Search results

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 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.

The purpose of this paper is to formulate a new problem of the optimal design of a series manufacturing production line system, and to develop an efficient heuristic approach to solve it. The optimal design objective is to maximize the efficiency subject to a total cost constraint.

To estimate series production line efficiency, an analytical decomposition‐type approximation is used. The optimal design problem is formulated as one of combinatorial optimization where the decision variables are buffers and types of machines. This problem is solved by developing and demonstrating a problem‐specific ant system algorithm. Numerical examples illustrate the effectiveness of the algorithm.

It has been found that this algorithm can always find near‐optimal or optimal solutions quickly. The approach developed in this paper for manufacturing lines can be adapted for power systems and telecommunication systems.

The paper presents a new approach for the optimal design of buffered series production lines. This optimization approach aims at selecting both the machines and the levels of buffers. The paper also develops an efficient solution approach based on the ant system meta‐heuristic.

The purpose of this article is to consider a corrective and preventive maintenance model with a view to both minimizing cost and maximizing system availability.

The proposed experimental multiobjective approach combines a simulation model and a statistical method to determine the best system parameters. The desirability function is used to convert a multiresponse problem into a maximization problem with a single aggregate measure. The model examined is based on a m identical machines system subject to unpredictable breakdown and repair, and the maintenance strategy used is based on the existing block‐replacement policy, which consists in replacing components upon failure or preventively, at scheduled intervals (T). Spare part inventory management is based on the (S, Q) model, whereby an order is placed when the replacement stock level drops below a given safety threshold level (S). At that time, a replacement part quantity (Q) is ordered, and is received after a stochastic lead time (τ).

The proposed model jointly minimizes the overall maintenance cost and maximizes system availability using a multiobjective optimization desirability function.

The multiobjective model can be used in a real manufacturing environment to help business decision makers determine the best compromise system parameters and adjust them to obtain desired response variables (overall production cost and system availability).

The proposed model allows the simultaneous optimization of two response variables, and determines the best system parameter compromise between the system cost minimization and the system availability maximization.

The purpose of this paper is to extend the optimal design problem of series manufacturing production lines to series‐parallel lines, where redundant machines and in‐process buffers are both included to achieve a greater production rate. The objective is to maximize production rate subject to a total cost constraint.

An analytical method is proposed to evaluate the production rate, and an ant colony approach is developed to solve the problem. To estimate series‐parallel production line performance, each component (i.e. each set of parallel machines) of the original production line is approximated as a single unreliable machine. To determine the steady state behaviour of the resulting non‐homogeneous production line, it is first transformed into an approximately equivalent homogeneous line. Then, the well‐known Dallery‐David‐Xie algorithm (DDX) is used to solve the decomposition equations of the resulting (homogenous) line. The optimal design problem is formulated as a combinatorial optimisation one where the decision variables are buffers and types of machines, as well as the number of redundant machines. The effectiveness of the ant colony system approach is illustrated through numerical examples.

Simulation results show that the analytical approximation used to estimate series‐parallel production lines is very accurate. It has been found also that ant colonies can be extended to deal with the series‐parallel extension to determine near‐optimal or optimal solutions in a reasonable amount of time.

The model and the solution approach developed can be applied for optimal design of several industrial systems such as manufacturing lines and power production systems.

The paper presents an approach for the optimal design problem of series‐parallel manufacturing production lines.

With continuous technological development in the twenty‐first century, the industry and industrial systems have become complex and making their availability more critical. In this context, the product support and its related issues such as spare parts play an important role. Lack of timely or incomplete support, such as the lack of spare parts when required, is likely to cause unexpected downtimes, which in turn often lead to incompensatable losses. Therefore the importance of predicting the correct support to keep the system functionally available needs to be emphasized. Required number of spare parts could be obtained based on technical and life parameters. This paper seeks to examine the system reliability and operating environment, which are the two parameters to be considered in this article.

A model is provided in this paper to determine the number of required spare parts with respect to the effect of the external factors, except time, on the reliability characteristics of components through the proportional hazard model. The model is verified with estimation of the number of spare hydraulic jacks, used on a load‐haul‐dump (LHD) machine, as non‐repairable components. The reliability of this non‐repairable part and its operational impact are assessed, while considering environmental factors and ignoring them.

