Model Optimization Method Research Articles

Customer support optimization using system dynamics: a multi-parameter approach

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Originality/value – 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.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

A Novel Train Rescheduling Approach in Double-Track Railways: Optimization Model and Solution Method Based on Simulated Annealing Algorithm

This paper presents a novel approach to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach restricts the effects of an incident to a specific time, based on which the trains are divided into rescheduled and unchanged ones, so that the latter retain their original time-table after the incident. The main contribution of this approach is the simultaneous consideration of three rescheduling policies: cancelling, delaying and re-ordering. A mixed-integer optimization model is developed to find optimal conflict-free time-table compatible with the proposed approach. The objective function minimizes two cost parts: the cost of deviation from the primary time-table and the cost of train cancellation. The model is solved by CPLEX 11 software which automatically generates the optimal solution of a problem. Also, a meta-heuristic solution method based on simulated annealing algorithm is proposed for tackling the large-scale problems. The results of an experimental analysis on two double-track railways of the Iranian network show an appropriate capability of the model and solution method for handling the simultaneous train rescheduling. The results indicate that the proposed solution method can provide good solutions in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software.

Order reduction of MIMO ARX systems using Laguerre bases

In this article, we explore the use of the Laguerre functions for the estimation of multivariable autoregressive with exogenous (ARX) input model. Each polynomial function of the MIMO ARX model associated to the inputs and to the outputs is expanded on independent Laguerre orthonormal bases. The resulting model is entitled MIMO ARX-Laguerre model. The optimal approximation of which is ensured once the poles characterising each Laguerre orthonormal basis are set to their optimal values. In this paper, a minimal recursive representation realisations for MIMO discrete-time linear systems are derived using Laguerre functions. Further we propose, from input/output measurements, an iterative optimisation algorithm for the free parameters (Laguerre poles). Simulation results show the effectiveness of the proposed optimal modelling method.

Toward a unifying strategy for the structure-based prediction of toxicological endpoints.

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n=660, n=1114) and three qualitative (n=447, n=905, n=1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.

Optimal expansions of multivariable ARX processes on Laguerre bases via the Newton–Raphson method

SummaryIn this article, an optimal linear MIMO system approximation by using discrete‐time MIMO autoregressive with exogenous input (ARX) model is proposed. Each polynomial function of the MIMO ARX model associated with the inputs and with the outputs is expanded on independent Laguerre orthonormal basis. The resulting model is entitled MIMO ARX–Laguerre model. The optimal approximation of which is ensured once the poles characterizing each Laguerre orthonormal basis are set to their optimal values. In this paper, a new method to estimate, from input/output measurements, the optimal Laguerre poles of the MIMO ARX–Laguerre model is proposed. The method consists in applying the Newton–Raphson's iterative technique in which the gradient and the Hessian are expressed analytically. The proposed algorithm is tested on a numerical example and on a benchmark system. Simulation results show the effectiveness of the proposed optimal modeling method. Copyright © 2015 John Wiley & Sons, Ltd.

Optimized power simulation of AlGaN/GaN HEMT for continuous wave and pulse applications**Project supported by the National Natural Science Foundation of China (No. 61204086).

An optimized modeling method of 8 × 100 μm AlGaN/GaN-based high electron mobility transistor (HEMT) for accurate continuous wave (CW) and pulsed power simulations is proposed. Since the self-heating effect can occur during the continuous operation, the power gain from the continuous operation significantly decreases when compared to a pulsed power operation. This paper extracts power performances of different device models from different quiescent biases of pulsed current–voltage (I–V) measurements and compared them in order to determine the most suitable device model for CW and pulse RF microwave power amplifier design. The simulated output power and gain results of the models at Vgs = −3.5 V, Vds = 30 V with a frequency of 9.6 GHz are presented.

