Existing inventory lot-sizing models assume certain demand and sufficient supply, which are not practical for industry. Dynamic inventory models can serve uncertain demand, but supply is assumed to be available. However, in the real world situation, supply is not always offered. So, the method that can deal with both uncertain demand and supply should be developed. Fuzzy logic control is now being the effective methodology in many applications under uncertainty. Therefore, a fuzzy logic approach for solving the problem of inventory control under uncertainty was proposed for a case study factory. In the proposed Fuzzy Inventory System (FIS), both demand and availability of supply are described by linguistic terms. Then, the developed fuzzy rules are used to extract the fuzzy order quantity and the fuzzy reorder point continuously. The order quantity and reorder point are both adjusted according to the FIS system. In this research, the suitable ranges for the inputs of the FIS model are justified for the case study factory. Moreover, the effect of trend demands for both increase and decrease are also analyzed with the proposed range. Inventory costs of the proposed fuzzy inventory system are compared with the existing model based on historical data of the case study factory. It found that the proposed range can obtain lower cost than the previous research FIS lot-sizing model, which is better than conventional approaches.
Analyzing The Concept Of Graded K-Preference Integration Representation Method
