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Reliability evaluation of healthcare services has been a challenging task for both operations managers and system engineers working in the respective field. The purpose of this paper is to develop a data envelopment analysis-based reliability allocation model.

A two-phase optimization scheme for the reliability evaluation and allocation of homogeneous system entities, namely, hospitals, operating in a healthcare network is proposed. First, reliability evaluation is performed nonparametrically through the frontier estimation technique data envelopment analysis by considering several failure modes and failure free discharged patients as the inputs and output of the service system. Subsequently, optimal reliability allocation that maximizes the overall network reliability subject to a budget constraint is carried out by utilizing weights of the inputs and output calculated on the Pareto optimal frontier, which is constructed from the most reliable hospitals operating in the network.

The popular performance assessment methodology DEA is found to be an invaluable reliability assessment and allocation tool, where optimal weights of the associated envelopment model, under certain budget restrictions, are used to maximize overall network reliability.

An empirical illustration of the proposed model is presented on a set of hospital network data from Turkey. Modeling implications can be carried out on similar service operations where identification of the critical performance indicator costs is possible.

The purpose of this paper is to consider a nonlinear problem of minimizing the cost of providing reliable systems. The authors assume that the system consists of several components in series, and for each such component, the cost of the component increases exponentially with its reliability.

In order to solve this nonlinear optimization problem, the authors propose two approaches. The first approach is based on the concept of adjusting the reliability of a pair of components to minimize the cost of the system. The authors call this procedure as reliability adjustment routine (RAR). Proofs of optimality and convergence for the proposed model are also provided. The second approach solves the problem by using a Lagrangian multiplier. A procedure is developed to obtain the maximum step size to achieve the desired optimal solution in minimum iterations. Proposed approaches are efficient and give exact solutions.

Proposed methods enable a decision maker to allocate reliability to the components in series while minimizing the total cost of the system. The developed procedures are illustrated using a numerical example. Although an exponential relationship between the component cost and reliability is assumed, this can be extended to various other nonlinear distributions.

This cost optimization problem, subject to system component reliability values, assumes the near practical nonlinear pattern of cost vs reliability. Such problems are complex to solve. The authors provide a unique approach called RAR to solve such convoluted problems. The authors also provide an approach to solve such problems by using a Lagrangian multiplier method. Various proofs have been worked out to substantiate the work.

The process by which the system failure allowance is allocated in some logical manner among its subsystems is termed Reliability Allocation. Many methods are available for such an allocation for series system but no method exists in case the system is non‐series‐parallel. In this article, the optimum allocation of reliability among its subsystems for general non‐series‐parallel systems has been discussed by extending the Minimum Effort Method which in its present form is applicable for series systems only. A number of effort functions are listed with a view to finding one which is suitable for application in this method and the same has been used for further calculations.

The purpose of this paper is to deal with the allocation requirements of the dependability of the multiphase systems.

It consists of a proposal for a combined methodology based on the simultaneous use of decomposition systems and reliability allocation method.

In the developed methodology, the authors use the principles of risk assessment and propose a new formulation of weight allowance with reference to the structural‒functional dependence.

The suggested methodology provides invaluable help to implementation process analysis.

The adopted allocation approach is based on the use of a three-dimensional model: temporal, structural and functional decomposition of systems.

A large number of research articles have appeared in the literature during the last two decades on the subject of system reliability optimisation, each with a view to providing simple, exact and efficient techniques. Here, an efficient, fast and exact technique is proposed for solving integer‐programming problems that normally arise in optimal reliability design problems. The algorithm presented is superior to any of the earlier methods available so far, being based on functional evaluations and a limited systematic search close to the boundary of resources. Thus it can quickly solve even very large system problems. It can also be effectively used with other operations research problems involving integer‐programming formulations.

This paper aims to present a simulated annealing (SA) algorithm to search the optimal solution of reliability‐redundancy allocation problems (RRAP) with nonlinear resource constraints.

The developed SA algorithm is coded in C++ and is applied to reliability design problems which include the series system (P1(a) and P1(b)), the series‐parallel system (P2), and the complex (bridge) system (P3). The numerical experiments are executed on an IBM‐PC compatible with a Pentium IV 2.0 GHz. The results are compared with those of previous studies.

The SA algorithm can find better solutions comparable to the previous studies in all problems except the problem P1(b). The difference on the order of 10⁻⁴ between the best and worst for all problems indicates good solution convergence of the SA algorithm. Note that the CPU times for these problems are within a few seconds by Pentium IV 2.0 GHz (P1(a) =2.78 sec, P1(b) =3.37 sec, P2=1.38 sec, and P3=1.40 sec).

The application of the SA is expanded to the RRAP, which can help reliability engineers design the system reliability.

This paper aims to focus on solving highly constrained redundancy optimization problems in binary complex systems.

The proposed algorithm searches a possibly improved solution in the k‐neighborhood (k≥2) of the current best feasible solution, by adding one unit in a selected subsystem and eliminating one from some other subsystem(s).

The algorithm is tested on complex system structures from the literature by solving a set of problems (with both linear and non‐linear constraints), with given and randomly generated data. It is observed that, compared with the other existing heuristics, there is much overall improvement in various performance measures.

The proposed algorithm is a better alternative and can be easily and efficiently applied to numerous real life systems such as computer and communication systems, telecommunication networks, automobile, nuclear and defense systems etc., giving optimal/near‐optimal solutions.

Researchers in reliability optimization have placed emphasis on heuristic approaches. The paper presents a new heuristic algorithm for solving the constrained redundancy optimization problems in complex binary systems.

The purpose of this research is to show that reliability analysis and its implementation will lead to an improved whole life performance of the building systems, and hence their life cycle costs (LCC).

This paper analyses reliability impacts on the whole life cycle of building systems, and reviews the up‐to‐date approaches adopted in UK construction, based on questionnaires designed to investigate the use of reliability within the industry.

Approaches to reliability design and maintainability design have been introduced from the operating environment level, system structural level and component level, and a scheduled maintenance logic tree is modified based on the model developed by Pride. Different stages of the whole life cycle of building services systems, reliability‐associated factors should be considered to ensure the system's whole life performance. It is suggested that data analysis should be applied in reliability design, maintainability design, and maintenance policy development.

The paper presents important factors in different stages of the whole life cycle of the systems, and reliability and maintainability design approaches which can be helpful for building services system designers. The survey from the questionnaires provides the designers with understanding of key impacting factors.

The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R&M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R&M parameters to reduce life cycle cost.

From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R&M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters.

The process depicted in the paper is followed for a contemporary helicopter and substantial amount of life cycle cost reduction is observed with improvement of R&M parameters.

The benefits of this methodology not only reduce life cycle cost but also improve the availability/serviceability through less failure and less time for scheduled maintenance. The methodologies also provide the reliability trends indicating potential area for design improvement.

The proposed approach assists asset managers to reduce the life cycle costs through improvement of R&M parameters.

The technological level achieved by manufacturing systems in recent years has caused an exponential enlargement of problems to be faced by management, such as cost minimisation, reliability and maintainability allocation, diagnostic system design. A newly developed model for assessing the impact of failures of equipment (subject to multiple repair modes) on system production capability is presented. It is based on the numerical simulation of delay times in the production flow and enables the analyst to consider alternative and realistic policies of failure management, different starting periods and ending procedures of a finite production phase. Some examples of series manufacturing systems, with and without buffer, are analysed and sensitivity analyses are performed to assess the influence of variations of reliability and/or maintainability characteristics of the equipment on the system performances.