The results indicate that the operating environment of system/machine has considerable influence on system performance. Forecasting the required support/spare parts based on technical characteristics and the system‐operating environment is an optimal way to prevent unplanned stoppages.

The environmental conditions in which the equipment is to be operated, such as temperature, humidity, dust, road conditions, maintenance facilities, maintenance crew training, operators' skill, etc., often have considerable influence directly on the system/machine or component reliability and indirectly on the product supportability characteristics. Spare parts, are classified as a product support item whose availability is important when planned or unplanned maintenance is to be carried out. Forecasting the required number of spare parts, based on technical characteristics and operating environmental conditions of a system, is one of the best ways to optimize unplanned stoppages.

Previously, the state of the specific technology and other factors have demonstrated the need for support in enhancing system effectiveness and preventing unexpected downtime. This paper sets the required number of spare parts necessary to fulfil this need.

It is assumed that the production system responds to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by equipment failures. The difficulty of controlling this type of production system resides in the variable nature of the remanufacturing process. In practice, remanufacturing operations for planned demand can be executed at different rates, referring to different component replacement and repair strategies. A sub‐optimal control policy in which inventory thresholds trigger the use of different execution modes has been formulated in previous research to address this problem when unplanned demands are processed under an exponential time distribution. The aim of this study is to extend this control policy to more realistic unplanned demand arrival and processing times distributions.

The proposed approach is based on a combination of analytical modeling, simulation experimentation and regression analysis. The model was validated by comparing the obtained simulation results with those obtained under an exponential processing time distribution.

The results demonstrate that the structure of optimal control can be approximated by the sub‐optimal multiple hedging point policy with non‐significant cost variations.

The simulation results demonstrate that hedging point control policies could be applicable to a wide variety of complex remanufacturing problems in which analytical solutions are not easily obtained.

The paper extends the concept of hedging point policy to the control of real‐word repair and remanufacturing operations. Once calculated, the sub‐optimal policy parameters can be simply implemented by practitioners through the definition of stock‐level parameters.

This paper aims at exploring the challenges of introducing a model integrating the Cost of Quality (COQ) into the modeling of a supply chain network.

This paper introduces a comprehensive supply chain model that minimizes a series of costs, in which COQ is integrated.

The scenario of incorporating COQ in supply chain network design will ensure the lowest overall cost, because it reduces the probability of defects and hence the probability of additional cost which might be due to corrective action.

With many industries today on the quest of improving their quality systems, finding ways to reduce nonconformities and failure of products is crucial. In industries such as the aerospace industry, the variable production cost is high; hence producing extra parts to compensate for defectives would be a costly option.

While COQ is a very good indicator of how much poor quality is costing a company, no work has been published in regard to integrating COQ into supply chain modeling.

The purpose of this paper is to investigate tractor maintenance practices and costs in Nigeria with a view to making replacement decisions and for overall farm budgeting.

The information was obtained via a structured questionnaire. Tractor owners were grouped into three categories: government agricultural settlements, established farms, and individual farms. Data used for analysis were collected from 60 farms located in major farming centers of the southwestern part of Nigeria. The report includes the formulation of repair equations, which relate tractor repair cost as a percentage of initial purchase prices to cumulative hours of use. Life cycle costs of three commonly‐used tractors were also estimated. The derived equations, which showed that the repair costs per hour increased with hours of use, were used to obtain the life cycle cost of each tractor model in different tractor owners' farms.

The proposed equation was found to be the best fit with coefficient of determination (R²) ranging from 0.992 to 0.996 for tractors in the three user categories. The trend observed in the life cycle costing of the three common tractors gave the general picture of when to replace each of the tractors based on the strategies being adopted by the tractor owners.

The results of this study would serve as baseline information to tractor owners regarding optimum use of tractors for minimizing repair and maintenance cost per hour and for making replacement policy. It would also be helpful to government and other agencies for future planning in the provision of tractor services to the farmers at relatively lower repair and maintenance cost.

Proves that a reduction in repair costs by careful operation and adequate maintenance could result in a significant reduction in tractor ownership costs.