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The purpose of this paper is to review the literature on maintenance optimization models and associated case studies. For these optimization models critical observations are made.

The paper systematically classifies the published literature using different techniques, and also identifies the possible gaps.

The paper outlines important techniques used in various maintenance optimization models including the analytical hierarchy process, the Bayesian approach, the Galbraith information processing model and genetic algorithms. There is an emerging trend towards uses of simulation for maintenance optimization which has changed the maintenance view.

A limited literature is available on the classification of maintenance optimization models and on its associated case studies. The paper classifies the literature on maintenance optimization models on different optimization techniques and based on emerging trends it outlines the directions for future research in the area of maintenance optimization.

The paper provides many references and case studies on maintenance optimization models and techniques. It gives useful references for maintenance management professionals and researchers working on maintenance optimization.

In hospitals, several patient flows compete for access to shared resources. Failure to manage these flows result in one or more disruptions within a hospital system. To ensure continuous care delivery, solving flow problems must not be limited to one unit, but should be extended to other departments – a prerequisite for solving flow problems in the entire hospital. Since most current studies focus solely on overcrowding in emergency units, additional insights are needed on system-wide patient flow management. The purpose of this paper is to look at the information available in system-wide patient flow management studies, which were also systematically evaluated to demonstrate which interventions improve inpatient flow.

The authors searched PubMed and Web of Science (Core Collection) literature databases and collected full-text articles using two selection and classification stages. Stage 1 was used to screen articles relating to patient flow management for inpatient settings with typical characteristics. Stage 2 was used to classify the articles selected in Stage 1 according to the interventions and their impact on patient flow within a hospital system.

In Stage 1, 107 studies were selected. Although a growing trend was observed, there were fewer studies on patient flow management in inpatient than studies in emergency settings. In Stage 2, 61 intervention studies were classified. The authors found that most interventions were about creating and adding supply resources. Since many hospital managers these days cannot easily add capacity owing to cost and resource constraints, using existing capacity efficiently is important – unfortunately not addressed in many studies. Furthermore, arrival variability was the factor most frequently mentioned as affecting flow. Of all interventions addressed in this review, the most prominent for advancing patient access to inpatient units was employing a specialized individual or team to maintain patient flow and bed placement across hospital units.

This study provides the first patient flow management systematic overview within an inpatient setting context.

This paper proposes the extension of a prognosis process by means of the integration of maintenance alternative impacts in order to develop a maintenance decision‐making tool.

The deployment of this extended prognosis process follows a methodology based both on probabilistic and on event approaches.

The importance of the maintenance function has increased due to its role in keeping and improving the system availability and safety but also the product quality. To support this new role, the maintenance concept has undergone several major developments to lead to proactive considerations mainly based on prognosis process allowing one to select the best maintenance plan to be carried out.

Studies over the last 20 years have indicated that around Europe the direct cost of maintenance is equivalent to between 4 and 8 per cent of total sales turnover. The indirect cost of maintenance is likely to be a similar amount. Thus, in the countries where modern maintenance practices have yet to be well adopted by industry, the potential savings from modern maintenance are massive. These modern and efficient maintenances imply identifying the root‐cause of component failures, reducing the failures of production systems, eliminating costly unscheduled shutdown maintenances, and improving productivity as well as quality. It means, for the companies, migrating from their traditional reactive approach, which is “fail and fix”, to “predict and prevent”. The advantage of the latter is that maintenance is performed only when a certain level of equipment deterioration occurs. This “proactive” maintenance is mainly based on prognosis process often considered as the Achilles heel, while its goal is fundamental for implementing anticipation capabilities. This paper looks into this issue by proposing the development of an innovative prognosis process integrating the modelling of maintenance actions and their impacts on system performances. It leads to offering a maintenance aided decision‐making tool cable of assisting the decision‐maker in selecting the best maintenance plan to be carried out.

The feasibility of this new prognosis is experimented on the manufacturing Tele‐Maintenance (TELMA) platform supporting the unwinding of metal bobbins.

The purpose of this paper is to demonstrate the potential of simulation approach for performance evaluation in a complex environment with a case of application from Indian Nuclear Power Plant.

In this work, stochastic simulation approach is applied to availability evaluation of AC Power supply system of Indian Nuclear Power Plant (INPP). In the presently followed test, maintenance policies on diesel generators and circuit breakers are considered to exactly model the practical scenario. System success logic incorporates the functional dependencies and dynamics in the sequence of operations and maintenance policies. In each iteration (random experiment), from simulated random behaviour of the system, uptime and down time are calculated based on system success logic. After sufficient number of iterations, unavailability and other required reliability measures are estimated from the results.

The subsystems of AC Power Supply System of NPP are having multi‐states due to surveillance tests and scheduled maintenance activities. In addition, the operation of DG involves starting and running (till its mission time) which is a sequential (or conditional) event. Furthermore, the redundancies and dependencies are adding to the complexity.

This paper emphasizes the importance of realistic reliability modelling in complex operational scenario with Monte‐Carlo simulation approach. Simulation procedure for evaluating the availability/reliability of repairable complex engineering systems having stand‐by tested components is presented. The same simulation model finds application in importance measures calculation, technical specification optimization and uncertainty quantification.