Joint quality control and preventive maintenance strategy for imperfect production processes

In this paper, we develop a joint quality control and preventive maintenance policy for a production system producing conforming and nonconforming units. The considered system consists of one machine which must supply another production line operating on a just-in-time basis. Each lot produced by the machine is subject to a quality control. According to the proportion l of nonconforming units observed and compared to a threshold value l _m , one decides to undertake or not maintenance actions on the system. In order to palliate perturbations caused by the stopping of the machine for preventive and corrective maintenance actions of random durations, a buffer stock h is built up to ensure the continuous supply of the subsequent production line. The proposed strategy is modelled using simulation and experimental design. This approach allows to generate a second order response surface allowing to easily determine the optimal rate, l_m^*{l_{m}^*}, of nonconforming units on the basis of which preventive maintenance actions should be performed, and the optimal size, h*, of the buffer stock to be built. These values minimize the total cost per time unit which includes the costs related to maintenance, quality and inventory.     

An artificial neural network (p, d, q) model for timeseries forecasting

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.     

Multiple robust H∞ controller design using the nonsmooth optimization method

This paper proposes a systematic technique to design multiple robust H_∞ controllers. The proposed technique achieves a desired robust performance objective, which is impossible to achieve with a single robust controller, by dividing the uncertainty set into several subsets and by designing a robust controller to each subset. To achieve this goal with a small number of divisions of the uncertainty set, an optimization problem is formulated. Since the cost function of this optimization problem is not a smooth function, a numerical nonsmooth optimization algorithm is proposed to solve this problem. This method avoids the use of Lyapunov variables, and therefore it leads to a moderate size optimization problem. A numerical example shows that the proposed multiple robust control method can improve the closed‐loop performance when a single robust controller cannot achieve satisfactory performance. Copyright © 2009 John Wiley & Sons, Ltd.     

The multi-agent programming contest from 2005–2010

The Multi-Agent Programming Contest is an annual international event on programming multi-agent systems: Teams of agents participate in a simulated cooperative scenario. It started in 2005 and is organised in 2010 for the sixth time. The contest is an attempt to stimulate research in the area of multi-agent system development and programming by (i) identifying key problems in the field and (ii) collecting suitable benchmarks that can serve as milestones for testing multi-agent programming languages, platforms and tools. This article provides a short history of the contest since it started and reports in more detail on the cows and cowboys scenario implemented for the 2008, 2009 and 2010 contest editions. We briefly discuss the underlying technological background and conclude with a critical discussion of the experiences and lessons learned.     

Adjustment of drag coefficient correlations in three dimensional CFD simulation of gas-solid bubbling fluidized bed

Fluidized beds have been widely used in power generation and in chemical, biochemical, and petroleum industries. 3D simulation of commercial scale fluidized beds has been computationally impractical due to the required memory and processor speeds. In this study, 3D Computational Fluid Dynamics simulation of a gas-solid bubbling fluidized bed is performed to investigate the effect of using different inter-phase drag models. The drag correlations of Richardon and Zaki, Wen-Yu, Gibilaro, Gidaspow, Syamlal-O’Brien, Arastoopour, RUC, Di Felice, Hill Koch Ladd, Zhang and Reese, and adjusted Syamlal are reviewed using a multiphase Eulerian-Eulerian model to simulate the momentum transfer between phases. Furthermore, a method has been proposed to adjust the Di Felice drag model in a three dimensional domain based on the experimental value of minimum fluidization velocity as a calibration point. Comparisons are made with both a 2D Cartesian simulation and experimental data. The experiments are performed on a Plexiglas rectangular fluidized bed consisting of spherical glass beads and ambient air as the gas phase. Comparisons were made based on solid volume fractions, expansion height, and pressure drop inside the fluidized bed at different superficial gas velocities. The results of the proposed drag model were found to agree well with experimental data. The effect of restitution coefficient on three dimensional prediction of bed height is also investigated and an optimum value of restitution coefficient for modeling fluidized beds in a bubbling regime has been proposed. Finally sensitivity analysis is performed on the grid interval size to obtain an optimum mesh size with the objective of accuracy and time efficiency.     

The voting analytic hierarchy process method for discriminating among efficient decision making units in data envelopment analysis

Making optimal use of available resources has always been of interest to humankind, and different approaches have been used in an attempt to make maximum use of existing resources. Limitations of capital, manpower, energy, etc., have led managers to seek ways for optimally using such resources. In fact, being informed of the performance of the units under the supervision of a manager is the most important task with regard to making sensible decisions for managing them. Data envelopment analysis (DEA) suggests an appropriate method for evaluating the efficiency of homogeneous units with multiple inputs and multiple outputs. DEA models classify decision making units (DMUs) into efficient and inefficient ones. However, in most cases, managers and researchers are interested in ranking the units and selecting the best DMU. Various scientific models have been proposed by researchers for ranking DMUs. Each of these models has some weakness(es), which makes it difficult to select the appropriate ranking model. This paper presents a method for ranking efficient DMUs by the voting analytic hierarchy process (VAHP). The paper reviews some ranking models in DEA and discusses their strengths and weaknesses. Then, we provide the method for ranking efficient DMUs by VAHP. Finally we give an example to illustrate our approach and then the new method is employed to rank efficient units in a real world problem.     

Stochastic chaos synchronization using Unscented Kalman-Bucy Filter and sliding mode control

This paper presents an algorithm for synchronizing two different chaotic systems by using a combination of Unscented Kalman-Bucy Filter (UKBF) and sliding mode controller. It is assumed that the drive chaotic system is perturbed by white noise and shows stochastic chaotic behavior. In addition the output of the system does not contain the whole state variables of the system, and it is also affected by some independent white noise. By combining the UKBF and the sliding mode control, a synchronizing control law is proposed. Simulation results show the ability of the proposed method in synchronizing chaotic systems in presence of noise.     

He’s homotopy perturbation method: A strongly promising method for solving non-linear systems of the mixed Volterra–Fredholm integral equations

In this paper, He’s homotopy perturbation method is applied to solve non-linear systems of mixed Volterra–Fredholm integral equations. Two examples are presented to illustrate the ability of the method. Also comparisons are made between the Adomian decomposition method and the homotopy perturbation method. The results reveal that He’s homotopy perturbation method is very effective and simple and in these examples leads to the exact solutions.     

Attention control with reinforcement learning for face recognition under partial occlusion

In this paper a new method for handling occlusion in face recognition is presented. In this method the faces are partitioned into blocks and a sequential recognition structure is developed. Then, a spatial attention control strategy over the blocks is learned using reinforcement learning. The outcome of this learning is a sorted list of blocks according to their average importance in the face recognition task. In the recall mode, the sorted blocks are employed sequentially until a confident decision is made. Obtained results of various experiments on the AR face database demonstrate the superior performance of proposed method as compared with that of the holistic approach in the recognition of occluded faces.