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Persaingan di dunia industri saat ini menuntut para produsen untuk bersaing dalam memberikan kualitas produk yang baik dengan waktu produksi yang singkat serta biaya produksi yang relatif murah. Hal tersebut berkaitan dengan mesin produksi yang memiliki peran penting untuk menghasilkan produk dengan kualitas baik. PT. Voith Paper Rolls Indonesia merupakan perusahaan jasa dan manufaktur yang bergerak dibidang perbaikan roll dan pembuatan material roll cover. Salah satu proses produksi yang dilakukan pada saat perbaikan roll yaitu proses pembubutan yang dilakukan sebanyak dua kali. Pada proses pembubutan sering kali terdapat perbedaan waktu yang signifikan antara waktu aktual dengan estimasi waktu yang didapatkan dari formulasi eksisting. Oleh karena itu laporan ini akan berfokus pada upaya perbaikan formulasi perhitungan untuk estimasi waktu proses pembubutan di mesin bubut A pada hard plain rubber roll dengan menganalisis pengaruh luas permukaan terhadap waktu proses menggunakan metode regresi linier sederhana melalui aplikasi IBM SPSS.

International audience; This paper is prepared within a collaboration between the Instituto Politécnico Nacional, which is a Mexican research institute that manages research on sliding-mode control theory, and the ARIA research team of the Intégration du Matériau au Système Lab., a French research groupthat engages research on model-based fault diagnosis and fault-tolerant control theories. The paper reviews the application of sliding mode control techniques to fault tolerant control and provides perspectives leading to posing some open problems. Operating principles, definitions of the basic concepts are recalled along with the control objectives and design procedures. The evolution of the sliding mode control technique through five generations (as classified by Fridman, Moreno and coworkers) is reviewed. Their respective design procedures, limitations, and robustness properties are also highlighted. The application of the five generations of sliding-mode controllers to fault-tolerant control is discussed. The focus is on some open problems that are judged to commonly be overlooked. Some applications in real-world systems are also presented.

This paper proposes an efficient decision support tool for the optimal production scheduling of a variety of paper grades in a paper machine. The tool is based on a continuous-time scheduling model and generalized disjunctive programming. As the full-space scheduling model corresponds to a large-scale mixed integer linear programming model, we apply data analytics techniques to reduce the size of the decision space, which has a profound impact on the computational efficiency of the model and enables us to support the solution of large-scale problems. The data-driven model is based on an automated method of identifying the forbidden and recommended paper grade sequences, as well as the changeover durations between two paper grades. The results from a real industrial case study show that the data-driven model leads to good results in terms of both solution quality and CPU time in comparison to the full-space model.

International audience; Nowadays, floods have become the widest global environmental and economic hazard in many countries, causing huge loss of lives and materials damages. It is, therefore, necessary to build an efficient flood forecasting system. The physical-based flood forecasting methods have indeed proven to be limited and ineffective. In most cases, they are only applicable under certain conditions. Indeed, some methods do not take into account all the parameters involved in the flood modeling, and these parameters can vary along a channel, which results in obtaining forecasted discharges very different from observed discharges. While using machine learning tools, especially artificial neural networks schemes appears to be an alternative. However, the performance of forecasting models, as well as a minimum error of prediction, is very interesting and challenging issues. In this paper, we used the multilayer perceptron in order to design a flood forecasting model and used discharge as input–output variables. The designed model has been tested upon intensive experiments and the results showed the effectiveness of our proposal with a good forecasting capacity.

This study deals with the Human Reliability Analysis. A causal methodology is used to report the dynamic responses over time of humans at work. This structure is mounted inside a main framework which includes multi criteria decisions about main and subsidiaries concepts that influence the work-stress and the operations management. Factors and variables from the Human, Team, Work, Environment behavior are considered and declined in constant, measurable and depended elements with inter and intra level connections. Contextualization in a safety based approach is reported as per different tasks in a motor company. Calibration on humans and assembly tasks is reported under a dynamic simulation across levels, auxiliary variables and constants. Critical influencing factors for human reliability at work are identified and modelled.

This paper frames a model which summarizes the principal factors and relationships to incorporate human element, as per fatigue engagement, in the strategic and tactical decision making. A characterization of humans as per a dynamic interferer in system is presented. The findings are tested in an U-shaped assembly division of an international motor company. Dual-Resource Constrained sets are proposed. A Multi Agent model architecture, incorporating system dynamics modelling per humans, is implemented. ANOVA is executed for interaction analyses. Results designed optimal job sequence, rest break and task’s sequence, recovery rate and load characters as per enhanced system’s performances. Stable fatigued conditions are under complete recovery. Production rate in system is affected by fatigue that in turn increases with rise in switching rate. Chaotic market behavior prefers decentralized assignment of worker to task. Continuous switching suffers from learning attitude. Chaotic demand impairs fatigue across workforce reporting instable operator fatigue under different allocation rules. Under loudly physical tasks, random assignment is preferable for productivity outcomes. Autonomous control of workers at work considerably impairs fatigue under complete resting schema.