Sensitivity Analysis Of Parameters Research Articles

Effect of twisting of intravitreal injections on ocular bio-mechanics: a novel insight to ocular surgery.

Although intravitreal (IVT) injections provide several advantages in treating posterior segment eye diseases, several associated challenges remain. The current study uses the finite element method (FEM) to highlight the effect of IVT needle rotation along the insertion axis on the reaction forces and deformation inside the eye. A comparison of the reaction forces at the eye's key locations has been made with and without rotation. In addition, a sensitivity analysis of various parameters, such as the needle's angular speed, insertion location, angle, gauge, shape, and intraocular pressure (IOP), has been carried out to delineate the individual parameter's effect on reaction forces during rotation. Results demonstrate that twisting the needle significantly reduces the reaction forces at the penetration location and throughout the needle travel length, resulting in quicker penetration. Moreover, ocular biomechanics are influenced by needle insertion location, angle, shape, size, and IOP. The reaction forces incurred by the patient may be reduced by using a bevel needle of the higher gauge when inserted close to the normal of the local scleral surface toward the orra serrata within the Pars Plana region. Results obtained from the current study can deepen the understanding of the twisting needle's interaction with the ocular tissue.

The effect of curative and preventive optimal control measures on a fractional order plant disease model

In this paper, we present a novel mathematical model of fractional order for epidemic dynamics in plants, this fractional order model (FOM) in the sense of Caputo derivatives governing fractional differential equations (FDEs), introducing modified parameters to coincide both sides dimensions for FOM that means enhancing its accuracy in representing the real-life scenarios to control the spread of the epidemic. Our contributions include a comprehensive qualitative analysis of the proposed FOM, such as proving the existence and uniqueness of the projected solution by transforming our problem into a fixed point problem and applying a Banach contraction principle and Schauder’s fixed-point theorem. The positivity and boundedness of this solution are also demonstrated. We precisely evaluate all equilibrium points (EPs), examine their local and global stability by using Routh–Hurwitz conditions and fractional LaSalle’s invariance principle (LIP), respectively and shed light on the dynamic behavior of the FOM. Furthermore, we discuss the sensitivity analysis for parameters of the control reproduction number (CRN), with insightful plots to illustrate the model’s response to parameter changes (replanting and roguing parameters). From this foundation, we formulate a fractional optimal control problem (FOCP) by incorporating preventive u1 and curative u2 controls to effectively eliminate the propagation of the epidemic in plants. The fractional necessary optimality conditions (FNOCs) are derived attribution to Pontryagin’s maximum principle (PMP). We employ the forward–backward sweep method (FBSM) based on fractional Euler method (FEM) in order to facilitate control implementation and show the different suggested strategies. By enhancing this method, optimal control efforts are projected onto the FOM and yield three various strategies to impact the epidemic’s trajectory. For each strategy, we explain how the presence (i.e. with control cases) and absence (without control cases) of the proposed controls affected the susceptible, protected and infected plants. Some attractive figures with various values of the weight factor and fractional order α for the CRN are also presented, allowing a visual assessment of their impact on the dynamics of the epidemic in plants. In addition, we calculate the objective function for controlled and uncontrolled cases to provide a quantitative measure of its effectiveness in containing the outbreak in the plants. The results gained from our analyses and simulations provide valuable guidance for the management and elimination of epidemics in plants by offering various scenarios to implement the proposed controls.

Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site

Abstract. Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm-2d-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately.

Performance analysis and capacity configuration of building energy system integrated with PV/T technology under different operation strategies

Implementing suitable operation strategies significantly enhances the overall performance of the energy network of solar buildings. Previous studies predominantly concentrated on dispatching energy from standalone photovoltaic or photothermal modules within buildings. The photovoltaic/thermal (PV/T) modules have garnered attention due to their higher energy efficiency. However, the investigations targeting the operation strategies of PV/T modules in building remain scarce. To fill this research gap, this study scrutinized the maximum self-consumption (MSC), time-of-use (TOU), and optimization-based (OPT) strategies within a building energy system integrated with PV/T modules. The investigation encompassed an analysis of overall performance, parameter sensitivity, and optimal energy storage capacity. The main results were: (1) The OPT strategy displayed superior flexibility and lower operation costs compared to other strategies but exhibited lower energy utilization efficiency and greater grid impact. (2) The MSC strategy demonstrated lower sensitivity concerning battery power and charge states when contrasted with other strategies. (3) The optimal capacities for battery and water tank configurations for the MSC, TOU, and OPT strategies were identified as 4 kWh/200 L, 2.5 kWh/200 L, and 4.5 kWh/100 L, respectively. This research aims to provide reference and insights for planning the integration of PV/T modules into building energy systems.

