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In real‐life applications maintenance managers often face complicated decision problems under uncertainty. This difficulty increases when they have to take conflicting objectives into account. A recent review of the literature shows that previous works consider repairable systems subject to random failures and analyse trade‐offs between the costs and the benefits of maintenance activities. The risk aversion of the maintenance decision maker may be not underlined enough. This paper aims to deal with a single component system that has to accomplish a series of missions of a given length.

The development of a maintenance strategy for this system is analysed from a risk aversion point of view. An attempt is made to highlight the attitude of a neutral decision maker versus a risk‐averse manager.

Presents a very simple framework to analyse the risk aversion effect on managers' decisions. The model confirms the observation that risk aversion implies no‐monotone relation between optimality frequencies of maintenance operations and the deformation rate of the breakdown probability.

Since the deformation rate is monotonic with time, the proposed model can be extended to derive optimal frequencies, which allow the implementation of the optimal deformation rates according to the probability law of the deformation rate δ.

In this paper, an optimal periodic replacement strategy is proposed. This strategy suggests new items to perform replacements at failure. Preventive replacements, scheduled at instants kT (k=1, 2,…) are carried out only if the item's age exceeds a threshold to be determined. Parameters T and b are derived from an optimization model aiming to maximize the steady state availability under budgetary constraints or to minimize the expected total cost per unit of time over an infinite horizon, while the steady state availability must be higher than some given threshold. Costs and durations associated with replacement actions are supposed to be known.

Employs mathematical models to investigate the expected cost rate and the steady state availability with illustrative examples.

Analytical and numerical results have been obtained for a system whose lifetime is distributed according to an Erlang distribution.

The proposed strategy seems more efficient than the basic block replacement strategy aiming to maximize the steady state availability. It is also easy to implement.

This new strategy would appear to be more efficient than the previous basic block replacement strategy.

The purpose of this study is to propose and model an integrated production‐maintenance strategy for a failure‐prone machine in a just‐in‐time context.

The proposed integrated policy is defined and a simulation model is developed to investigate it.

The paper focuses on finding simultaneously two decision variables: the period (T) at which preventive maintenance actions have to be performed; and the sequence of jobs (S). These values minimize the maintenance costs (M_C) and the expected total earliness and tardiness costs (ET_C) away from a common due‐date D.

The paper attempts to integrate in a single model the two main aspects of any manufacturing and production systems: production and maintenance. It focuses on a stochastic scheduling problem in which n immediately available jobs are to be scheduled jointly with the preventive maintenance. The effect of the period (T) and the sequence of job (S) on the expected total cost are shown through a numerical example.

The paper proposes an integrated model that links production, preventive maintenance and corrective maintenance. It is simultaneously focusing on the period (T) at which preventive maintenance actions have to be performed and the sequence of jobs (S) to reduce production and maintenance‐related costs.

The purpose of this paper is propose a methodology for monitoring industrial processes that cannot be stabilized, but are nevertheless capable.

The proposed procedure uses the C_P(u,v) family of capability indices proposed by Vännman (including the indices C_PK, C_PM, C_PMK) combined with one‐sided two‐out‐of‐three and three‐out‐of‐four run rules strategies.

This paper introduces a new strategy, where capability indices are monitored in place of the classical sample statistics like the mean, median, standard deviation or range.

When doing a capability analysis it is recommended to first check that the process is stable, e.g. by using control charts. However, there are occasions when a process cannot be stabilized, but is nevertheless capable. Then the classical control charts fail to efficiently monitor the process position and variability. The approach suggested in this paper overcomes this problem.

The experimental results presented in this paper demonstrate how the new proposed approach efficiently monitors capable processes by detecting decreases or increases of capability level.

The purpose of this paper is to propose a framework to identify all the feasible disassembly sequences for a multi‐component product and to find an optimal disassembly sequence, according to specific criteria such as cost, duration, profit, etc.

Taking into account topological and geometrical constraints of a product structure, an AND/OR disassembly graph is built. Each graph node represents a feasible subassembly. Two nodes i and j are connected by an arc (i, j), called a transition, if the subassembly j can be obtained from the subassembly i by removing one or several connectors. Constraint programming approach is used to generate the feasible subassemblies and related transitions.

If a cost z_ij is incurred to perform a transition (i, j), an optimal disassembly sequence can be generated for a given subassembly, using the shortest path algorithm or a linear programming model.

The proposed approach performs very well compared to other approaches published in the literature, even when applied to products requiring parallel disassembly and including a large number of parts.

This approach has been successfully applied to assess the wheelchair maintainability at the design stage and will be implemented in CAD systems. One other application, regarding the disassembly process and total revenue maximization for product recycling, is now under consideration.

Applying constraint programming to efficiently generate the set of the feasible subassemblies constitutes the main contribution in this paper. This process is the hardest step in the disassembly sequencing problem.

Process capability indices (PCI) are frequently used in order to measure the performance of production processes. In their 2005 article, Castagliola and Castellanos proposed a new approach for the estimation of bivariate PCIs in the case of a bivariate normal distribution and a rectangular tolerance region. This paper proposes extending Castagliola and Garcia‐Castellanos's paper to the estimation of bivariate PCIs in the case of non‐normal bivariate distributions.

The proposed method is based on the use of Johnson's System of distributions/transformations in order to transform the bivariate non normal distribution into an approximate bivariate normal distribution. Numerical examples are presented and some criteria are given in order to choose the appropriate Johnson's distribution.

The proposed method is only dedicated to the case of two quality characteristics and a rectangular tolerance region (the most common case).

The proposed method allows the evaluation of bivariate capability indices irrespective of the distribution of the data and thus allows obtaining more reliable estimates for these values.

The main originality of the method presented in this paper is its ability to compute bivariate capability indices when the distribution of the data is not a bivariate normal distribution, i.e. the general case.

The purpose of this paper is to apply a recent kind of neural networks in a reliability optimization problem for a series system with multiple‐choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget and weight. The problem is formulated as a non‐linear binary integer programming problem and characterized as an NP‐hard problem.

The design of neural network to solve this problem efficiently is based on a quantized Hopfield network (QHN). It has been found that this network allows one to obtain optimal design solutions very frequently and much more quickly than other Hopfield networks.

For systems more complex than series systems considered in this paper, the proposed approach needs to be adapted. The QHN‐based solution approach can be applied in many industrial systems where reliability is considered as an important design measure, e.g. in manufacturing systems, telecommunication systems and power systems.

The paper develops a new efficient method for reliability optimization. The most interesting characteristic of this method is related to its high‐speed computation, since the practical importance lies in the short computation time needed to obtain an optimal or nearly optimal solution for large industrial problems.

The purpose of this paper is to provide some heuristic and meta heuristic tools to aid airline companies in flight planning while taking into account maintenance planning. The objective is to maximize aircraft utilization before the maintenance interventions and to smooth the long‐term flight load of the aircraft so that maintenance checks are regular for all the fleet.

The proposed methods, based on operational research solution technics, build a flight planning for an airline company. The simulation of the proposed methods is observed over 40 weeks and evaluated with different problem instances and maintenance policies.

The longest path based method with increasing priority and the simulated annealing are shown to have the best aircraft utilization results.

Further research could propose some methods which build simultaneous maintenance and flight planning.

The economic value and legal considerations of maintenance activities in the airline industry show the importance of maximizing aircraft utilization. The proposed methods are compared to decide the best maintenance policy. These methods are simple and efficient.

This paper provides a connection between an industrial problem of aircraft maximization under maintenance constraints and operational research. Simple but efficient methods are evaluated in terms of two criteria: aircraft maximization and flight load smoothing.