Search results

Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service experience, in order to attract more customers and achieve higher customer satisfaction. Although customer service and satisfaction have been discussed by other researchers, to the knowledge, there has been no dynamic and intelligent way to model and optimize customer support systems for product and service providers. The purpose of this paper is to develop a modeling method for customer support optimization.

In this study, a system dynamics (SD) model has been formulated to investigate the dynamic characteristics of customer support in an IT service provider. The proposed simulation model considers the dynamic, non-linear, and asymmetric interactions among its components, and allows study of the behavior of the customer support system under controlled conditions. Furthermore, a particle swarm optimization method was developed to investigate the proper combination of parameters and strategy development of the support center.

This paper proposes a novel modeling, simulation, and optimization approach for complex customer support systems of information and communications technology (ICT) service providers. This method helps managers improve their customer support systems. Moreover, the simulation results of the case study show that ICT service providers can gain benefit by managing their customer service dynamically over time using the proposed artificial intelligent multi-parameter modeling and optimization method.

The proposed holistic modeling approach and multi-parameter optimization method will greatly help managers and researchers understand the factors influencing customer support. Moreover, it facilitates the process of making new improvement strategies based on provided insights.

The paper shows how SD simulation and multi-parameter optimization can provide insights into the field of customer support. However, the existing literature lacks a holistic view of these kinds of simulation systems, as well as a multi-parameter optimization method for SD methodology.

The purpose of this paper is to propose a pressure-, temperature- and acceleration-sensitive structure-integrated inductor-capacitor (LC) resonant ceramic sensor to fulfill the measurement of multi-parameters, such as the measurement of pressure, temperature and acceleration, simultaneously in automotive, aerospace and aeronautics industries.

The ceramic-based multi-parameter sensor was composed of three LC tanks, which have their resonant frequencies sensitive to pressure, temperature and acceleration separately. Two aspects from the specific sensitive structure design to the multiple signals reading technology are considered in designing the multi-parameter ceramic sensor. Theoretical analysis and ANSYS simulation are used in designing the sensitive structure, and MATLAB simulation and experiment are conducted to verify the feasibility of non-coverage of multi-readout signals.

It is found that if the parameters of sensitive structure and layout of the LC tanks integrated into the sensor are proper, the implementation of a multi-parameter sensor could be feasible.

The ceramic sensor proposed in the paper can measure pressure, temperature and acceleration simultaneously in harsh environments.

The paper creatively proposes a pressure-, temperature- and acceleration-sensitive structure-integrated LC resonant ceramic sensor for harsh environments and verifies the feasibility of the sensor from sensitive structure design to multiple-signal reading.

Low power devices with switched-mode power supply represent harmonic generating apparatus in widespread use nowadays. The influence of personal computers (PCs), which affect the supply voltage, is considered. Harmonic level due to simultaneous PC operation is quantified by the total harmonic distortion of input current (THD _I ). The purpose of this paper is to propose a multi-parameter mathematical model for the THD _I calculation. The model is convenient for practical engineering application.

The model is derived using the measured and simulated data. The model coefficients are obtained in the least squares sense.

Mathematical modeling of THD _I is the least expensive and the most convenient solution for engineering application. The models proposed in the literature have many drawbacks, which motivated the authors to develop a more comprehensive solution. Grid stiffness, capacitance of PC power supply unit and PC cluster size represent the major parameters that affect THD _I , and as such they are taken into account in the proposed model. The influence of other existing parameters from both line and load side is also discussed and the reasons for their omitting from the model are explained. The model considers various PC configurations within the cluster and it enables the THD _I calculation for an arbitrary PC cluster size.

Due to its comprehensiveness and mathematical simplicity, the model is suitable for practical use, and its accuracy is verified through conducted measurements presented in the paper.

The proposed model is more comprehensive than the existing ones, and it overcomes their shortcomings. The THD _I calculation is simplified to the level of applying basic arithmetic operations only, without jeopardizing the accuracy. The validity of the model is supported by additional measurements carried out in sites characterized by grid conditions quite different from that used for model developing.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

The purpose of this paper is to review the available reduced order modeling approaches in the literature for predicting the flow and specially temperature fields inside data centers in terms of the involved design parameters.

This paper begins with a motivation for flow/thermal modeling needs for designing an energy‐efficient thermal management system in data centers. Recent studies on air velocity and temperature field simulations in data centers through computational fluid dynamics/heat transfer (CFD/HT) are reviewed. Meta‐modeling and reduced order modeling are tools to generate accurate and rapid surrogate models for a complex system. These tools, with a focus on low‐dimensional models of turbulent flows are reviewed. Reduced order modeling techniques based on turbulent coherent structures identification, in particular the proper orthogonal decomposition (POD) are explained and reviewed in more details. Then, the available approaches for rapid thermal modeling of data centers are reviewed. Finally, recent studies on generating POD‐based reduced order thermal models of data centers are reviewed and representative results are presented and compared for a case study.

