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Article
Publication date: 30 April 2020

Ahmad Nasseri, Sajad Jamshidi, Hassan Yazdifar, David Percy and Md Ashraful Alam

With suitable optimization criteria, hybrid models have proven to be efficient for preparing portfolios in capital markets of developed countries. This study adapts and…

113

Abstract

Purpose

With suitable optimization criteria, hybrid models have proven to be efficient for preparing portfolios in capital markets of developed countries. This study adapts and investigates these methods for a developing country, thus providing a novel approach to the application of banking and finance. Our specific objectives are to employ a stochastic dominance criterion to evaluate the performances of over-the-counter (OTC) companies in a developing country and to analyze them with a hybrid model involving particle swarm optimization and artificial neural networks.

Design/methodology/approach

In order to achieve these aims, the authors conduct a case study of OTC companies in Iran. Weekly and daily returns of 36 companies listed in this market are calculated for one year during 2014–2015. The hybrid model is particularly interesting, and the results of the study identify first-, second- and third-order stochastic dominances among these companies. The study’s chosen model uses the best performing combination of activation functions in our analysis, corresponding to TPT, where T represents hyperbolic tangent transfers and P represents linear transfers.

Findings

Our portfolios are based on the shares of companies ranked with respect to the stochastic dominance criterion. Considering the minimum and maximum numbers of shares to be 2 and 10 for each portfolio, an eight-share portfolio is determined to be optimal. Compared with the index of Iran OTC during the research period of this study, our selected portfolio achieves a significantly better performance. Moreover, the methods used in this analysis are shown to be as efficient as they were in the capital markets of developed countries.

Research limitations/implications

The problem of optimizing investment portfolios has to allow for correlations among returns from the financial maintenance period under consideration if an asymmetric distribution of returns exists (Babaei et al., 2015). Therefore, it is desirable to select an appropriate criterion in order to prepare an optimal portfolio and prioritize investment options. Although a back propagation technique is very popular in artificial neural (ANN) training, it is time-consuming to train a network in this way, and other methods such as particle swarm optimization (PSO) should be considered instead. In the hybrid combination of PSO and ANN, it is not the structure of a neural network that changes. Rather, the weighting method and the training technique chosen for the network are the important aspects, and these relate to PSO, so the only role ANN plays in this process is to reduce the errors.

Practical implications

The hybrid model combining ANN and PSO is seen to be considerably successful for generating optimal results and appropriate activation functions. These results are consistent with the theoretical findings of Das et al. (2013) and an application of the simple PSO in a study conducted by Pederson and Chipperfield (2010). Our research results also confirm the efficiency of stochastic dominance criteria as noted in the studies conducted by Roman et al. (2013), ANN as in a study carried out by Kristijanpoller et al. (2014) and PSO as in studies conducted by Liu et al. (2015) and Deng et al. (2012). These studies were carried out in the capital markets of developed countries, whereas the authors’ analysis relates to a developing country.

Originality/value

The authors deduce that the tools and methods whose efficiency was proven in the capital markets of developed countries also apply to, and demonstrate efficiency in, two novel applications of portfolio optimization within developing countries. The first of these is gaining familiarity with the theory and practice of these research tools and the methods that enrich financial knowledge of investors in developing countries. The second of these is the application of tools and methods identified by investors in the capital markets of developing countries, which enables optimal allocation of financial resources and growth of the markets. The authors expect that these findings will contribute to improving the economies of developing countries and thus help with economic development and facilitation of improving trends.