Sensitivity analysis of design parameters and their interactions and performance prediction of a novel twin turbine wave energy converter

In addressing the challenges associated with accurately predicting the efficiency of wave energy converters, particularly due to the intricate nature of certain parameters resistant to straightforward analytical derivation, this paper proposes a novel semi-parameter analytical dynamic model. This model not only effectively overcomes the inherent challenges but also undergoes rigorous validation through experimental testing. Building upon this foundation, we employ response surface methodology to develop a predictive framework for evaluating the energy harvesting efficiency of our innovative twin-turbine wave energy converter. The objectives of this predictive model are twofold: firstly, to eliminate the need for intricate numerical integration of dynamic differential equations governing the converter's behaviour; secondly, to distil the energy harvesting performance prediction model from intricate design parameters. This streamlined model facilitates an incisive analysis of design parameter sensitivities and interactive influences. Validation involves analysis of variance (ANOVA) and comparison against the semi-parameter analytical dynamic model. The innovative aspect of our wave energy converter lies in the creative use of twin turbines connected through a pulley-driven transmission system. This unique configuration significantly enhances generator rotational speed, enabling efficient power take-off mechanism placement within the buoy. The pulley-driven transmission system not only allows easy adjustment of the transmission ratio but also heightens generator efficiency by aligning its operational speed within the desired rated speed range. Additionally, the transmission system adeptly absorbs load fluctuations, impacts and vibrations from oceanic waves, enhancing device robustness and longevity. The streamlined design also facilitates maintenance access, affirming practicality.

Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer.

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.

From parameters to policies: sensitivity analysis and fiscal and monetary reactions

PurposeThis paper seeks to explore the sensitivity of these parameters and their impact on fiscal policy outcomes. We use the existing literature to establish possible ranges for each parameter, and we examine how changes within these ranges can alter the outcomes of fiscal policy. In this way, we aim to highlight the importance of these parameters in the formulation and evaluation of fiscal policy.Design/methodology/approachThe role of fiscal policy, its effects and multipliers continues to be a subject of intense debate in macroeconomics. Despite adopting a New Keynesian approach within a macroeconomic model, the reactions of macroeconomic variables to fiscal shocks can vary across different contexts and theoretical frameworks. This paper aims to investigate these diverse reactions by conducting a sensitivity analysis of parameters. Specifically, the study examines how key variables respond to fiscal shocks under different parameter settings. By analyzing the behavioral dynamics of these variables, this research contributes to the ongoing discussion on fiscal policy. The findings offer valuable insights to enrich the understanding of the complex relationship between fiscal shocks and macroeconomic outcomes, thus facilitating informed policy debates.FindingsThis paper aims to investigate key elements of New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models. The focus is on the calibration of parameters and their impact on macroeconomic variables, such as output and inflation. The study also examines how different parameter settings affect the response of monetary policy to fiscal measures. In conclusion, this study has relied on theoretical exploration and a comprehensive review of existing literature. The parameters and their relationships have been analyzed within a robust theoretical framework, offering valuable insights for further research on how these factors influence model forecasts and inform policy recommendations derived from New Keynesian DSGE models. Moving forward, it is recommended that future work includes empirical analyses to test the reliability and effectiveness of parameter calibrations in real-world conditions. This will contribute to enhancing the accuracy and relevance of DSGE models for economic policy decision-making.Originality/valueThis study is motivated by the aim to provide a deeper understanding of the roles macroeconomic model parameters play concerning responses to expansionary fiscal policies and the subsequent reactions of monetary authorities. Comprehensive reviews that encompass this breadth of relationships within a single text are rare in the literature, making this work a valuable contribution to stimulating discussions on macroeconomic policies.

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.

Numerical parameter sensitivity analysis of residual stresses induced by deep rolling for a 34CrNiMo6 steel railway axle