CO<sub>2</sub> 배출량 평가를 위한 BIM 모델 구축 최적화 방안

The World Meteorological Organization and United Nations Environment Programme (UNEP) have officially declared that carbon dioxide is the main cause in global warming. Architectural structures are primarily responsible for emissions, and in order to reduce their emissions, it is essential to introduce rapid and accurate calculation methods during the design phase of construction. The emissions of building components can be calculated by applying the correction factors derived in the design phase directly to standardized data based on building information modeling (BIM). With the objective to support energy-efficient building design, this study had the goal of investigating the accuracy of BIM-based quantity calculation, thereby focusing on a BIM library and modeling, and presenting the results as user guidelines to allow BIM data to be used for the rapid and objective analysis and verification of emissions. To this end, we studied the applicability of BIM-based quantity calculation on the basis of the information for major materials presented in previous studies related to emission evaluation. Additionally, an optimal BIM modeling method is presented based on the results of an analytical comparison of the calculated quantities after applying the BIM data generation methods for different combinations of architectural components to a multi-family residence. The results of this study are expected to be of great help in designing low-carbon buildings by predicting the emissions of building components in a BIM-based design phase.

A general modeling method for opportunistic maintenance modeling of multi-unit systems

This paper presents a deterioration state space partition method for opportunistic maintenance modeling of multi-unit systems. The method represents common characteristics of opportunistic maintenance models based on different maintenance strategies. All possible maintenance groups of general multi-unit systems with a known number of non-identical units at each maintenance decision time and their corresponding probabilities are deduced using the presented approach. Further, a general representation of the stationary law of the system deterioration and its numerical solution is developed. Numerical experiments verify the correctness and validity of the state space partition method and the numerical solution of the stationary probability density. The proposed method is applicable to both single-unit and multi-unit systems, and it provides a new generalized modeling method for maintenance optimization of multi-unit systems.

Novel Optimization Model and Efficient Solution Method for Integrating Dynamic Optimization with Process Operations of Continuous Manufacturing Processes

In this paper, we propose a novel mathematical model for the integrated optimization for production planning, scheduling, and dynamic optimization of continuous manufacturing processes. With a novel approach to estimate the inventory cost, the integrated problem is first formulated as a mixed-integer dynamic optimization (MIDO) problem, which is then reformulated into a large scale mixed-integer nonlinear program (MINLP). By using metamodeling to characterize the detailed linking information between different decision layers, we develop an efficient solution method to decompose the integrated MINLP into a mixed-integer linear program (MILP) for an integrated planning and scheduling problem with flexible recipes, and a set of dynamic optimization problems for generating metamodels. We further apply a bilevel decomposition algorithm to improve the computational efficiency of solving the integrated planning and scheduling problem with flexible recipes. The proposed models and algorithms are demonstrated through case studies of methyl methacrylate (MMA) polymerization processes. The results show that the proposed methods reduce the computational time by more than 2 orders of magnitude compared with the approach of solving the entire integrated optimization problem directly.

Alternative Splice Variants in Gene Expression Values in Patients with Marfan’s Syndrome

Background: Alternative splicing of messenger RNAs provides cells with the opportunity to create protein isoforms of a multitude of functions from a single gene by excluding or including exons during post-transcriptional processing. Reconstructing the contribution of these splice variants on the total amount of gene expression remains difficult. Methods: We introduced a probabilistic formulation of the alternative splicing reconstruction problem using a finite mixture model, and provide a solution based on the maximum likelihood principle. Our model is based on the assumption that the expected expression level of exons in a particular splice variant is the same for all exons in that variant but allows for measurement error. In this algorithm the expression in a patient can be written as a weighted sum of the number of splice variant mixture multivariate Gaussian densities. We estimated the model parameter by maximizing the total likelihood using a Nelder and Mead optimization algorithm in R. To evaluate our algorithm we compared the AIC/BIC values of six models: Established optimal normal mixture modeling method, all exons are equally transcripted, the currently known splice variants, all possible splice variants, the known variants aided with the high prevalent variants of the all possible variants model, and manually selected splice variants. Results: We applied the models to three genes (SLC2A10, TGFβR2 and FBN1), with 25, 29 and 265 possible splice variants, associated with Marfan’s syndrome in gene/exon expression data of 63 patients with Marfan’s syndrome. The models with the known splice variants aided with the high prevalent splice variants from the all possible splice variants had the best AI C/BI C values for all three genes. In SLC2A10 and FBN1 there was one, in TGFβR2 two predominant splice variants. Conclusion: We found four possible new splice variants in three genes associ- ated with Marfan’s syndrome.

