Optimal production policy for a closed-loop supply chain with stochastic lead time and learning in production

This paper considers a closed-loop supply chain with one manufacturer and one retailer for trading a single product. On behalf of the manufacturer, the retailer collects the used items from the end customers for possible remanufacturing. The production of nished products (manufactured and remanufactured) is subject to learning. The lead time for the retailer is assumed to be stochastic. The manufacturer delivers the retailer's order quantity in a number of equal-sized shipments. The objective is to determine the optimal number of shipments and shipment size by minimizing the average expected total cost of the closedloop supply chain. A solution method for the model is presented, and important results are obtained for numerical examples. According to the numerical study, an impressive cost reduction due to consideration of learning in production and remanufacturing is observed. To investigate the impact of key model parameters on the optimal results, a sensitivity analysis is also carried out. The proposed model is applicable to those business rms whose production process is executed by the human beings.