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It is essential for manufacturers to consider the interrelation among quality, inventory, and maintenance decisions to detect imperfect quality products, keep the production system in good operating condition, and manage quality and inventory costs. Hence, this paper aims to develop an integrated model of inventory planning, quality engineering, and maintenance scheduling in which the expected total cost per time unit is minimised by determining the sample size, sampling interval, control limit coefficient, along with production cycle time. In this regard, an imperfect multi-product manufacturing system is considered, in which the inventory shortage in satisfying the demand for each product type and the idle time during the production cycle are not allowed. It is assumed that the process starts in an in-control condition where most produced units are conforming. However, due to the occurrence of an assignable cause (AC), the process mean moves to an out-of-control condition in which a significant fraction of non-conforming units is produced. The efficiency of the proposed mathematical model is evaluated by a numerical example, and then the sensitivity of the proposed model to important inputs is analysed. Finally, a comparative study based on the Taguchi design approach is given to confirm the capability of the proposed model to achieve remarkable cost savings.

AbstractA process is in its stable state when it has sufficient precision and accuracy. The accuracy is expressed as the deviation of the process mean from its target while the low dispersion indicates the high process precision. Therefore, joint monitoring of multivariate process mean and variability ensures a high rate of producing items. This study proposes a multivariate generalized likelihood ratio scheme called DS-MGLR with two features: (1) considering the effect of imprecise observations, and (2) enhancing the chart sensitivity in reacting to process disturbances. Since the shift magnitude in the distribution parameters is not known in advance, the expected average run length is also calculated for both the MGLR and DS-MGLR charts. To reduce the impact of measurement errors, an improved version of the DS-MGLR chart based on multiple measurement approach is developed. Three comparative studies are presented to indicate the importance of the properties of the developed charts. The first study confirms the efficiency of the double sampling strategy, while the second study indicates the error effect. The third one shows the effectiveness of the multiple measurement approach in compensating for the error contamination. Finally, the applicability of the proposed chart is highlighted using a real data example. [ABSTRACT FROM AUTHOR]

AbstractToday, manufacturers face two challenges: the financial limitation of sampling and the large number of study quality characteristics. In such situations, the problem dimension is usually larger than the sample size, which is known in the literature of statistical quality control as a high dimensional process. The use of conventional multivariate charts to monitor the covariance matrix of such processes is not possible for two reasons: (1) the non-reversibility of the sample covariance matrix, (2) the possibility of the occurrence of sparse shifts in which only a few elements of the covariance matrix are affected. This paper focuses on Phase II monitoring of the covariance matrix of high-dimensional processes based on integrating the ridge penalized likelihood ratio (RPLR) statistic and the combined DS-VSS sampling method, in which DS and VSS features are used concurrently. The performance of the proposed DS-VSS RPLR control chart is compared with the RPLR one in terms of three metrics of the average run length, standard deviation of the run length, and expected value of sample size under three types of joint diagonal/off-diagonal, diagonal and off-diagonal shift patterns. The results show that the DS-VSS scheme is more sensitive than the RPLR in detecting both sparse and non- sparse changes. [ABSTRACT FROM AUTHOR]

Purpose: Cloud manufacturing is a service-oriented production model that centralizes production resources to respond to specific customer needs in different locations. One of the main issues in cloud manufacturing systems is the proper allocation of sub-tasks to enterprises and their optimal scheduling. Most studies assume that all tasks have only one type of structure, although it is possible to have tasks with different structures in one order set. Furthermore, existing scheduling models in the cloud manufacturing literature tend to assume that all tasks are available at time zero and that the logistics time/cost among enterprises is negligible. Therefore, in this study, an optimization model with three objective functions of task completion time, the cost imposed on the cloud manufacturing system, and the quality of the selected services are developed to get closer to the real world; three features are included in it: 1) the possibility of tasks with two structures, series and parallel, 2) different arrival times of tasks in the cloud manufacturing system, and 3) time/cost of logistics between different enterprises. Methodology: First, six examples with different numbers of tasks and subtasks are designed with both sequential and parallel structures. To solve the proposed model accurately and achieve the global optimum, the CPLEX solver is used in the GAMS software. Findings: To verify the importance of the characteristics of the developed model, two comparative studies are carried out. In the first comparative study, the presented model is compared with a similar model in which it is assumed that all tasks are available at time zero. The second comparative study examines the effect of considering logistics time/costs between enterprises when allocating subtasks to services. The results of the comparative studies show the misleading level of the cloud manufacturing manager in the face of the reduced models. Originality/Value: This research aims to present a model for the simultaneous scheduling of tasks with sequential and parallel structures, considering the different task arrival times and logistics in the cloud manufacturing system. [ABSTRACT FROM AUTHOR]

