Knowledge management
Vahid Negintaji; Morteza Shafiee; Hilda Saleh
Abstract
The research aims to design a sustainable supply chain model to achieve world class with a fuzzy hybrid approach based on critical situation information systems. This research is developmental-applicative in terms of its purpose, and the method used is a combined method that includes historical method ...
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The research aims to design a sustainable supply chain model to achieve world class with a fuzzy hybrid approach based on critical situation information systems. This research is developmental-applicative in terms of its purpose, and the method used is a combined method that includes historical method (gathering information) and survey method (questionnaire distribution). The statistical population of this research consists of experts in the handwoven carpet industry and professors in the field of world-class production. The sampling method is purposive and model fitting was done with Dematel techniques and interpretive structural model. Based on the literature, 15 indicators were identified, which are: lean production (elimination of redundant activities), cost of materials and transportation, reduction of time to reach the market and waiting, technology and machinery and software Design tools, supply chain agility, applying honest principles in hiring local people, increasing consumer awareness to consume sustainable products, focusing on social/community welfare, insurance and pension guarantee, creating strong legal facilities to take care of industries in times of Corona, compatible production processes With environment and green distribution, ISO 14001, reverse logistics and recycling, green packaging and distribution and creating sustainable procurement strategies. The results show that based on the designed interpretative structural model, the low-level components of applying honest principles in hiring local people, focusing on the social/personal welfare of employees and issuing, creating strong legal facilities in times of Corona and environmentally friendly production processes and Green distribution has the most influence on the whole model.
Data mining
Ali Esmaili; Houshang Taghizadeh; Naser Faqhi Farhamand
Abstract
This research aims to study the data-driven model of gas consumption management, with a focus on addressing unauthorized use through the analysis of information systems. Research was conducted using a metasynthesis approach and technique in the field of gas consumption management and mathematical programming ...
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This research aims to study the data-driven model of gas consumption management, with a focus on addressing unauthorized use through the analysis of information systems. Research was conducted using a metasynthesis approach and technique in the field of gas consumption management and mathematical programming with genetic algorithms. ATLAS.ti software was used for analysis. The influencing factors related to a specific period of time were examined and searched for in this research. Internal and external sources from the years 2006 to 2023 were analyzed. 27 studies were selected based on the Critical Appraisal Skills Programme (CASP) technique. In the continuation of mathematical modeling using MATLAB software, the simulation was conducted to compare the performance of three proposed algorithms. Based on the results obtained from the meta-combination technique, the main categories include the use of renewable energy, gas consumption management, shortcomings, obstacles, data-driven solutions, consequences of gas consumption management, and economic growth. All three models also demonstrated the basis for optimal gas consumption and the reduction of unauthorized consumption. The utilization of data analysis can enhance system efficiency, pinpoint weaknesses and losses, boost productivity, and optimize the utilization of gas energy. Based on the analysis, it was shown that data mining can be very useful in managing gas energy consumption and identifying unauthorized breaches. Overall, simulating gas energy consumption management using a genetic algorithm can provide efficient and effective solutions, handle complex and dynamic scenarios, and offer insights into optimizing gas consumption and energy efficiency.
