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The purpose of this paper is to provide an overview of RAMS engineering in industry and research.

A range of research articles (1988‐2005) covering RAMS engineering is discussed to provide a practical and theoretical overview of RAMS engineering in industry and research.

The paper provides information about the current scenario and also about the past scenario of RAMS engineering in research and industry. Research limitations/implications – This paper reviews much of the literature on RAMS engineering. The literature is collected from major journals and conference proceedings, the period covered is from 1988 to 2005. The authors have tried to make it reasonably comprehensive, but those papers which are not included were either inadvertently overlooked or considered peripheral to this survey. In the present work the authors have only considered those articles which have included two or more aspects of RAMS. Practical implications – The paper is a very useful source of information for researchers working in the area of RAMS engineering. Originality/value – This paper offers help to researchers in understanding the current and past status of RAMS.

The purpose of this paper is to develop a computer simulation model to evaluate the bowl phenomenon and the allocation at the end of the line of stations with either greater mean operation times or higher variability of operation times.

The model was developed on the basis of a realistic case problem and applied to a six‐station assembly line. The evaluation criteria were the: minimization of the total elapsed time; maximization of the average percentage of working time; and minimization of the average time in the system.

The performance of an assembly line with independently normally distributed operation times could be improved by applying the bowl phenomenon. The allocation of large operation mean times to stations located near the end of the line did not produce improved results. Instead a more balanced allocation proved to be more significantly effective. On the other hand, the assignment of larger variability of operation times to the stations near the end of the line improved the performance of the assembly line.

The investigation contributed to the computer simulation approach to solving assembly line problems that dealt with the impact of normally distributed operation times on the bowl phenomenon and assembly lines with increasing mean operation times and higher variability of operation times at the end of the line of stations.

Considers a class of control systems known as generalized kanban control systems (GKCS) which can be used to implement a pull control mechanism in a manufacturing system. In a GKCS, the production system is decomposed into stages, where each stage consists of a production sub‐system. There are two design parameters per stage: one controls the work‐in‐process in the stage and the other determines the maximum number of finished products of this stage. Investigates the influence of these design parameters on the efficiency of generalized kanban control policies by deriving qualitative properties as well as using experimental results on the behaviour of GKCS.

Emergency departments (ED) are faced with the challenge of capacity planning that caused by the high demand for patients and limited resources. Consequently, inadequate resources lead to increased delays, impacts on the quality of care and increase the health-care costs. Such circumstances necessitate utilizing operational research modules, such as the Markov decision process (MDP) to enable better decision-making. The purpose of this paper is to demonstrate the applicability and usage of MDP on ED.

The adoption of MDP provides invaluable insights into system operations based on the different system states (e.g. very busy to unoccupied) to ensure optimal assigning of resources and reduced costs. In this paper, a descriptive health system model based on the MDP is presented, and a numerical example is illustrated to elaborate its appropriateness in optimal policy decision determination.

Faced with numerous decisions, hospital managers have to ensure that the appropriate technique is used to minimize any undesired outcomes. MDP has been shown to be a robust approach which provides support to the critical decision-making processes. Additionally, MDP also provides insights on the associated costs which enable the hospital managers to efficiently allocate resources ensuring quality health care and increased throughput while minimizing costs.

Applying MDP in the ED is a unique and good starting. MDP is powerful tool helps in making a decision in the critical situations, and the ED needs such tool.

To solve the problem that the traditional methods miss key information in the process of bearing fault identification, this paper aims to apply the phase-space reconstruction (PSR) theory and intelligent diagnosis techniques to extend the one-dimensional vibration signal to the high-dimensional phase space to reveal the system information implied in the univariate time series of the vibration signal.

In this paper, a new method based on the PSR technique and convolutional neural network (CNN) is proposed. First, the delay time and the embedding dimension are determined by the C-C method and the false nearest neighbors method, respectively. Through the coordinate delay reconstruction method, the two-dimensional signal is constructed, and this information is saved in a set of gray images. Then, a simple and efficient convolutional network is proposed. Finally, the phase diagrams of different states are used as samples and input into a two-dimensional CNN for learning modeling to construct a PSR-CNN fault diagnosis model.

The proposed PSR-CNN model is tested on two data sets and compared with support vector machine (SVM), k-nearest neighbor (KNN) and Markov transition field methods, and the comparison results showed that the method proposed in this paper has higher accuracy and better generalization performance.