To optimise the benefits of the deep-rolling process in the service life context of treated components, the process application must be investigated. In addition to the reduction in surface roughness and near-surface material strengthening, compressive residual stresses are introduced, which are primarily responsible for the increase in service life for components, especially in the case of high-strength steel materials. A numerical parameter sensitivity analysis is performed in order to investigate the introduced residual stresses in detail. For this purpose, a validated deep-rolling simulation model is used, which replicates the deep rolling of a railway axle made of the high-strength steel material 34CrNiMo6. The model is based on an elastic-plastic Chaboche material model parameterised on uniaxial tensile and LCF test results and validated with residual stress measurements. Using this model as a basis, the effect of the main process parameters deep-rolling force, feed rate, friction coefficient, number of overruns, tool geometry, and shaft geometry on the resulting residual stress state are investigated. The results reveal that the deep-rolling force has the most significant influence on the introduced residual stress state and should therefore be highlighted. In the case of applying a deep-rolling force of more than 10 kN, maximum compressive residual stresses of around − 1000 MPa are introduced, and a strong saturating behaviour is shown. Maximum compensating tensile residual stresses of + 100 MPa occur below the surface. The main influence of the deep-rolling force is the effective depth achieved, which is determined by the depth of the zero crossing. This varies from 1 mm with an applied force of 2 kN to more than 3.5 mm with 20 kN. Furthermore, the results are analysed to conclude suggestions for the process’s applicability, and a proposal for an optimised deep-rolling treatment is presented. There multiple deep rolling with decreased deep-rolling forces is used to achieve a comparably optimised residual stress state. In summary, with the presented results, a contribution to a deeper understanding of the deep-rolling process can be achieved; the influence of the most important process parameters on the residual stress in-depth profiles is established; an optimisation proposal is presented; and correlations are found. Thus, the base work for further fatigue strength assessments and the optimisation of the deep-rolling process regarding the increase of service is laid.

An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters. Therefore, to quantitatively evaluate the relative importance of model parameters on the production forecasting performance, sensitivity analysis of parameters is required. The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design. A data-driven global sensitivity analysis (GSA) method using convolutional neural networks (CNN) is proposed to identify the influencing parameters in shale gas production. The CNN is trained on a large dataset, validated against numerical simulations, and utilized as a surrogate model for efficient sensitivity analysis. Our approach integrates CNN with the Sobol' global sensitivity analysis method, presenting three key scenarios for sensitivity analysis: analysis of the production stage as a whole, analysis by fixed time intervals, and analysis by declining rate. The findings underscore the predominant influence of reservoir thickness and well length on shale gas production. Furthermore, the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.

Investigation of overall performance of turbofan engine with pulse detonation combustor in bypass duct

The performance model was established to study the performance of mixed exhaust turbofan engine with pulse detonation combustor (PDC) in the bypass duct. The effects of the total pressure loss of isolator and bypass circulation parameters on the characteristic of PDC and performance of engine were analyzed. Then, the sensitivity analysis of performance parameters was carried out on the influence of the components parameters. The performance of mixed exhaust turbofan engine with PDC in the bypass duct was compared with that of the conventional afterburner turbofan engine at the same design circulation parameters. The results show that to increase the total pressure loss of isolator is beneficial to the improvement of performance because of the improvement of pressure ratio; when fan pressure ratio is constant, with the increasing of bypass ratio, the specific fuel consumption and specific thrust increase; with the increasing of fan pressure ratio, when bypass ratio is 0.2 to 0.4, specific thrust increases first and then decreases. When bypass ratio is 0.4 to 0.5, specific thrust increases first and then remains constant. When bypass ratio is 0.5 to 0.9, specific thrust increases, but the rate of increase reduces. At different bypass ratio, specific fuel consumption decreases with increasing of fan pressure ratio; the performance of engine is more sensitive to the component parameters that directly affect the flow state parameters of the bypass duct; because of supercharging performance of PDC, PDC in the bypass duct only uses part of air flow to organize combustion that can make specific thrust of turbofan engine with PDC in the bypass duct and conventional afterburner turbofan engine are equivalent. Meanwhile, specific fuel consumption can be reduced by 27.7% at design point and 12.8%-26.8% at off-design points.

Region optimization and sensitivity analysis of THT assembly process parameters based on flexible soldering robot platform

In order to solve the problem that the selection of key process parameters of a flexible soldering robot depends on manual experience, the empirical model of its THT assembly process parameters is established with the response surface method and the reliability of the model is verified. On this basis, the multi-parameter relative sensitivity of the key process parameters is analyzed through the Monte-Carlo simulation, and their single-parameter sensitivity curve is drawn. Then, based on the sensitivity analysis, the stability region division method is designed, and the stability region of process parameters for soldering quality comprehensive score is obtained. The optimal region of process parameters of the soldering robot are acquired by compressing the stability region: soldering temperature from 350-368 ℃, soldering time from 1.31-1.48 s and solder feeding volume from 1.5-2.0 mm3. The average value of the soldering quality comprehensive score within the optimal region of process parameters is 90.2 points.

Lyme Disease Models of Tick-Mouse Dynamics with Seasonal Variation in Births, Deaths, and Tick Feeding.

