Multi-Attribute Decision Making
Ahmad Jafarnejad Chaghoshi; seyed mahdi rouhani poor; hannan Amoozad Mahdiraji; Mohammad Ehsanifar
Abstract
Purpose: product quality includes three variables: design, conformance and use. Measuring the quality of products with respect to all three quality variables is one of the important challenges of the country. Therefore, the present study was an attempt to figure out how quality factors are related to ...
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Purpose: product quality includes three variables: design, conformance and use. Measuring the quality of products with respect to all three quality variables is one of the important challenges of the country. Therefore, the present study was an attempt to figure out how quality factors are related to each other and to determine the relative weight of these factors and to provide a product quality measurement model using hesitant fuzzy linguistic terms.
Methodology: The present study falls into the category of applied studies in terms of objective and can be recognized as a quantitative study in terms of methodology. The population of the study incorporates academic experts and university-industry experts. Sample size (n=10) was determined using the purposeful and snowball sampling method. Due to the uncertainty of experts' in determining the mutual impact of product quality factors, the DEMATEL technique was combined with hesitant fuzzy logic, the resulting technique was then integrated with the network analysis process (DANP), and the final model was extracted. Thanks to this procedure, the present study can be deemed innovative.
Findings: The cause and effect relationships between the main factors of product quality were identified and extracted using DEMATEL technique. Then, taking into account the intensity of the mutual impact of quality factors on each other and using the DNAP technique, the product quality factors were ranked in three dimensions: design quality, conformance and use. According to the findings, management factors and resources (employees-infrastructure-environment) were identified as causal factors that affect other factors. On the other hand, the DANP output showed that "design quality" is the most important factor in product quality. so, with the relative weights of the factors, the product quality measurement model was obtained.
Originality/ value: Researchers and industrial managers at the national level will be able to identify the relationship between quality factors and use this model to measure product quality or the quality rate of goods according to relative weight of each factor.
stochastic/Probabilistic/fuzzy/dynamic modeling
Mohamad Sharifzadegan; Tahmourth Sohrabi; Ahmad Jafarnejad Chaghoshi,
Abstract
Purpose: The complex conditions prevailing in the industries and the increasing costs of production equipment and machinery and competitiveness in gaining market share, show the role and importance of production planning and maintenance with other parts of the industry. Integrating such decisions can ...
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Purpose: The complex conditions prevailing in the industries and the increasing costs of production equipment and machinery and competitiveness in gaining market share, show the role and importance of production planning and maintenance with other parts of the industry. Integrating such decisions can take fundamental steps to reduce costs and increase quality. Maintaining and creating the continuity of production activities depends on accurate and correct planning of production, maintenance activities and how to support these processes. The need for integration and coherence in the simultaneous planning of such activities causes a lack of rework and parallel work and obstacles and delays and inconsistencies at different levels of production.Methodology: In this research, a two-objective mathematical model of production planning and repairs with limited resources is presented in conditions of uncertainty.Findings: The results of comparing accurate and meta-innovative solutions show the improvement in the company's products and the optimal use of material and human resources. Sensitivity analysis also shows that the failure rate of the machine before and after preventive maintenance has a great impact on the value of the objective function of the mathematical model. The results show that the average error of the ant algorithm is only 3%. This is while the average solving time in GAMZ is 45,000 seconds, while the average solving time of the ant algorithm is about 354 seconds.Originality/Value: This shows that the ant algorithm has a very small amount of error with much less time and therefore the efficiency of this solution method can be well explained.