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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 introduce and investigate the performances of a new CUSUM‐S² control chart designed to monitor the sample variance of samples from a normally distributed population.

The proposed chart monitors a statistic computed as a logarithmic transformation of the sample variance; the introduction of the sample variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.

A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S² chart has been assessed through a comparison with an EWMA‐S² control chart proposed in the literature to monitor the process dispersion.

The paper only deals with uncorrelated normally distributed data.

The obtained results show how the CUSUM‐S² chart is particularly suitable when reduction in the process dispersion should be detected by means of subgroups having limited sample sizes.

The paper shows the new CUSUM‐S² control chart allows a decreasing of the variability to be detected faster than the corresponding EWMA‐S² control chart proposed earlier in the literature.

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 use of control charts to monitor a product quality characteristic requires the selection of their design parameters. To select feasible design parameters, the constraints related to the inspection resources available at the workstation, its configuration and the process operating parameters should be taken into account. The aim of this research is to discuss the design of Shewhart and EWMA control charts monitoring dispersion in the presence of these constraints and for processes characterized by a random shift size.

In this paper the design of the investigated control charts has been intended in a broader sense as the design of a local inspection procedure constrained by a set of resource/environmental process factors characterizing the workstation configuration. An economic objective pursuing the minimization of the total inspection cost has been considered.

Constraining the design of a control chart is an essential issue to find chart design parameters that can be effectively implemented by quality practitioners. The influence of the process‐operating parameters has been modelled and some guidelines have been suggested through the use of contour plots.

The economic design of control charts has been scarcely implemented by quality practitioners due to the difficulty of adapting them to the multifaceted complexity and constraints present within manufacturing environments. This paper tries to contribute to the existing literature by improving the available mathematical models through modelling the actual workstation configuration and resource allocation; furthermore, for random shift processes it investigates the performance of the Shewhart and EWMA control charts monitoring process dispersion.

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.

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.

The purpose of this study is to propose and model an inspection and preventive maintenance policy for randomly failing systems that alternate operating and idle periods according to their mission profile.

A maintenance policy is defined and modeled mathematically. The paper focuses on finding the age T for inspection which maximizes the stationary availability of the system.

Except for the case of only self‐announcing failures, there always exists a finite optimal strategy T*. Two sufficient conditions for the uniqueness of such an optimum are also derived.

Many productive systems alternate operating and inactive periods, their failures may be self‐announcing or not self‐announcing (detected only through inspection). This paper presents a maintenance strategy for such systems in order to maximize their stationary availability. The proposed strategy suggests submitting the system to inspection when its age reaches T units of time.

This paper states a general expression of the system stationary availability which is considered as the performance criterion. Conditions of existence and uniqueness of an optimal strategy are developed.

The purpose of this paper is to present computer‐generated combined arrays as efficient alternatives to Taguchi's crossed arrays to solve robust parameter problems.

The alternative combined array designs were developed for the cases including six to twelve variables where CMR designs are not smaller than Taguchi's designs. The efficiency to estimate the effects of interest was calculated and compared to the efficiency of the corresponding CMR designs.

For all the cases investigated at least one computer generated combined array design was found with the same size as the CMR design and with higher efficiency.

Robust parameter design identifies appropriate levels of controllable variables in a process for the manufacturing of a product. The designed experiments involve the controllable variables along with the uncontrollable or noise variables to design a product or process that will be robust to changes in these noise variables. Response surface methodology estimates the actual relationship between the response and the input variables with an empirical model based on the designed experiment. Once the empirical model is fitted, the surface described by the model can be used to describe the behavior of the response over the experimental region. The higher efficiency of the computer generated combined array designs proposed in this research produces lower variances for the parameter estimates and lower variance of prediction for the model. As a result, the response will be described in a more realistic form.

The paper shows that using a computer‐generated design to solve a robust parameter problem will result in a better approximation to the true response of the process. Consequently, optimizing the fitted model will produce settings for the parameters closer to the real optimal settings.