IMPROVING REMANUFACTURING SYSTEMS DECISIONS BY BAYESIAN APPROACH

Reverse supply chain comprises sequences of activities required to recover a post-consumed product from a customer to manufacturer. Remanufacturing of used products is one of the most desirable opportunities among the product recovery choices. We consider a hybrid system for a single remanufacturable product and model the hybrid system as an open queueing network (OQN) with a stochastic return of used products and demand. In the remanufacturing system's it is critical to coordinate the recovery operation, and disposal decisions of the inducted cores satisfy non-stationary demand. We utilize Bayesian approach in dealing with return and recovery rate uncertainties of the remanufacturing system. In Bayesian updating procedure, recently obtained data is pooled with the formerly existing data about the parameter that we interested. A numerical example is presented to show the effect of the parameter revising via Bayesian approach on the system's operational performance measures.