Details

Journal of Applied Accounting Research, vol. 21 no. 3
Type: Research Article
ISSN: 0967-5426

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Article
Publication date: 1 September 2004

David F. Percy

Successful strategies for maintenance require good decisions and we commonly use stochastic reliability models to help this process. These models involve unknown parameters, so we…

1142

Abstract

Successful strategies for maintenance require good decisions and we commonly use stochastic reliability models to help this process. These models involve unknown parameters, so we gather data to learn about these parameters. However, such data are often difficult to collect for maintenance applications, leading to poor parameter estimates and incorrect decisions. A subjective modelling approach can resolve this problem, but requires us to specify suitable prior distributions for the unknown parameters. This paper considers which priors to adopt for common maintenance models and describes the method of predictive elicitation for determining unknown hyperparameters associated with these prior distributions. We discuss the computational difficulties of this approach and consider numerical methods for resolving this problem. Finally, we present practical demonstrations to illustrate the potential benefits of predictive elicitation and subjective analysis. This work provides a major step forward in making the methods of subjective Bayesian inference available to maintenance decision makers in practice. Practical implications. This paper recommends powerful strategies for expressing subjective knowledge about unknown model parameters, in the context of maintenance applications that involve making decisions.

Details

Journal of Quality in Maintenance Engineering, vol. 10 no. 3
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 1 March 1996

David F. Percy and Khairy A.H. Kobbacy

Develops practical models for preventive maintenance policies using Bayesian methods of statistical inference. Considers the analysis of a delayed renewal process and a delayed…

1057

Abstract

Develops practical models for preventive maintenance policies using Bayesian methods of statistical inference. Considers the analysis of a delayed renewal process and a delayed alternating renewal process with exponential times to failure. This approach has the advantage of generating predictive distributions for numbers of failures and downtimes rather than relying on estimated renewal functions. Demonstrates the superiority of this approach in analysing situations with non‐linear cost functions, which arise in reality, by means of an example.

Details

Journal of Quality in Maintenance Engineering, vol. 2 no. 1
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 15 November 2021

Sidali Bacha, Ahmed Bellaouar and Jean-Paul Dron

Complex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process…

94

Abstract

Purpose

Complex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process (NHPP) and the renewal process (RP), which represent the minimum and maximum repair, respectively. However, the industrial environment affects systems in some way. This is why the main objective of this work is to model the CRS with a concept reflecting the real state of the system by incorporating an indicator in the form of covariate. This type of model, known as the proportional intensity model (PIM), will be analyzed with simulated failure data to understand the behavior of the failure process, and then it will be tested for real data from a petroleum company to evaluate the effectiveness of corrective actions carried out.

Design/methodology/approach

To solve the partial repair modeling problem, the PIM was used by introducing, on the basis of the NHPP model, a multiplicative scaling factor, which reflects the degree of efficiency after each maintenance action. Several values of this multiplicative factor will be considered to generate data. Then, based on the reliability and maintenance history of 12-year pump's operation obtained from the SONATRACH Company (south industrial center (CIS), Hassi Messaoud, Algeria), the performance of the PIM will be judged and compared with the model of NHPP and RP in order to demonstrate its flexibility in modeling CRS. Using the maximum likelihood approach and relying on the Matlab software, the best fitting model should have the largest likelihood value.

Findings

The use of the PIM allows a better understanding of the physical situation of the system by allowing easy modeling to apply in practice. This is expressed by the value which, in this case, represents an improvement in the behavior of the system provided by a good quality of the corrective maintenance performed. This result is based on the hypothesis that modeling with the PIM can provide more clarification on the behavior of the system. It can indicate the effectiveness of the maintenance crew and guide managers to confirm or revise their maintenance policy.

Originality/value

The work intends to reflect the real situation in which the system operates. The originality of the work is to allow the consideration of covariates influencing the behavior of the system during its lifetime. The authors focused on modeling the degree of repair after each corrective maintenance performed on an oil pump. Since PIM does not require a specific reliability distribution to apply it, it allows a wide range of applications in the various industrial environments. Given the importance of this study, the PIM can be generalized for more covariates and working conditions.