It is concluded that low‐dimensional models are needed in order to predict the multi‐parameter dependent thermal behavior of data centers accurately and rapidly for design and control purposes. POD‐based techniques have shown great approximation for multi‐parameter thermal modeling of data centers. It is believed that wavelet‐based techniques due to the their ability to separate between coherent and incoherent structures – something that POD cannot do – can be considered as new promising tools for reduced order thermal modeling of complex electronic systems such as data centers

The paper reviews different numerical methods and provides the reader with some insight for reduced order thermal modeling of complex convective systems such as data centers.

The aim of the paper is to analyze the impact of cooperativeness of managers who occupy central positions in interaction networks on the performance and stress levels of a whole organization.

To explore this relationship, a multi-parameter agent-based model is proposed which implements the prisoner’s dilemma game approach on a scale-free network in the NetLogo environment. A description of the socioeconomic aspects and the key concepts implemented in the model is provided. Stability and correctness have been tested through a series of validation experiments, including sensitivity analysis. The source code is available for further exploration and testing.

The simulations revealed that improving the stress resistance of all employees moderately increases organizational performance. Analyzing managers’ roles showed that increasing only the stress resistance of managers does not account for significantly higher overall performance. However, a substantial increase in organizational performance and a decrease in stress levels are achieved when managers are unconditionally cooperative. This effect is stronger for the lowered stress resistance of employees. Therefore, the willingness of managers to cooperate under all circumstances can be a key factor in achieving better performance and building a more pleasant, stress-free working environment.

This paper aims to present a model for analyzing cooperation, specifically in the organizational context, extending the prisoner’s dilemma with novel concepts and mechanisms. Although the results confirm the existing theories about the importance of central nodes in complex networks, they also provide further details on how the cooperative behavior of central nodes (i.e. the managers) might benefit the organization.

This paper aims to mainly discuss how to suppress the disturbances accurately and effectively in the wind energy conversion system (WECS) of the direct drive surface mount permanent magnet synchronous generator (SPMSG).

The disturbances in wind energy conversion system have seriously negative influence on the maximum power tracking performance. Therefore, a model predictive control (MPC) method of model compensation active disturbance rejection control (ADRC) strategy in parallel connection is designed, which optimizes the speed tracking performance compared with the existing control strategy of MPC and ADRC in series connection. Based on the traditional ADRC, a multi parameter model compensation ADRC strategy is added to better estimate the disturbances. At the same time, a torque feedback strategy is added to compensate the disturbances caused by load torque and further optimize the speed loop tracking performance.

The simulation results show that the designed control method has advantages than the traditional control method in compensating the disturbances and tracking the maximum power more effectively.

The simulation results show that the designed control method is superior to the traditional proportional control method, which can better compensate the internal and external disturbances and track the maximum power more effectively.

Purpose: This chapter synthesises a variety of findings on the topic of aggressive driving and delivers a suite of strategies for moderating such behaviours. Examples and formal definitions of aggressive driving acts are given, along with specific techniques for reducing excessive speed and other aggressive behaviours.

Methodology: Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings: Speeding is the most common type of aggressive driving, and speeding-related crashes represent a high share of traffic deaths. Speeding relates to many factors, including public attitudes, personal behaviours, vehicle performance capabilities, roadway design attributes, laws and policies. Anonymity, while encased in a vehicle, and driver frustration, due to roadway congestion or other issues, contribute to aggressive driving.

Research implications: More observational data are needed to quantify the effects of the contributing factors on aggressive driving.

Practical implications: Driver frustration, intoxication and stress can lead to serious crashes and other traffic problems. They can be addressed, to some extent, through practical enforcement, design decisions and education campaigns.

Engineering components/structures with geometric discontinuities normally bear complex and variable loads, which lead to a multiaxial and random/variable amplitude stress/strain state. Hence, this study aims how to effectively evaluate the multiaxial random/variable amplitude fatigue life.

Recent studies on critical plane method under multiaxial random/variable amplitude loading are reviewed, and the computational framework is clearly presented in this paper.

Some basic concepts and latest achievements in multiaxial random/variable amplitude fatigue analysis are introduced. This review summarizes the research status of four main aspects of multiaxial fatigue under random/variable amplitude loadings, namely multiaxial fatigue criterion, method for critical plane determination, cycle counting method and damage accumulation criterion. Particularly, the latest achievements of multiaxial random/variable amplitude fatigue using critical plane methods are classified and highlighted.

This review attempts to provide references for further research on multiaxial random/variable amplitude fatigue and to promote the development of multiaxial fatigue from experimental research to practical engineering application.