Valoriser les processus pour élaborer des cahiers des charges: une approche innovante

Lors de la fusion de communes, l’organisation des services du feu et leur efficacité dans la lutte contre les incendies et autres sinistres est une préoccupation des autorités cantonales. L’accroissement des activités socio-économiques et des besoins humains exerce une pression grandissante sur les corps des sapeurs-pompiers (CSP), créant des difficultés de recrutement et de fonctionnement, réduisant l’efficacité d’utilisation des ressources disponibles (hommes et équipement). Le processus de collaboration ou de fusion au niveau des CSP vise à mieux identifier les problèmes et à être capable de répondre aux normes en vigueur, notamment en intervenant dans le délai stipulé (15 minutes à partir de l’alarme). Ce papier introduit un modèle de représentation des processus liés aux activités des sapeurs-pompiers (SP). La bonne compréhension de ces processus permet d’identifier les activités de chaque acteur et ainsi d’élaborer son cahier des charges (CdC). A partir de trois processus caractéristiques, les différentes étapes du modèle proposé sont explicitement mises en évidence

An improved tabu search approach for solving the job shop scheduling problem with tooling constraints

AbstractFlexible manufacturing systems (FMSs) are nowadays installed in the mechanical industry. In such systems, many different part types are produced simultaneously and it is necessary to take tooling constraints into account for finding an optimal schedule.A heuristic method is presented for solving the m-machine, n-job shop scheduling problem with tooling constraints. This method, named TOMATO, is based on an adaptation of tabu search techniques and is an improvement on the JEST algorithm proposed by Widmer in 1991

Finding the adequate location scenario after the merger of fire brigades thanks to Multiple Criteria Decision Analysis Methods

This paper addresses the issue of selecting a suitable location for a fire station in canton of Fribourg, as a result of a fire brigades’merger, by applying Multiple Criteria Decision Analysis (MCDA) methods. Solving the problem of determining fire station locations through various methods has been analyzed in-­‐depth by researchers. However, a different approach, based on application of methods like ELECTRE and PROMETHEE is advanced in this paper. The selection of the most suitable fire station site is obtained by applying the designated methods to five distinctive alternatives (called scenarios), taking into consideration the relatively limited information and specifics, and the extensive number of relevant criteria that summed up to seventy-­‐ eight. Taking the merger of the three local fire departments as an example, the proposed methods for selecting a suitable location for the fire station demonstrate and justify the reason behind this choice. Research shows that the applied methods have been proven to be useful and powerful tools that exhibited acceptable levels of consistency when selecting the best project. The main finding is that one scenario in particular proved to be strongly dominant over the others and most suitable in determining the fire station location

Intra-organizational knowledge transfer process in Vietnam's information technology companies

Intra-organizational knowledge transfer has attracted much attention of researchers and practitioners in recent years since knowledge transfer has been considered as a critical determinant of an organization’s capacity to confer sustainable competitive advantage. Despite extensive research on knowledge transfer issues, the effect of knowledge transfer on organizational performance still has not been fully examined or attracted adequate empirical testing. Therefore, the objective of this study is to investigate organizational factors influencing intra-organizational knowledge transfer, and examine the relationship between knowledge transfer process, its antecedents and organizational performance. Drawing on several theoretical streams, an integrated theoretical model of intra organizational knowledge transfer together with 13 hypotheses were developed and tested in the context of IT companies in Vietnam. To achieve the objectives, a triangulation of quantitative and qualitative studies was applied. A quantitative survey was employed to test hypotheses in the conceptual model derived from relevant literature. Data were collected from a survey of 218 managers and technical staff working in 36 IT companies located in Hanoi and HoChiMinh City. Multiple regression techniques were used to analyze the data. A case study research was conducted with the aim of illustrating the intra organizational knowledge transfer process within a company operating in a transition economy like Vietnam. Data for case study were mainly collected by interviewing managers and technical staff during a one-month field study in the FPT Software Solutions Company. The main findings showed that intra-organizational knowledge transfer is most affected by organizational culture, incentive system and organizational structure. Adaptability and solidarity are two culture values enabling the transfer process. A transparent and flexible incentive system motivates individuals to exchange and apply knowledge in their daily work. High level of centralization creates difficulties for social interaction and reduces autonomy and active involvement of employees, vi which are essential for successful knowledge transfer. High formalization facilitates the knowledge transfer process by providing a clear direction for employees and enhancing communication flow through an extensive monitoring and reporting requirement. The frequency of using IT tools did not significantly influence the intra organizational knowledge transfer process after other independent variables were added in the regression model. This suggests either that IT tools may not directly itself is not enough to ensure successful knowledge transfer. Therefore, to facilitate knowledge transfer process, it is important to foster knowledge-sharing attitude through providing greater opportunities for deeper involvement of users in the system. Although the knowledge transfer process was found not to mediate the relationship between its antecedents and organizational performance, the process itself moderately predicts organizational performance. This suggests that intra organizational knowledge transfer process should be considered as one of the factors contributing to company performance. The research has filled gaps in existing literature in several ways. Firstly, it extends our understanding of the important facilitators of intra-organizational knowledge transfer process. Secondly, it attempts to integrate both soft and hard organizational factors to create a comprehensive model of intra-organizational knowledge transfer. Thirdly, it clarifies the role of the intra-organizational knowledge transfer process in improving the company’s performance in a transition economy. Overall, the results of the study contribute to the advancement of research in the area of intra-organizational knowledge transfer and provide practical implications for managers of IT companies in Vietnam by shedding light on determinant factors of knowledge transfer process and examining the link between knowledge transfer process and firm performance

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving

A dataset on the physiological state and behavior of drivers in conditionally automated driving

This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simulator. Five studies simulated conditionally automated driving (L3 SAE), and the other one simulated manual driving (L0-SAE). The dataset includes physiological data (electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steering wheel angle, …), performance data of non-driving-related tasks, and questionnaire responses. Among them, measures from standardized questionnaires were collected, either to control the experimental manipulation of the driver's state, or to measure constructs related to human factors and driving safety (drowsiness, mental workload, affective state, situation awareness, situational trust, user experience). In the provided dataset, some raw data have been processed, notably physiological data from which physiological indicators (or features) have been calculated. The latter can be used as input for machine learning models to predict various states (sleep deprivation, high mental workload, ...) that may be critical for driver safety. Subjective self-reported measures can also be used as ground truth to apply regression techniques. Besides that, statistical analyses can be performed using the dataset, in particular to analyze the situational awareness or the takeover quality of drivers, in different states and different driving scenarios. Overall, this dataset contributes to better understanding and consideration of the driver's state and behavior in conditionally automated driving. In addition, this dataset stimulates and inspires research in the fields of physiological/affective computing and human factors in transportation, and allows companies from the automotive industry to better design adapted human-vehicle interfaces for safe use of automated vehicles on the roads