A FUZZY LOGIC-BASED ALGORITHM FOR SUPPLY CHAIN MANAGEMENT CONSIDERING DIFFERENT CASES
DOI:
https://doi.org/10.23055/ijietap.2020.27.6.5883Keywords:
Supply Chain Management, Fuzzy Logic, Game Theory, Machine Learning, Multi-Layer PerceptronAbstract
In this paper, a new fuzzy based method is proposed for planning closed loop supply chain systems in the presence of market uncertainty using different game theories. The aim of this method is to find best production and product distribution strategy while rivals can take different market strategies that affect the quota of market. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a hybrid Fuzzy-based Multi-layer Perceptron and Simulated Annealing Algorithm (FBMLP-SA) is developed and results are compared with Branch and Bound (BnB); a hybrid Tabu Search and Simulated Annealing (SA) algorithms and a hybrid Ant Colony Optimization (ACO) and Simulated Annealing algorithms. For evaluating the production strategies in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affect the quota of market among suppliers. Comparing different game theories shows that the proposed FBMLP-SA method can successfully generate various and effective production strategies while rivals change their strategyPublished
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