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.     

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.     

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.     

Stochastic dominance in multicriterion analysis under risk

Traditionally, in the literature on the modelling of decision aids one notes the propensity to treat expected utility models and outranking relation models as rivals. It may be possible, however, to benefit from the use of both approaches in a risky decision context. Stochastic dominance conditions can be used to establish, for each criterion, the preferences of a decision maker and to characterise them by a concave or convex utility function.Two levels of complexity in preference elicitation, designated as clear and unclear, are distinguished. Only in the case of unclear preferences is it potentially interesting to attempt to estimate the value function of the decision maker, thus obtaining his (her) preferences with a reduced number of questions. The number of questions that must be asked of the decision maker depends upon the level of the concordance threshold that he(she) requires in the construction of the outranking relations using the ELECTRE method.     

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.     

Painting the Prison 'Red': Constructing and Experiencing Aboriginal Identities in Prison

Dominant Western paradigms of the social work profession havelargely failed to integrate Aboriginal traditional knowledgesand practices on healing and helping. This paper contributesto the promotion of a context-based approach to social workin prison by examining Aboriginality from both institutionaland individual points of view. Drawing on documentary analysesand interviews with Aboriginal women prisoners in Canada, thepaper sheds light on the prison’s endorsement of a hegemonicvision of Aboriginality, and on social work practitioners’inclination to adhere to it. Conversely, we argue that Aboriginalwomen prisoners negotiate their passage into prison throughAboriginal self-identification configurations that often havelittle in common with the prison’s vision of Aboriginality.Service delivery in prison may be enhanced by considering individualmodes of resisting identity-based oppression in prison, andby challenging prisons’ master narrative on Aboriginality.