The method proposed in this paper provides a reliable solution in the field of rolling bearing fault diagnosis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0113/

The purpose of this paper is to develop a novel model of a call center that must treat calls with distinctly different service depending on whether they orginate from VIP or regular customers. VIP calls must be responded to immediately but regular calls can be routed to a retrial queue if the operators are busy.

This study’s proposed model can easily reveal the optimal arrangement of operators while minimizing computational time and without losing any precision of the performance measure when dealing with a call center with more operators.

Based on the results of the comparison between the exact method and the proposed approximation method, the approach shows that the larger the number of operators or inbound calls, the smaller the error between the two methods.

This investigation presents a computational method and management cost function intended to identify the optimal number of operators for a call center. Because of computational limitations, many operators could not be easily analyzed using the exact method. For the manager of a call center, the sooner the optimal solution is found, the faster business strategies are deployed. This study develops an approximation method and compares it with the exact method.

The purpose of this paper is to address reported weaknesses with existing equipment reliability improvement methods through their integration into the Six‐Sigma DMAIC methodology.

The evaluation was done by assessing the weaknesses of traditional methods such as reliability centered maintenance (RCM), evaluating what Six‐Sigma could potentially offer to close the gaps, and testing potential improvements through an example application.

It is concluded that Six‐Sigma addresses many RCM flaws and weaknesses. It is also concluded that Six‐Sigma, if integrated with other reliability techniques, can produce results that are far more objective and dependable.

Six‐Sigma, however, still bears its own cons and limitations. It requires good data which are sometimes unavailable. Six‐Sigma is also lengthier and consumes more resources per single problem since it focuses at one problem at a time.

The introduction of Six‐Sigma into equipment reliability/maintenance applications is quite original since this methodology has traditionally been limited to manufacturing and only recently to administrative processes. The outcome is of significant value as it opens up a new perspective into the development of reliability improvement measures for plant equipment.

Accurate presentation of the rock microstructure is critical to the grain-scale analysis of rock deformation and failure in numerical modelling. 3D granite microstructure modelling has only been used in limited studies with the mineral pattern often remaining poorly constructed. In this study, the authors developed a new approach for generating 2D and 3D granite microstructure models from a 2D image by combining a heterogeneous material reconstruction method (simulated annealing method) with Voronoi tessellation.

More specifically, the stochastic information in the 2D image is first extracted using the two-point correlation function (TPCF). Then an initial 2D or 3D Voronoi diagram with a random distribution of the minerals is generated and optimised using a simulated annealing method until the corresponding TPCF is consistent with that in the 2D image. The generated microstructure model accurately inherits the stochastic information (e.g. volume fraction and mineral pattern) from the 2D image. Lastly, the authors compared the topological characteristics and mechanical properties of the 2D and 3D reconstructed microstructure models with the model obtained by direct mapping from the 2D image of a real rock sample.

The good agreements between the mapped and reconstructed models indicate the accuracy of the reconstructed microstructure models on topological characteristics and mechanical properties.

The newly developed reconstruction method successfully transfers the mineral pattern from a granite sample into the 2D and 3D Voronoi-based microstructure models ready for use in grain-scale modelling.

The purpose of this paper is to investigate how queueing theory has been applied to derive results for a Sparre Andersen risk process for which the claim inter‐arrival distribution is hyper Erlang.

The paper exploits the duality results between the queueing theory and risk processes to derive explicit expressions for the ultimate ruin probability and moments of time to ruin in this renewal risk model.

This paper derives explicit expressions for the Laplace transforms of the idle/waiting time distribution in GI/HE_r(k_i,λ_i)/1 and its dual HE_r(k_i,λ_i)/G/1. As a consequence, an expression for the ultimate ruin probability is obtained in this model. The relationship between the time of ruin and busy period in M/G/1 queuing system is used to derive the expected time of ruin.

The study of renewal risk process is mostly concentrated on Erlang distributed inter‐claim times. But the Erlang distributions are not dense in the space of all probability distributions and therefore, the paper cannot approximate an arbitrary distribution function by an Erlang one. To overcome this difficulty, the paper uses the hyper Erlang distributions, which can be used to approximate the distribution of any non‐negative random variable.

The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.