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Article
Publication date: 6 July 2015

Zeyu Ma, Jinglai Wu, Yunqing Zhang and Ming Jiang

The purpose of this paper is to provide a new computational method based on the polynomial chaos (PC) expansion to identify the uncertain parameters of load sensing proportional…

190

Abstract

Purpose

The purpose of this paper is to provide a new computational method based on the polynomial chaos (PC) expansion to identify the uncertain parameters of load sensing proportional valve (LSPV), which is commonly used to improve the efficiency of brake system in heavy truck.

Design/methodology/approach

For this investigation, the mathematic model of LSPV is constructed in the form of state space equation. Then the estimation process is implemented relying on the experimental measurements. With the coefficients of the PC expansion obtained by the numerical implementation, the output observation function can be transformed into a linear and time-invariant form. The uncertain parameter recursively update functions based on Newton method can therefore be derived fit for computer calculation. To improve the estimation accuracy and stability, the Newton method is modified by employing the acceptance probability to escape from the local minima during the estimation process.

Findings

The accuracy and effectiveness of the proposed parameter estimation method are confirmed by model validation compared with other estimation methods. Meanwhile, the influence of measurement noise on the robustness of the estimation methods is taken into consideration, and it is shown that the estimation approach developed in this paper could achieve impressive stability without compromising the convergence speed and accuracy too much.

Originality/value

The model of LSPV is originally developed in this paper, and then the authors propose a novel effective strategy for recursively estimating uncertain parameters of complicate pneumatic system based on the PC theory.

Details

Engineering Computations, vol. 32 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 17 July 2009

Emmanuel Blanchard, Adrian Sandu and Corina Sandu

The purpose of this paper is to propose a new computational approach for parameter estimation in the Bayesian framework. A posteriori probability density functions are obtained…

Abstract

Purpose

The purpose of this paper is to propose a new computational approach for parameter estimation in the Bayesian framework. A posteriori probability density functions are obtained using the polynomial chaos theory for propagating uncertainties through system dynamics. The new method has the advantage of being able to deal with large parametric uncertainties, non‐Gaussian probability densities and nonlinear dynamics.

Design/methodology/approach

The maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. Direct stochastic collocation is used as a less computationally expensive alternative to the traditional Galerkin approach to propagate the uncertainties through the system in the polynomial chaos framework.

Findings

The new approach is explained and is applied to very simple mechanical systems in order to illustrate how the Bayesian cost function can be affected by the noise level in the measurements, by undersampling, non‐identifiablily of the system, non‐observability and by excitation signals that are not rich enough. When the system is non‐identifiable and an a priori knowledge of the parameter uncertainties is available, regularization techniques can still yield most likely values among the possible combinations of uncertain parameters resulting in the same time responses than the ones observed.

Originality/value

The polynomial chaos method has been shown to be considerably more efficient than Monte Carlo in the simulation of systems with a small number of uncertain parameters. This is believed to be the first time the polynomial chaos theory has been applied to Bayesian estimation.

Details

Engineering Computations, vol. 26 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 17 June 2008

Dariusz Gąsior

The purpose of this paper is to examine the problem of rate allocation (RA) in computer networks in cases when some parameters are unknown or their values are imprecise.

Abstract

Purpose

The purpose of this paper is to examine the problem of rate allocation (RA) in computer networks in cases when some parameters are unknown or their values are imprecise.

Design/methodology/approach

The application of uncertain variables for the RA problems in computer networks in the presence of uncertainty is proposed.

Findings

Decision‐making problem formulations for RA in computer networks with unknown parameters of the utility functions and bandwidths based on the network utility maximization concept are given. Solution algorithms for all these problems are proposed.

Research limitations/implications

It is assumed that an expert can describe possible values of unknown network parameters in the form of a certainty distribution. Then, the formalism of uncertain variables is applied and the knowledge of an expert is modelled with certainty distributions.

Practical implications

The RA algorithms obtained can be useful for designing and planning computer networks.

Originality/value

The new approach to the RA problem in computer networks in the presence of uncertainty, in cases when the probabilistic approach cannot be applied, is proposed and discussed.

Details

Kybernetes, vol. 37 no. 5
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 7 August 2009

Dariusz Gąsior

The purpose of this paper is to deal with a problem of admission control in computer networks when some of their parameters are uncertain. The case is considered when the most…

Abstract

Purpose

The purpose of this paper is to deal with a problem of admission control in computer networks when some of their parameters are uncertain. The case is considered when the most common probabilistic description of the uncertainty cannot be used and another approach should be applied.