X/XO or H2O2 induced IPEC-J2 cell as a new in vitro model for studying apoptosis in post-weaning piglets.

We previously demonstrated that intestinal epithelial cell apoptosis in weaned piglets is much more serious than that observed in sucking piglets and is related to oxidative stress during weaning. It is difficult to study the apoptosis mechanisms only using in vivo methods because of the limit of existing research technology. An in vitro cellular system is required for piglet intestinal epithelial cell apoptosis research. In this study, a non-tumorigenic epithelial cell line, IPEC-J2 cells, was employed as a cell model. Hydrogen peroxide and xanthine/xanthine oxidase (X/XO) were both used and compared for apoptosis modeling. The concentrations of hydrogen peroxide and XO were selected and verified using cell viability analysis, the comet assay and flow cytometry. Intracellular ROS were measured using fluorescent probes. Additionally, the expression levels of the apoptosis-related genes Fas, Bcl-2, P53, Caspase 3, Caspase 8, and Caspase 9 were analyzed using quantitative RT-PCR. The results indicated the optimal modeling method is a final concentration of 0.5mM H2O2 incubated with IPEC-J2 cells for 1h at 37°C in 5% CO2 for hydrogen peroxide-induced apoptosis modeling, and a final concentration of 250μM X/50 U/L XO incubated with IPEC-J2 cells for 6h at 37°C in 5% CO2 for X/XO-induced apoptosis modeling. For the apoptotic pathway, the X/XO modeling method is more similar to 21 days weaning piglets. Therefore, we suggest that X/XO modeling with IPEC-J2 cells be used as an in vitro cell culture model for weaning piglet intestinal epithelial cell apoptosis.

Modeling of differential diagnosis classifiers of pathological states of the cardiovascular system

The results of creating differential diagnosis classifiers of pathological states of the cardiovascular system are given in the paper. This task allows to solve the problem of creating non-invasive diagnosis method that allows to use repeated measurements, obtained in a state of unrest. A feature of this formulation is the classification of complex objects, each of which can overlap with other objects. It is proposed to use the convolution of objects, obtained through the application of the modified combinatorial group method of data handling. Classification problem is solved by the one-against-all approach, i.e. a set of models, opposing each state to all other is modeled. The main purpose of the work was selecting the optimal modeling method, which allows to get the maximum accuracy on the training and test samples. As a result of the analysis, it was proved that the group method of data handling, which has shown the classification accuracy of over 90 % at the separation of all diagnoses is optimum for such problems.

Bi-Level Design Optimization of the Hatch Door Mechanism

Optimization model and solution method are developed in the present paper to improve the design of the hatch door mechanism of a regional airliner. The real motion trajectory of the hatch door normally has deviations from the ideal trajectory due to existence of machining errors. In order to decrease such deviations, a bi-level optimization model is developed for the design of the size tolerance limits of the key parts. The design objective is to minimize the extremum deviation of the trajectory of the objective point of the hatch door. The extremum deviation is obtained by solution of the inner-level size optimization problem for the fixed size tolerance limits. The optimization models for motion control of the hatch door mechanism are solved by the response surface method.

Decomposition method of complex optimization model based on global sensitivity analysis

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.

