada in its most recent edition of population projections for the Canadian provinces and territories. Among other things, the new method is shown to improve the consistency between internal migration assumptions and results and to facilitate the projection of the uncertainty associated with this component.     

Long‐Term Contribution of Immigration to Population Renewal in Canada: A Simulation

We analyze the direct and indirect demographic contribution of immigration to the foreign‐origin composition of the Canadian population according to various projection scenarios over a century, from 2006 to 2106. More specifically, we use Statistics Canada's Demosim microsimulation model to assess the long‐term sensitivity to immigration levels and the frequency of mixed unions of the share of immigrants in Canada and of persons who have at least one ancestor who arrived after 2006. The results of the simulations show that the population renewal process through immigration happens at a fast pace in a high immigration and low fertility country such as Canada. Under the scenarios developed, immigrants who entered after 2006 and their descendants could form the majority of the population by 2058 at the earliest and by 2079 at the latest and could represent between 62 percent and 88 percent in 2106. They also show that mixed unions are a key element of the speed at which the changes are likely to occur in the long run.     

Global Robustness with Respect to the Loss Function and the Prior

We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of , we obtain the exact set.     

The reconstruction of childhood–implications for theory and practice

First, the article deals with selected aspects of change that have framed the lives of contemporary families and children in Scandinavia: urbanization; secularization and norm-pluralism; changes in work-conditions; ideologies about the role of mothers; structural changes in family patterns; and the modern professionalization of care. Second, how change induces a crisis in the grand developmental approach within child psychology is investigated. It is argued that any a priori universal and non-contextual derived type of knowledge about development fails to grasp what is going on within families and in care and socialization in rapidly changing cultures such as Scandinavia and other Western European countries. An alternative paradigm is presented that should make us better able to understand development as embedded in culture and time. Third, some new challenges for educating the skilled practitioner are focused upon. Here reflective competence will be addressed: expertise as an ‘eternal process of inquiry; the ability to use multi-based and often conflicting knowledge about families and children in order to solve problems in everyday situations; the strengthened emphasis on the practitioner-as-person and the ability to use practical and reflected knowledge in different contexts. Finally, new bases of scientific knowledge are suggested for use in the future education of practitioners.     

Economic Modeling Triggers More Efficient Planning: An Experimental Justification

Consider a firm as an organization that needs to efficiently coordinate several specialized departments in an uncertain environment. Decision making involves collective planning sessions and decentralized operational processes. In this setting this paper explores the role of economic modeling through an experimental game. Results support the idea that economic modeling favors higher performance. Economic modeling facilitates the emergence of common knowledge and the decomposition of a group decision problem into individual decision problems that are meaningfully interrelated. This paper was presented at the Porquerolles Summer School on Cognitive Sciences, September 2001.     

Nonparametric particle filtering approaches for identification and inference in nonlinear state-space dynamic systems

Most system identification approaches and statistical inference methods rely on the availability of the analytic knowledge of the probability distribution function of the system output variables. In the case of dynamic systems modelled by hidden Markov chains or stochastic nonlinear state-space models, these distributions as well as that of the state variables themselves, can be unknown or untractable. In that situation, the usual particle Monte Carlo filters for system identification or likelihood-based inference and model selection methods have to rely, whenever possible, on some hazardous approximations and are often at risk. This review shows how a recent nonparametric particle filtering approach can be efficiently used in that context, not only for consistent filtering of these systems but also to restore these statistical inference methods, allowing, for example, consistent particle estimation of Bayes factors or the generalisation of model parameter change detection sequential tests. Real-life applications of these particle approaches to a microbiological growth model are proposed as illustrations.