Design/methodology/approach

The uncertain versions of admission control problem with quality of service requirements are considered. The uncertain variables are used to describe possible values of the unknown parameters in computer networks.

Findings

Given are formulations for the admission control problem in computer networks with unknown values of the capacities based on the network utility maximization concept. Solution algorithms for all these problems are proposed.

Research limitations/implications

It is assumed that an expert can describe possible values of uncertain network parameters in the form of a certainty distribution. Then the formalism of uncertain variables is applied and the knowledge of an expert is modelled with the use of certainty distributions. Decisions strongly depends on the quality of an expert's knowledge.

Practical implications

Obtained admission control algorithms can be useful for planning and designing of computer networks.

Originality/value

A new approach to the admission control problem in computer networks in the presence of uncertainty, in the case when the uncertain variable can be applied, is proposed and discussed.

Details

Kybernetes, vol. 38 no. 7/8
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 28 June 2022

Jizhuang Hui, Shuai Wang, Zhu Bin, Guangwei Xiong and Jingxiang Lv

The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under…

Abstract

Purpose

The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under complex uncertainty.

Design/methodology/approach

An improved chance-constrained method is developed, in which confidence level of uncertain parameters is used to process uncertainty, and based on this, the reliability of the constraints is measured. Then, this study proposes a robust reconstruction method to transform the chance-constrained model into a deterministic model that is easy to solve, in which the robust transformation methods are used to deal with constraints with uncertainty on the right/left. Then, experimental studies using a real-world production data set provided by a gearbox synchronizer factory of an automobile supplier is carried out.

Findings

This study has demonstrated the merits of the proposed approach where the inventory of products tends to increase with the increase of confidence level. Due to a larger confidence level may result in a more strict constraint, which means that the decision-maker becomes more conservative, and thus tends to satisfy more external demands at the cost of an increase of production and stocks.

Research limitations/implications

Joint decisions of production lot-sizing and scheduling widely applied in industries can effectively avert the infeasibility of lot-size decisions, caused by capacity of lot-sing alone decision and complex uncertainty such as product demand and production cost. is also challenging.

Originality/value

This study provides more choices for the decision-makers and can also help production planners find bottleneck resources in the production system, thus developing a more feasible and reasonable production plan in a complex uncertain environment.

Details

Assembly Automation, vol. 42 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 5 March 2018

Pengbo Wang and Jingxuan Wang

Uncertainty is ubiquitous in practical engineering and scientific research. The uncertainties in parameters can be treated as interval numbers. The prediction of upper and lower…

Abstract

Purpose

Uncertainty is ubiquitous in practical engineering and scientific research. The uncertainties in parameters can be treated as interval numbers. The prediction of upper and lower bounds of the response of a system including uncertain parameters is of immense significance in uncertainty analysis. This paper aims to evaluate the upper and lower bounds of electric potentials in an electrostatic system efficiently with interval parameters.

Design/methodology/approach

The Taylor series expansion is proposed for evaluating the upper and lower bounds of electric potentials in an electrostatic system with interval parameters. The uncertain parameters of the electrostatic system are represented by interval notations. By performing Taylor series expansion on the electric potentials obtained using the equilibrium governing equation and by using the properties of interval mathematics, the upper and lower bounds of the electric potentials of an electrostatic system can be calculated.

Findings

To evaluate the accuracy and efficiency of the proposed method, the upper and lower bounds of the electric potentials and the computation time of the proposed method are compared with those obtained using the Monte Carlo simulation, which is referred to as a reference solution. Numerical examples illustrate that the bounds of electric potentials of this method are consistent with those obtained using the Monte Carlo simulation. Moreover, the proposed method is significantly more time-saving.