Determination of optimal operating factors via modeling for livestock wastewater treatment: Comparison of simulated and experimental data

This study evaluated the optimized operational conditions of a modified four-stage Bardenpho process via modeling for livestock wastewater treatment. The objective is to determine whether the effluent Korean water quality standards can be met using the modified process and ozone/activated carbon reactors. The parameters related to microbial respiration (i.e., YH, YA, μH, bH, and iXB) and effluent concentrations (i.e., fp, ηg, Kh, and ηh) in GPS-X based on ASM1 were calibrated according to the influent characteristics. Consequently, the differences in the COD and TN concentrations of the calibrated and experimental results were 29.58 and 19.15 mg/L, respectively. A modeling method based on multiple RSM was used to determine the optimized operational conditions, resulting in a DO concentration of 6.6 mg/L in each aerobic reactor, an internal recycling rate of 4.9Qin, and a sludge recycling rate of 1.4Qin. Although the COD and TN concentrations during actual operation using the optimized conditions were slightly different than those predicted by GPS-X, the differences were not significant; therefore, the effluent Korean water quality standards were satisfied. With more specific criteria that reduce the differences between the predicted and actual concentrations, the modeling method for operational optimization would be sufficient under any circumstances.

Level Set-Based Topology Optimization of Hinge-Free Compliant Mechanisms Using a Two-Step Elastic Modeling Method

This paper presents a two-step elastic modeling (TsEM) method for the topology optimization of compliant mechanisms aimed at eliminating de facto hinges. Based on the TsEM method, an alternative formulation is developed and incorporated with the level set method. An efficient algorithm is developed to solve the level set-based optimization problem for improving the computational efficiency. Two widely studied numerical examples are performed to demonstrate the validity of the proposed method. The proposed formulation can prevent hinges from occurring in the resulting mechanisms. Further, the proposed optimization algorithm can yield fewer design iterations and thus it can improve the overall computational efficiency.

Multi-Objective Optimization Design of Steel Frames Based on Multiple Attribute Decision-Making Method

This paper describes in detail a multi-objective optimization strategy and decision-making method in the process of steel frame optimization design. A step-by-step analysis process integrating optimization algorithm and model analysis is proposed to solve the present problem. A multi-objective algorithm method using fast Non-dominated Sorting Genetic Algorithm is employed to obtain the Pareto-optimal solution set through an evolutionary optimization process. A high-level multiple attribute decision-making method based on intuitionistic fuzzy set theory is adopted to rank these solutions from the best to worst, and to determine the best solution. An example is used to demonstrate the proposed optimization model and decision-making method.

Study on Flood Control Pre-Discharge Dispatching Scheme of Reservoir Based on Fuzzy Optimum Selection

Flood control dispatching of reservoir belongs to the ex-ante decision. This paper established the flood pre-discharge dispatching multi-objective risk index system and fuzzy optimization model method, taking Long Men reservoir as an example, to study the design flood pre-discharge scheme for different frequency, different frequency analysis of design flood pre-discharge flow and pre-discharge time, provide the reservoir flood pre-discharge scheme of different frequency flood and provide reliable basis for reservoir flood control pre-discharge dispatching.

Measurement and modeling of Bidirectional Reflectance Distribution Function (BRDF) on material surface

Measurement and modeling of Bidirectional Reflectance Distribution Function (BRDF) is made to describe optical scattering characteristics of the material surface. A measurement device of BRDF with the ability to real-time measurement is developed by the micro fiber spectrometer and three-dimensional turntables. The measured spectral range is from visible to near infrared with 2.4nm wavelength resolution. The measured angular range is 0–360° in azimuth angle and 0–85° in zenith angle with 0.01° angle resolution. BRDF measurement on tinfoil and ceramic tile is made and the measurement error is no more than 6.05%. A six-parameter model of BRDF is built according to material surface characteristics. Based on the simulated annealing algorithm, the model parameters of BRDF are obtained through fitting processing of measured data and the maximal fitting error is 9.94%. The results show that the measuring accuracy of BRDF depends on the precision of measuring platform, the sensitivity of measuring instrument and the stability of illuminating light source, and the modeling accuracy of BRDF is closely related to the measured data and optimization modeling method. The measurement and modeling of BRDF may benefit future optical remote sensing, computer graphics and environmental surveillance.