Lyme disease is the most common vector-borne disease in the United States impacting the Northeast and Midwest at the highest rates. Recently, it has become established in southeastern and south-central regions of Canada. In these regions, Lyme disease is caused by Borrelia burgdorferi, which is transmitted to humans by an infected Ixodes scapularis tick. Understanding the parasite-host interaction is critical as the white-footed mouse is one of the most competent reservoir for B. burgdorferi. The cycle of infection is driven by tick larvae feeding on infected mice that molt into infected nymphs and then transmit the disease to another susceptible host such as mice or humans. Lyme disease in humans is generally caused by the bite of an infected nymph. The main aim of this investigation is to study how diapause delays and demographic and seasonal variability in tick births, deaths, and feedings impact the infection dynamics of the tick-mouse cycle. We model tick-mouse dynamics with fixed diapause delays and more realistic Erlang distributed delays through delay and ordinary differential equations (ODEs). To account for demographic and seasonal variability, the ODEs are generalized to a continuous-time Markov chain (CTMC). The basic reproduction number and parameter sensitivity analysis are computed for the ODEs. The CTMC is used to investigate the probability of Lyme disease emergence when ticks and mice are introduced, a few of which are infected. The probability of disease emergence is highly dependent on the time and the infected species introduced. Infected mice introduced during the summer season result in the highest probability of disease emergence.

Optimization of PMSM for EV based on Vibration and Noise Suppression

The key to the suppression of vibration and noise for PMSM is the optimization of electromagnetic excitation force. The method of motor body optimization can effectively reduce the radial excitation force of the motor, so as to suppress the vibration and noise of the motor. Firstly, the stator structure of the motor is optimized with V-shape skew slot based on the analytical modeling of the radial electromagnetic excitation force of the motor. Then, the structural parameters of the motor that affect the electromagnetic excitation force of the motor are determined, and the average torque, torque ripple and radial electromagnetic excitation force generated by tangential electromagnetic excitation force are taken as the optimization objectives. The sensitivity analysis and classification of the structural parameters of the motor are carried out. The multi-objective genetic algorithm and response surface method are combined to optimize the structural parameters of the motor. Finally, the finite element analysis, modal analysis, multi-speed vibration and noise analysis of the optimized motor are done. The performance comparisons before and after optimization have proved that the peak of equivalent sound power level have decreased by 8.65% after the optimization of V-shaped skewed slot structure. After the optimization of structural parameters, the power level of permanent magnet synchronous motor has been reduced by 9.22%. For the vibration noise caused by resonance and the main frequency of vibration noise harmonics, the suppression effects are also better than those of V-shape skewed slots optimization, and the ERPL values are reduced by 9.22% and 10.12%, respectively, in two cases. The results show that the vibration and noise of permanent magnet synchronous motor are effectively suppressed.

Fatigue Life Prediction Model of FRP-Concrete Interface Based on Gene Expression Programming.

Under fatigue loading, the interfacial fatigue life of fiber-reinforced polymer(FRP)-concrete is an important index for the analysis of the fatigue performance of reinforced concrete beams strengthened with FRP materials and the evaluation of the reinforcement effect. To solve the problems of the inconsistent and limited accuracy of existing fatigue life prediction models, gene expression programming (GEP) was used to study the interfacial fatigue life of FRP-concrete. Firstly, 219 sets of interfacial fatigue test data were collected, which included two kinds of reinforcement methods, namely, externally bonded (EB) reinforcement and near-surface-mounted (NSM) reinforcement; secondly, Pearson correlation analysis was used to determine the key factors affecting the fatigue life, and then GEP was used to explore the influence of different input forms on the prediction accuracy of the model. Fatigue life calculation formulas applicable to the two kinds of reinforcement methods, i.e., EB and NSM, were established, and a specific calculation formula was established. The model was subjected to parameter sensitivity analysis and variable importance analysis and was found to reflect the intrinsic relationship between the fatigue life and various factors. Finally, the GEP model was compared with the models proposed by other researchers. Five statistical indices, such as the coefficient of determination and the average absolute error, were selected to assess the model, and the results show that the GEP model has higher prediction accuracy than other models, with a coefficient of determination of 0.819, and indicators such as the average absolute error are also lower than those of the rest of the models.