Details

International Journal of Quality & Reliability Management, vol. 40 no. 1
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 1 June 1997

K.A.H. Kobbacy, D.F. Percy and B.B. Fawzi

Preventive maintenance (PM) is an effective maintenance policy which is widely applied in industry. Reviews the main approaches of modelling PM and discusses the characteristics…

1015

Abstract

Preventive maintenance (PM) is an effective maintenance policy which is widely applied in industry. Reviews the main approaches of modelling PM and discusses the characteristics of real life PM data which influence the methods for modelling PM. The most salient features of these data are the limited size and intensive censoring effect. Then introduces a parametric bootstrap method for fitting PM data to distributions. A simulation study to compare this method with the established Akaike and Schwarz criteria shows that while the bootstrap method is marginally better in identifying the true distribution, this is counterbalanced by the intensive computational effort needed.

Details

Journal of Quality in Maintenance Engineering, vol. 3 no. 2
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 13 July 2022

Salih Tekin, Kemal Bicakci, Ozgur Mersin, Gulnur Neval Erdem, Abdulkerim Canbay and Yusuf Uzunay

With the irresistible growth in digitization, data backup policies become essential more than ever for organizations seeking to improve reliability and availability of…

510

Abstract

Purpose

With the irresistible growth in digitization, data backup policies become essential more than ever for organizations seeking to improve reliability and availability of organizations' information systems. However, since backup operations do not come free, there is a need for a data-informed policy to decide how often and which type of backups should be taken. In this paper, the authors present a comprehensive mathematical framework to explore the design space for backup policies and to optimize backup type and interval in a given system. In the authors' framework, three separate cost factors related to the backup process are identified: backup cost, recovery cost and data loss cost. The objective function has a multi-criteria structure leading to a backup policy minimizing a weighed function of these factors. To formalize the cost and objective functions, the authors get help from renewal theory in reliability modeling. The authors' optimization framework also formulates mixed policies involving both full and incremental backups. Through numerical examples, the authors show how the authors' optimization framework could facilitate cost-saving backup policies.

Design/methodology/approach

The methodology starts with designing different backup policies based on system parameters. Each constructed policy is optimized in terms of backup period using renewal theory. After selecting the best back-up policy, the results are demonstrated through numerical studies.

Findings

Data backup polices that are tailored to system parameters can result in significant gains for IT (Information Technology) systems. Collecting the necessary parameters to design intelligent backup policies can also help managers understand managers' systems better. Designed policies not only provides the frequency of back up operations, but also the type of backups.

Originality/value

The original contribution of this study is the explicit construction and determination of the best backup policies for IT systems that are prone to failure. By applying renewal theory in reliability, the authors present a mathematical framework for the joint optimization of backup cost factors, i.e. backup cost, recovery time cost and data loss cost.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 2
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 25 October 2019

Nicolas La Roche-Carrier, Guyh Dituba Ngoma, Yasar Kocaefe and Fouad Erchiqui

Reliability plays an important role in the execution of the maintenance improvement and the understanding of its concepts is essential to predict the type of maintenance according…

240

Abstract

Purpose

Reliability plays an important role in the execution of the maintenance improvement and the understanding of its concepts is essential to predict the type of maintenance according to the equipment state. Thereby, a computational tool was developed and programming with VBA in Excel® for reliability and failure analysis in a mining context. The paper aims to discuss these issues.

Design/methodology/approach

The developed approach use the modeling of stochastic processes, such as the renewal process, the non-homogeneous Poisson process and less conventional method as the Bayesian approach, by considering Jeffreys non-informative prior. The resolution gives the best associated model, the parameters estimation, the mean time between failure and the reliability estimate. This approach is validated with the reliability analysis of inter-failure times from underground rock bolters subsystems, over a two-year period.

Findings

Results show that Weibull and lognormal probability distribution fit to the most subsystems inter-failure times. The study revealed that the bolting head, the rock drill, the screen handler, the electric/electronic system, the hydraulic system, the drilling feeder and the structural consume the most repair frequency. The hydraulic and electric/electronic subsystems represent the lowest reliability after 50 operation hours.

