This paper discusses the application of discrete event simulation (DES) for modeling the operations of an emergency department (ED). The model was developed to help the ED managers understand the behavior of the system with regard to the hidden causes of excessive waiting times. It served as a tool for assessing the impact of major departmental resources on key performance indicators, and was also used as a cost effective method for testing various what-if scenarios for possible system improvement. The study greatly enhanced managers' understanding of the system and how patient flow is influenced by process changes and resource availability. The results of this work also helped managers to either reverse or modify some proposed changes to the system that were previously being considered. The results also show a possible reduction of more than 20% in patients' waiting times

In this paper, a novel outlier-resistant filtering problem is concerned for a class of networked systems with dead-zone-like censoring under the weighted try-once-discard protocol (WTODP). To describe the phenomenon of dead-zone-like censoring, the sensor output is characterized by the Tobit model in which the censored region is restrained by specified left- and right-censoring thresholds. The WTODP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. In the case of the measurement outliers, a saturation function is employed in the Tobit Kalman filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the approach of the matrix inequality, an upper bound is first obtained on the filtering error covariance where the gain matrix of the Tobit Kalman filter is carefully designed to minimize the obtained upper bound. Moreover, the exponential boundedness of the filtering error is analyzed in the mean square sense. Finally, the effectiveness of the proposed outlier-resistant filtering algorithm is verified by three practical examples.

Purpose: Customer relationship management (CRM) strategies rely heavily on the importance and performance of the attributes that define a service. The aim of this paper is first to investigate the asymmetric relationship between performance of service attributes and customer satisfaction, and second, through a case study in the mobile telecommunication industry to prove that the importance of a service attribute is a function of the performance of that attribute.Design/methodology/approach: An empirical study using questionnaires with a focus on service enquiring about the performance of service key attributes and overall customer satisfaction was conducted. The data were fed into the Kano customer satisfaction model and the importance‐performance analysis (IPA) method for analysis and comparison.Findings: The results indicate that there is a dynamic relationship between service attributes and overall customer satisfaction. Service attributes have a different impact on customer satisfaction regardless of their classification. The importance of service attributes can be derived from their performance and this can be proved in the Mobile Telecommunication sector. Also this research concludes that the major weaknesses in the Mobile Telecommunication industry that cause the highest customer dissatisfaction are the range of phones, the accuracy of billing and payment, and the service plans, whereas the major strengths as a source of customer satisfaction are customer service quality, value for money and network performance.Research limitations/implications: The Kano model of customer satisfaction needs to be extended to other customer behaviour variables such as customer retention (e.g. purchase intention) and customer loyalty (e.g. word‐of‐mouth, feedback) for improved decision analysis. The paper does not include customer retention and loyalty factors.Practical implications: The methodology employed here can be easily applied by marketers for evaluating customer behaviours and service quality performance for improved decision making and resource allocation.Originality/value: There is little evidence that extensive work has been dedicated to studying the relationship between service attributes and customer satisfaction through Kano's model. The paper specifically investigates the applicability of the model and the key factors in the mobile telecommunication industry.

This paper is concerned with the Tobit Kalman filtering problem for a class of discrete time-varying systems subject to censored observations, integral measurements and probabilistic sensor failures under the Round-Robin protocol (RRP). The censored observations are characterized by the Tobit observation model, the integral measurements are described as functions of system states over a certain time interval required for data acquisition, and the sensor failures are governed by a set of uncorrelated random variables. The RRP is employed to decide the transmission sequence of sensors in order to alleviate undesirable data collisions. By resorting to the augmentation technique and the orthogonality projection principle, a protocol-based Tobit Kalman filter (TKF) is developed with the coexistence of integral measurements and sensor failures that lead to a couple of augmentation-induced terms. Moreover, the performance of the proposed filter is analyzed through examining the statistical property of the error covariance of the state estimation. Further analysis shows the existence of self-propagating upper and lower bounds on the estimation error covariance. A case study on ballistic roll rate estimation is presented to illustrate the efficacy of the developed filter.

Wafer bin maps contain vital information that helps semiconductor manufacturers to identify the root causes and defect pattern failures in wafers. Conventional manual inspection techniques in inspecting these failures are labour intensive and cause prolonged production cycle time. Therefore, automatic inspection techniques can solve this problem. This paper proposes a deep learning approach based on deep convolutional generative adversarial network (DCGAN) and a new Capsule Network (WaferCaps). DCGAN was used to upsample the original dataset and therefore increase the data used for training and balance the classes at the same time. While WaferCaps was proposed to classify the defect patterns according to eight classes. The performance of our proposed DCGAN and WaferCaps was compared with different deep learning models such as the original Capsule Network (CapsNet), CNN, and MLP. In all of our experiment, WM-811K dataset was used for the data upsampling and training. The proposed approach has shown an effective performance in generating new synthetic data and classify them with training accuracy of 99.59%, validation accuracy of 97.53% and test accuracy of 91.4%.