An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters

Heart rate variability (HRV) analysis is often used to estimate human health and fitness status. More specifically, a range of parameters that express the variability in beat-to-beat intervals are calculated from electrocardiogram beat detections. Since beat detection may yield erroneous interval data, these errors travel through the processing chain and may result in misleading parameter values that can lead to incorrect conclusions. In this study, we utilized Monte Carlo simulation on real data, Kolmogorov–Smirnov tests and Bland–Altman analysis to carry out extensive analysis of the noise sensitivity of different HRV parameters. The used noise models consider Gaussian and student-t distributed noise. As a result we observed that commonly used HRV parameters (e.g. pNN50 and LF/HF ratio) are especially sensitive to noise and that all parameters show biases to some extent. We conclude that researchers should be careful when reporting different HRV parameters, consider the distributions in addition to mean values, and consider reference data if applicable. The analysis of HRV parameter sensitivity to noise and resulting biases presented in this work generalizes over a wide population and can serve as a reference and thus provide a basis for the decision about which HRV parameters to choose under similar conditions.

A novel numerical framework to simulate mechanical response and damage characteristics of mountain tunnels under faulting

This paper presents a novel numerical framework that can simulate the mechanical response and damage characteristics of mountain tunnels under faulting more precisely and efficiently. Both the submodeling and two-stage models are adopted in the framework. The submodeling technique is used to obtain a precise simulation of tunnel-fault interaction while ensuring computational efficiency. The two-stage models can acquire accurate initial stress of the tunnel lining and simulate the tunnel-rock interaction during fault movement, which can also achieve the transition from static to dynamic analysis. The computational efficiency can be improved by about 73 % via the proposed numerical framework compared to the conventional numerical analysis when keeping the same mesh sizes. Moreover, overall lining damage indices and cross-sectional ovalization are introduced to quantitatively evaluate the respective damage and deformation of the tunnel lining cross-section, and damage level classifications for tunnel lining under faulting are established. Parametric sensitivity analyses are performed to investigate key factors affecting the mechanical damage behavior of mountain tunnels under strike-slip faulting, including fault dislocation, fault zone width, and the tunnel-fault intersection angle. The increasing fault dislocation causes larger tunnel-rock interaction around the fault plane, making the damage and deformation of tunnel lining around the fault plane more severe. The fault zone width has a significant impact on the damage and deformation range of tunnel lining. The tunnel-fault intersection angle contributes great influence on the magnitude and range of tunnel lining damage and deformation. The tunnel lining would suffer relatively severe damage and deformation when the two fault blocks move away from each other along the tunnel axial.

Patent pricing under incomplete information

ABSTRACT Patent pricing is fundamental for patent industrialisation. This article aims to capture the patent price under incomplete information with game theory approaches. The results show first, patent industrialisation with a high technology level promotes outputs and reduces the prices of final goods. Second, competition decreases the price of patents, while asymmetric information deters patent industrialisation. Finally, a new type of risk function is established to merit the risk of cheap information, and we show that exaggerated technology amplifies the risks of both cost and profit. The policy implication is to strengthen information disclosure and supervision about patents. Finally, numerical examples are illustrated with the sensitivity analysis of the key parameters to justify the proposed model.

Design concept evaluation based on cloud rough model and modified AHP-VIKOR: An application to lithography tool manufacturing process

Design concept evaluation is a wide domain of research as the production process of new product development is mainly based on the evaluations of experts. As different types of uncertainties like vagueness, randomness, and diversity essentially exist in the evaluation information, the manipulation models that deal with just one type of uncertainty are not appropriate for ranking the design alternatives. The integration of multiple approaches into a single mathematical model to create an effective framework is more useful for uncertain information manipulation. This paper presents an innovative group decision-making approach to evaluate design concepts by integrating cloud rough numbers, analytic hierarchy process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to introduce a novel cloud rough AHP-VIKOR model. The concept of cloud rough numbers is utilized by fusing the merits of the cloud model theory and rough approximations in addressing randomness in experts’ judgments and intrapersonal uncertainty for handling consensus inconsistency. Some new modified algebraic operations are introduced to remove the errors in already existing operations of cloud rough numbers. The cloud rough AHP technique is introduced to compute weights of design criteria by computing the cloud rough numbers of linguistic values and their reciprocals. Based on cloud rough values of positive and negative ideals, the individual regret, group utility values and evaluation indices are determined. Using the distance of evaluation indices from minimized cloud rough value, the design alternatives are ranked using different formulae. The significance of the proposed approach is illustrated with a real-world application in lithography tool manufacturing process. The out-performance and reliability of the proposed model is studied by a sensitivity analysis of different parameters in the method, and a comparison analysis with existing approaches.

Improvement of the Unequal Division Shear Zone Model Based on Sensitivity Analysis and Classification of Uncertain Parameters

Improvement of the Unequal Division Shear Zone Model Based on Sensitivity Analysis and Classification of Uncertain Parameters