Originality/value

This paper provides a rapid computational method to estimate the upper and lower bounds of electric potentials in electrostatics analysis with interval parameters. The precision of the proposed method is acceptable for engineering applications, and the computation time of the proposed method is significantly less than that of the Monte Carlo simulation, which is the most widely used method related to uncertainties. The Monte Carlo simulation requires a large number of samplings, and this leads to significant runtime consumption.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 37 no. 2
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 1 August 2004

S. Chan Choi and Sharan Jagpal

Most pricing studies assume that firms have complete information about demand. In practice, managers must make decisions, given incomplete information about the demand for their…

1106

Abstract

Most pricing studies assume that firms have complete information about demand. In practice, managers must make decisions, given incomplete information about the demand for their own products as well as those of their rivals. This paper develops a duopoly pricing model in which firms market differentiated products in a world of uncertainty. Results show that the predictions of standard strategic pricing models may not hold when firms face parameter uncertainty and are risk‐averse. Under well‐defined conditions, there may be a “first‐mover” disadvantage to the firm that attempts to be the Stackelberg price leader in the market, especially in a market where demand is highly uncertain. Interestingly, if parameter uncertainty is sufficiently high, it may even be necessary for the price leader to share market information with its rival. When firms are risk‐averse, uncertainty generally decreases equilibrium prices and the variabilities of profits.

Details

Journal of Product & Brand Management, vol. 13 no. 5
Type: Research Article
ISSN: 1061-0421

Keywords

Article
Publication date: 1 December 2002

Z. Bubnicki

The definitions and basic properties of so called uncertain variables are presented. The uncertain variables are described by certainty distributions given by an expert and…

Abstract

The definitions and basic properties of so called uncertain variables are presented. The uncertain variables are described by certainty distributions given by an expert and characterizing approximate values of the variables. Control problems for uncertain systems with static and dynamic plants are considered. A method of the stability analysis for a system with uncertain parameters is described. Simple examples illustrate the presented approaches.

Details

Kybernetes, vol. 31 no. 9/10
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 8 February 2021

Saied Farham-Nia and Alireza Ghaffari-Hadigheh

The aim of this paper is to study the optimal pricing decision in a supply chain with a dual distribution channel in a centralized and decentralized decision-making systems and…

Abstract

Purpose

The aim of this paper is to study the optimal pricing decision in a supply chain with a dual distribution channel in a centralized and decentralized decision-making systems and investigate the economic impact of retail services on pricing behaviors with respect to the power structures.

Design/methodology/approach

To reach the equilibrium behavior of decision-makers, two-stage optimization, the Stackelberg game and the Bertrand–Nash game have been used. Also, to explore the effect of environmental uncertainty on the behavior of decision-maker, demand functions are characterized as an uncertain price dependent, service dependent and channel dependent. Decision parameters are based on experts’ belief degree, in the sense of uncertainty theory initiated by Liu (2007).

Findings

Obtained results reveal that the retail services have a strategic role in the centralized supply chain and the decentralized supply chain with dominant manufacturer, while both the supply chain and the consumer suffer from higher environmental indeterminacy.

Research limitations/implications

This study is based on possible scenarios of dual distribution system only. Further research is recommended to investigate the applicability of the authors framework in different distribution systems.

Practical implications

The study findings are believed to be valuable for supply chains and organizations about to make a strategic decision on price of their good/service.

Originality/value

The paper contributes to the scarce literature on Uncertainty Theory initiated by Liu (2007), and combination of it with Game Theory for pricing in distribution system of supply chains. The study also contributes by investigating impact of non-price competitive factor (level of service) on pricing strategy.

Details

Journal of Modelling in Management, vol. 16 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 13 August 2019

Hui Lü, Kun Yang, Wen-bin Shangguan, Hui Yin and DJ Yu

The purpose of this paper is to propose a unified optimization design method and apply it to handle the brake squeal instability involving various uncertainties in a unified…

Abstract

Purpose

The purpose of this paper is to propose a unified optimization design method and apply it to handle the brake squeal instability involving various uncertainties in a unified framework.

Design/methodology/approach

Fuzzy random variables are taken as equivalent variables of conventional uncertain variables, and a unified response analysis method is first derived based on level-cut technique, Taylor expansion and central difference scheme. Next, a unified reliability analysis method is developed by integrating the unified response analysis and fuzzy possibility theory. Finally, based on the unified reliability analysis method, a unified reliability-based optimization model is established, which is capable of optimizing uncertain responses in a unified way for different uncertainty cases.

Findings

The proposed method is extended to perform squeal instability analysis and optimization involving various uncertainties. Numerical examples under eight uncertainty cases are provided and the results demonstrate the effectiveness of the proposed method.

Originality/value

Most of the existing methods of uncertainty analysis and optimization are merely effective in tackling one uncertainty case. The proposed method is able to handle the uncertain problems involving various types of uncertainties in a unified way.

Details

Engineering Computations, vol. 37 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

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