Presenting a model based on artificial intelligence in the reverse supply chain of the home appliance industry in Tehran province by fuzzy genetic algorithm
محورهای موضوعی : Supply Chainpeyman Barzegar Keliji 1 , hasan ali aghajani 2 , seyed ahmad shayan nia 3
1 - PhD Student in Industrial Management (Production and Operations), Department of Industrial Management, Islamic Azad University, Firoozkooh Branch, Firoozkooh, Iran.
2 - Faculty of Economics and Administrative Sciences Department,Mazandaran University, Babolsar, Iran.
3 - Department of Industrial Management, Islamic Azad University, Firoozkooh Branch, Firoozkooh, Iran.
کلید واژه: Supply Chain, Reverse supply chain, Artificial Intelligence, Fuzzy Genetics Algorithm,
چکیده مقاله :
The purpose of this study is to identify the indicators affecting the improvement of the reverse supply chain in the home appliance industry in Tehran province. Preliminary indicators were obtained using theme analysis method and during in-depth and semi-structured interviews with 14 experts of the reverse supply chain in the home appliance industry in Tehran province. The final indicators of the research were identified using fuzzy Delphi method and A model for optimizing indicators in the form of objective function and constraints is presented. . In order to analyze the data, in the first place, with the help of ten experts, the final indicators were introduced and then, using the opinions of 36 people related to the issue of supply chain, the indicators were prioritized. The results of this study showed that the managerial dimension and consumer feedback , is a key factor in accepting new change and entering the issue of reverse supply chain. Knowledge dimension, including continuous improvement in the field of learning and education, etc., after management indicators and in the same initial stages, can be a determining criterion for improving the reverse supply chain in the industry. Finally, a model was designed using a model that shows the output of the status of indicators and using genetic algorithm (along with model testing in Gamz program) in MATLAB software, analysis was performed and the results were introduced by introducing optimal values and An improved model was presented.