Occurring disruptions and faults violate the perfectness of a production process in determining Economic Production Quantity (EPQ). Integrating EPQ, Statistical Process Control (SPC), and maintenance terms has brought satisfactory outcomes for such an imperfect process. Under the violated independence assumption, the Autoregressive Moving Average (ARMA) chart has been applied in the integrated models. Under autocorrelation, the extended Acceptance control chart (ACC) has indicated significant reductions in costs. Nevertheless, it has not been applied to any integrated modeling yet. Until now, constant input factors have been considered for modeling imperfect and autocorrelated processes. Uncertainty in estimating those factors can deteriorate the effectiveness of models. We present the first robust design for imperfect and autocorrelated processes under uncertainty. We apply a particle swarm optimization algorithm to provide solutions. The investigations of simple and integrated modeling indicate that applying the ACCs causes better statistical and economic results. Also, a real-life application is presented. [ABSTRACT FROM AUTHOR]

Based on the manufacturer-consumer context, a two-dimensional preventive maintenance policy is developed for sold products under a two-dimensional warranty by pursuing two objectives: (1) minimization of the imposed warranty servicing cost on the manufacturer and (2) minimization of lost opportunity cost resulting from the product downtime. In addition, in order to adapt the model to real applications, we depart from the literature by taking into consideration the continuous nature of the usage rate variable. To illustrate the advantages of the model, three types of couple-based comparative models are made. Type I consists of two models with a one-dimensional preventive maintenance policy, while type II includes a model with no maintenance activity. Finally, type III involves two single-objective models. The results obtained from the comparison of the suggested model with the type I and II models confirm the significant effect of employing a two-dimensional preventive maintenance policy in improving both manufacturers’ and customers’ satisfaction. In addition, comparisons of the suggested bi-objective model with three types of models indicate the importance of taking into account both the warranty servicing cost and loss opportunity. [ABSTRACT FROM AUTHOR]

Recently, simultaneous monitoring of multivariate process mean and variability has received increasing attention in the literature of statistical process monitoring (SPM). However, the deleterious impact of parameter estimation on the capability of control charts designed for simultaneous monitoring of the mean vector and covariance matrix of multivariate processes has been clearly neglected. In this paper, we study the effect of estimation error on both in-control and out-of-control properties of the multivariate exponentially weighted moving average (EWMA)-based generalized likelihood ratio (MELR) chart. Simulation studies in terms of the average run length (ARL), the standard deviation of run length (SDRL), and the median run length (MRL) metrics are conducted to explore how the amount of Phase I reference samples affects the performance of the MELR chart. The results show that extra variability due to estimation error reduces the detecting capability of the MELR chart while increases its false alarm rate. Meanwhile, a real-life data is provided to illustrate poor Phase I estimation results in more false alarms than expected. [ABSTRACT FROM AUTHOR]

This research presents simultaneous design of production planning, maintenance strategy and control chart in which two issues are considered: (1) multiple assignable cause and (2) equipment failure. The first one means that various factors may shift the process to an out-of-control state; while the second one means that the equipment is not always available and may be suddenly failed. Also, to reduce the production costs, a rework process is performed on the nonconforming items right after the regular cycle. Furthermore, three types of maintenance activities are applied based on system conditions. The corrective maintenance is conducted when the equipment failure occurs. The preventive maintenance is accomplished when the process continues until the end of the cycle, and the predictive maintenance is performed whereas the chart issues a true alarm. To check the effectiveness of the model, three comparative studies are examined. The first one compares the suggested model with a similar model without the rework process; the second one demonstrates the importance of employing the statistical process monitoring; and the last one compares the model with a similar model without considering the equipment failure. The results show the superiority of the integrated model compared to the others in the cost-saving. [ABSTRACT FROM AUTHOR]

In recent years, adaptive control charts in which the design parameters depend on the observed samples have been successfully used as efficient alternatives for traditional control charts with constant parameters. In crisp run control rules, the process state may change very sharply from in-control to out-of-control conditions which increase the rate of false alarms. To overcome this drawback, this paper presents an adaptive Shewhart-type control chart, where the design parameters (sample size (n), sampling interval (h), and control limit coefficients (k and k r , s )) are defined with linguistic variables. To accomplish that, the chart parameters are determined based on the location of eight previous chart statistics using a set of fuzzy rules in a continuous environment. In order to improve the sensitivity of the proposed control chart in detecting small shifts in both location and scale parameters, the adaptive procedure is designed by integration of fuzzy Western Electric rules and fuzzy adaptive sampling rules. After designing the control charts using a fuzzy inference system (FIS), in order to provide an economic design of the proposed control chart, a tuned Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal values corresponding to membership functions of the control chart parameters. Finally, using simulation studies, the capability of the proposed control chart is analyzed and compared with common charts in the literature. The results confirm that under different shifts in location and scale parameters, the proposed control chart outperforms other charts in terms of both economic and statistical criteria. [ABSTRACT FROM AUTHOR]