Journal of Quality Engineering and Management

Abstract  The issue studied in this paper, considering redundancy allocation and buffer allocation problems simultaneously in a series-parallel production system. The purpose of this research is to improve the availability, total system costs and buffer capacity through determining the optimal buffers size between work stations, selecting high reliability machines and assigning them to work stations, and developing a proper maintenance and repair plan. In this paper, the preventive and emergency repairs to machines are allowed and their cost is considered in the cost function. Furthermore, it is assumed that machine failure rates are random and follows a distribution function such as Weibul. Given these assumptions, it is very difficult to obtain the availability and cost functions via mathematical relations, explicitly. Thus, a hybrid simulation, design of experiments, and neural network approach are applied to estimate the availability and cost functions. In order to analyze the proposed model, a numerical example was used
 and based on the proposed methodology, was analyzed and evaluated. The model related to the problem was coded and solved by the NSGA-II algorithm and the Pareto set of answers was obtained. The results of the research indicated the validity of the proposed methodology for the problem under study. 

Abstract The purpose of this paper is to design a multi-objective mathematical programming model for scheduling multifunctional machines in a production cell. For this purpose, a multiobjective invasive weed algorithm was proposed and its solution results were compared with multi-objective and genetic particle swarm algorithms. Algorithm parameters were adjusted according to Taguchi method. The innovation of this article, on the one hand, is in implementing the idea of machine processing speed in the production of parts with different qualities. In other words, to ensure quality, processing speed and loading rate are adjusted in the machine, and on the other hand, a multi-objective algorithm with a new chromosome structure was designed to optimize the model. To analyze the performance of solution algorithms, thirty sample problems with different dimensions were designed and performed ten times each. The analysis of the results showed that the multi-objective invasive weed-based algorithm was able to solve and answer problems more than other algorithms. 
 
 

Abstract Nowadays, in line with the rapid growth of image and video-based inspection technologies such as machine vision systems, applications of image-based statistical process control are found in a wide variety of industries and processes. Considering the spatio-temporal variation of the observations in an image-driven process monitoring, the purpose of this study is to use multivariate statistical process control methods in order to evaluate and analyze the quality of samples from a dairy production process. In this research, the color content of each pixel is identified in the standard RGB format, and then the color conversion is performed to the new three-dimesnional L*a*b* color space. After estimation of the spatio-temporal autoregressive (STAR) model, multivariate control charts are employed to monitor both mean and vrainace of the estimates. Change point analysis using likelihood ratio statistic and decomposion of control statistic have improved the interpretability of the out-of-control signals on the control chart. The results of a case study related to the dairy industry reveals the capability of the propsed method in recognition of out-of-control conditions using image processing and analysis of the product surface color. 

Abstract  The present research is about the location of preventive facilities. Effective preventive health care services play an important role in reducing medical costs and mortality in all human societies, and the level of customer access to these services can be considered as a measure of their effectiveness and effectiveness. In order to solve the waiting and queuing problem, a biobjective mathematical and nonlinear problem is presented to address the issue of reducing the maximum waiting time for the visitors with the aim of increasing the maximum coverage. This research method is based on modeling. Data analysis was performed using Matlab software, and the answers obtained from the meta-heuristic algorithms were compared in the Minitab software. From the results of this study, it is possible to increase coverage by preventive facilities and increase waiting time. Another result of this study is the comparison of the effectiveness of each of the metaheuristic NSGAII and MOIWO with the defined index. 
 
 

Abstract
 Supervising the service quality and aligning performance objectives in cloud service centers facilitates the effective delivery of the requirements and related operational goals. Nowadays, with huge level of resource sharing and dynamic workloads in data centers, there is a need for comprehensive approaches for service quality supervisory in clouds. Previous research has considered this issue from different aspects and at present, there is a lack of multi-objective and methodologic supervisory approach to connect major previous approaches. Therefore, compared with related work, the purpose of this research is to take steps to compensate the mentioned deficiencies and the most important achievement is a novel service contact aware and quality supervisory methodology in cloud data centers. Proposed methodology, unlike existing solutions, is independent from the management strategy and environmental operators and supervises the goal quality metrics in cloud ecosystem. The empirical results indicate the usefulness and superiority of the proposed methodology in identifying quality bottlenecks and supervising the service quality targets. 

Abstract One of the most important factors of effect on designing effective supply chain is reduction of economic costs. On the other hand, greenhouse gas emission and pollutants increase has driven managers of organizations and researchers to look for designing and setting up networks that are focused on optimization of environmental factors and reduction of pollutants in all sectors in addition to economic optimization. In addition to these two factors, product delivery time is one of factors of effect on supply chain. In this research an integrated direct and reverse logistic model is studied by considering three objective functions: minimizing environmental effects, economic costs and delivery time. In this research model, uncertainty is considered through solution robustness. Possible scenarios are defined and evaluated by common risk management tools. Then scenario-oriented robust model of the problem is presented by considering uncertain parameters. The model is completed by replacing occurrence rate of each scenario by outputs of risk assessment (normalized RPN) in the model. Then the model is turned in to a single-objective model by applying LP metric method, and solved with the GAMS software. Finally it can be said that an effective supply chain can be designed by combining risk assessment and robust optimization.