Journal of Quality Engineering and Management

Abstract Widespread environmental degradation and increasing emissions of greenhouse gases have raised environmental concerns among communities and governments. One of the ways to reduce environmental pollution is to implement a green supply chain. In this study, the selection of green suppliers of biomass power plant equipment and how to allocate demand to them as one of the key strategic decisions of the supply chain has been investigated. Due to the multiplicity of criteria in this issue, using pairwise comparison methods for weighting will not be effective. In the proposed integrated model, the principal component analysis method is used to meet this challenge. Finally, using a multi-objective mathematical planning model, the amount of demand allocation to each supplier is determined. The proposed method was used to select green suppliers of Shiraz biomass power plant equipment. The results show the efficiency of the proposed model for selecting green suppliers of biomass power plant equipment.

Abstract Multi-response optimization performed by the response procedure method is very common. Before optimization, we need to select and fit the appropriate model for each response. A major problem that may occur due to incorrect fitting of models and failure to reach optimal solutions is model misidentification. The solid regression model method, which is a semi-parametric method for estimating d, can have a better performance than both parametric and non-parametric estimation methods against model misclassification. In this research, the use of a robust regression model method is proposed to improve the model estimation and the appropriate fit of each of the answers will be investigated by one of the multivariate optimization methods, namely the utility function. In the following, an applied study is presented to compare parametric, nonparametric and semi-parametric methods. The results of this study show that the performance of the stable regression model is more appropriate in many situations as well as in the modeling stage than the other two methods. Therefore, the optimization results with a stable regression model are much more reliable.

Abstract Due to the lack of water in the current era and the support of governments in replacing worn-out water transmission lines, the polyethylene pipe industry has become one of the attractions of interest for investors. But one of the concerns of the community is the quality of these pipes. Therefore, in the present study, the effect of quality management system on the quality of polyethylene pipes is investigated using system dynamics. The case study of this research is one of the companies in Kerman Pipe Manufacturing Industry. In this article, using the opinion of industry elites, the necessary variables were identified and causal diagrams and state and flow diagrams were drawn using Wensim software. The results of the analysis showed that when implementing the quality management system, all organizational units should be coordinated with each other and the role of management in it is very key so that any management negligence causes poor product quality, reduced customer satisfaction and consequently reduced The amount of product sales and waste increases and eventually the organization is in a state of liquidation, bankruptcy and doubling.

Abstract One of the most widely used fields in production planning is related to determining the amount and sequence of production. The development of production batch sizing models as one of the branches of production planning science has always had a special place among researchers. Determining the size of a production batch means breaking a batch into a number of subcategories, each subcategory of which is completed and transferred to the next machine to continue the operation so that the operations can overlap. The purpose of this study is to determine the size of the production batch by considering the distribution costs and using single loading devices such as pallets and containers. Problems associated with decisions regarding the size of the production batch and the loading of products in the machines were also modeled in this study, in which constraints such as weight constraints, volume constraints, or the amount of load loaded in the boxes were also considered. Finally, MIP models based on the "branching and cutting" method on an optimization package were investigated and the results of this method showed that these methods can be implemented in many different practical situations.

Abstract In statistical quality control, the concentration and dispersion of continuous quality characteristics in a process are usually examined simultaneously with the help of two graphs. In this way, the changes in each will be identifiable simultaneously. Due to the high efficiency of control graphs of mean and standard deviation with double sampling in rapid identification of mean changes and standard deviation of the process, discussion of this category of graphs seems necessary. In this paper, based on the results of designing mean control and standard deviation diagrams with double sampling, mean and control standard deviation diagrams with triple sampling are introduced. Statistical design of mean control and standard deviation diagrams with triple sampling is formulated as an optimization problem and to solve this problem, genetic algorithm is proposed.

Abstract There are several ways to increase the competitiveness of organizations. One of the best solutions offered is to improve productivity and efficiency. Data Analysis (DEA), which is a mathematical method and one of the best non-parametric methods, measures the performance of organizations based on input and output variables. Units whose efficiency score equals one are efficient. Efficient units are also ranked using the Anderson-Peterson (AP) method. In this research, a new development method for evaluating and ranking organizations based on performance scores is presented. The case study is the evaluation of the performance of cement companies listed on the stock exchange, which were ranked using the collective model and Anderson-Peterson. Also, the rank of companies was calculated using the new development model and TOPSIS model and compared with each other. The results showed that the ranking of companies using the new development model (N-DEA) is a good solution for calculating the efficiency and ranking of decision-making units.