A Novel Continuous KNN Prediction Algorithm to Improve Manufacturing Policiesin a VMI Supply Chain

This paper examines and compares various manufacturing policies which a manufacturer may adopt soas to improve the performance of a supply chain under vendor managed inventory (VMI) partnership.The goal is to maximize the combined cumulative profit of supply chain while minimizing the relevantinventory management costs. The supply chain is a two-level system with a single manufacturer singleretailer at each level, in which the manufacturer takes the responsibility of overall inventories of supplychain. A base system dynamics (SD) simulation model is first employed to describe the dynamicinteractions between the variables and parameters of manufacturer and retailer under VMI. Then, thementioned policies are constructed using the base SD model that lead us to differentiate the behavior ofsupply chain members for each policy within the same duration of time. In this paper, we usecontinuous K-nearest neighbor (CKNN) as one of the instance-based learning methodologies to predictthe best manufacturing rates. This algorithm effectively increases the combined profit of supply chainin comparison with other two policies discussed in this study. Accordingly, a numerical example alongwith a number of sensitivity analyses are conducted to evaluate the performance of proposed policies

کلیدواژه ها:

Vendor Managed InventoryContinuous K-nearest NeighborLearning ، System Dynamics