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Analyses a split production system which has a single station in the first stage followed by two parallel stations in the second stage, in connection with the efficiency of the system. Develops a mathematical model using semi‐regenerative processes to study the transient behaviour of the system. Obtains systems of convolution integral equations satisfied by various state probabilities and uses an iterative numerical technique to solve these systems of convolution integral equations to evaluate various measures of system performance.

Analyses an n‐unit cold‐standby system with a single repair facility under the assumption that the failure and repair times are arbitrarily distributed. Develops a mathematical model using semi‐regenerative processes. Obtains a system of integral equations satisfied by various state probabilities corresponding to different initial conditions. Obtains explicit expressions for the expected number of failures and expected number of visits to down state in a specified interval of time. Discusses two special cases when either the failure or repair rate is constant. Analyses numerically and compares particular cases with one, two and three units by assuming Weibull distribution for failure time and normal distribution for repair time.

High reliability, maintainability, safety and supportability are expected from today’s modern systems. In the recent years supportability has been widely accepted as a major factor in logistics discipline. The main purpose of this paper is to demonstrate how advanced mathematical models can be used to analyse the effect of supportability on systems availability. The paper discusses supportability aspects and its effect on operational availability of complex systems using advanced mathematical models like Markov, semi‐Markov and non‐Markov models. The powerful mathematical models discussed in the paper would help reliability engineers and practitioners to predict the logistic support requirements to achieve specified operational availability.

A merge production system which has parallel stations in the first stage followed by a single station in the second stage is analysed in connection with the production rate of the system. A mathematical model is developed using semi‐regenerative process and a system of convolution integral equations satisfied by various state probabilities is obtained. An iterative numerical technique is used to solve the system of integral equations obtained to evaluate the production rate of the system.

The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.

This research provides the identification of important inputs and outputs for six sigma projects that are then analyzed using data envelopment analysis (DEA) to identify projects, which result in maximum benefit. Maximum benefit here provides a Pareto optimal solution based on inputs and outputs directly related to the efficiency of the six sigma projects under study. A sensitivity analysis of efficiency measurement is also carried out to study the impact of variation in projects' inputs and outputs on project performance and to identify the critical inputs and outputs.

DEA, often used for relative efficiency analysis and productivity analysis, is now successfully constructed for six sigma project selection.

Provides a practical approach to guide the selection of six sigma projects for implementation, especially for companies with limited resources. The sensitivity analysis discussed in the paper helps to understand the uncertainties in project inputs and outputs.

This paper introduces DEA as a tool for six sigma project selection.

Discusses two measures, namely, maintenance free operating period (MFOP) and failure free operating period (FFOP) that are seen as the future reliability and maintenance measures. The concept of MFOP is a new reliability metric driven by the Ministry of Defence, UK, which is becoming popular among projects such as future offensive aircraft systems (FOAS), joint strike fighter (JSF) and ultra reliable aircraft (URA) etc. Analyses how these two measures can change the whole operation of military and commercial aircraft operations.

In this paper reliability models for fault tolerant N‐version programming and consensus recovery block (combination of N‐version programming and recovery block) with deadline mechanism are analysed. Explicit expressions for reliability of N‐version programming and consensus recovery block with exponential execution times are derived. The paper also presents optimisation models for real time N‐version programming. The objective of the optimisation problem is to maximise reliability of the software satisfying a budget constraint. The paper also includes efficient branch and bound procedure that can be used to solve the optimisation problem.

Derives expressions for performance availability and performance reliability of fault tolerant systems operating under various levels of stress during a mission using stochastic reward models (SRM). Develops a mathematical model using semi‐regenerative reward model. Obtains a system of convolution integral equations satisfied by various state probabilities corresponding to different initial conditions. As a particular case, analyses a 3‐unit parallel system under three different stress levels. Uses an iterative numerical method to solve the system of integral equations satisfied by various state probabilities to study the effect of parameters on various system performance.

Proposes optimization models for spare provisioning. In the first model, considers a series system with m components where each component can have a maximum of (n‐1) spares. The objective function is to maximize the availability of the system satisfying a constraint on space required for the spares. In the second model, considers a series‐parallel system where each component of the system can have a maximum of (n‐1) spares. The optimization models developed in the paper can be solved using general purpose software such as SOLVER of EXCEL. Also presents an efficient branch and bound algorithm which can be used to solve the optimization problem.

Driven by motivation of quality enhancement and brand reputation promotion, automotive industries try to improve product quality and customer satisfaction by performing quality pilot programs continuously. The purpose of this paper is to develop a dynamic model to select the improvement quality pilot program from competitive candidates based on dynamic customers’ feedback.

An extended dynamic multi-criteria decision-making method is developed by embedding dynamic triangular fuzzy weighting average operators into fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and the novel evaluation indicator “ζ” is introduced to reflect prioritization performance.

The two evaluation indicators (Q and “ζ” ) assist quality managers to identify the best program with respect to multiple conflicting criteria and the best choice based on these two indexes shows high conformity. Besides, ranking sequences obtained by “ζ” can avoid the dilemma that there are several candidates with top priority calculated by comprehensive group utility value Q.

The dynamic MCDM method has been applied into the quality improvement procedure in Chinese domestic auto factories and contributes to highly efficient promotion.

Few dynamic models on pilot program selection for quality improvement based on dynamic customers’ feedback, this study deals with the dynamic promotion by an extended fuzzy VIKOR method and presents a case application.