Originality/value

For the first time, this case study defines practical failures and reliability information for rock bolter subsystems based on real operation data. This paper is useful to the comparative evaluation of rock bolter by detecting the weakest elements and understanding failure patterns in the individual observation subsystems on the overall machine performance.

Details

International Journal of Quality & Reliability Management, vol. 37 no. 2
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 2 October 2007

C.A.V. Cavalcante and A.T. de Almeida

The purpose of this paper is to develop a model that permits more rational planning for preventive maintenance, by controlling failures in the specific context of equipment…

1928

Abstract

Purpose

The purpose of this paper is to develop a model that permits more rational planning for preventive maintenance, by controlling failures in the specific context of equipment breakdown. Thus not only the cost and reliability parameters are dealt with, but also the peculiarities of different contexts in which maintenance activities occur. Furthermore, it aims to include Bayesian methodology in the procedure to overcome main difficulties in failure data.

Design/methodology/approach

A multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion, and the ability to provide a solution in uncertainties situations, has been developed from adaptation of classical models. This model also uses Bayesian elements to address uncertainties during equipment failures.

Findings

The paper finds that in a preventive maintenance planning, as a multi‐criteria decision problem, different types of uncertainties may be identified, which may be categorized as external uncertainties and internal uncertainties. In the proposed model a division of procedures has been established, dealing with external uncertainties first, then the internal uncertainties related to the structure of the problem and analysis of the decision are addressed using the multi‐criteria decision‐making method PROMETHEE III that allows the amplification of the notion of indifference. In this way, a suitable structure to connect two types of uncertainties was structured.

Practical implications

The model will assist the decision‐maker in preventive maintenance planning to take uncertainties into account, seeing the alternatives that are closest, through an amplification of the notion of indifference provided by PROMETHEE III. Furthermore, in practice the proposed model have an impact on maintenance cost and reliability of production plant.

Originality/value

This paper proposes a multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion; and the ability to provide a solution even when failure data are unavailable or incomplete.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 4
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 1 June 2001

A. Pillay, J. Wang, A.D. Wall and T. Ruxton

The current practice of maintenance on fishing vessels varies according to the operating policies of the owner/operator. On most occasions, the crew does not carry out regular…

826

Abstract

The current practice of maintenance on fishing vessels varies according to the operating policies of the owner/operator. On most occasions, the crew does not carry out regular maintenance while at sea. As such, all maintenance work is completed while the vessel is at the discharging port. The time between discharge ports can be as long as three to six months, which allows for failures on the machinery propagating and leading to a catastrophic breakdown. Discusses the possibility of avoiding such events by means of implementing an inspection regime based on the delay‐time concept. Operating and failure data that have been gathered from a fishing vessel are used to demonstrate the proposed approach. The outcome of the model is incorporated into the existing maintenance policy of the fishing vessel to assess its effectiveness.

Details

Journal of Quality in Maintenance Engineering, vol. 7 no. 2
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 6 February 2017

Hamed Maleki and Yingjie Yang

The purpose of this paper is to illustrate an uncertain programming model for scheduling of preventive maintenance (PM) actions. The PM scheduling, in which PM actions are…

557

Abstract

Purpose

The purpose of this paper is to illustrate an uncertain programming model for scheduling of preventive maintenance (PM) actions. The PM scheduling, in which PM actions are performed under fixed intervals, is solved by grey systems theory.

Design/methodology/approach

The paper applied the grey evaluation method based on triangular whitenization weight functions which includes two classes: endpoint evaluation method and center-point evaluation method.

Findings

Two methods give the same results based on endpoint and center-point triangular whitenization weight functions. For validation, the results were compared by Cassady’s method.

Originality/value

The scheduling of PM is crucial in reliability and maintenance engineering. Hundreds of parts compose complex machines that require replacement and/or repairing. It is helpful to reduce the outage loss on frequent repair/replacement parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency.

Details

Grey Systems: Theory and Application, vol. 7 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

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