Fuzzy Logic Approach to Inventory Lot-Sizing Problem Under Uncertain Environment
In this research fuzzy logic approach is applied to solve the problem of inventory lot-sizing under uncertain demand and supply. Conventional stochastic inventory lot-sizing model and existing dynamic models determine only uncertain demand. However, unavailability of supply can happen in many types of manufacturing system. It causes the problems of shortage and overstock. In the proposed Fuzzy Inventory Control (FIC) system, both demand and availability of supply are considered and described by linguistic terms. Then, the developed fuzzy rules are used to extract the fuzzy order quantity and the fuzzy reorder point continuously. The model is more effective than the conventional approach due to adjustment of both order quantity and reorder point. A time step simulation is used to analyze the results of different types of inventory lot-sizing model. Inventory costs of the proposed fuzzy inventory system are compared with existing models based on historical data of a case study factory. It found that FIC system can obtain extremely lower cost than the conventional stochastic and dynamic lot-sizing models.
KeywordsFuzzy logic Lot-sizing Uncertainty Demand and supply Inventory control Dynamic model
This work was supported by the Commission on Higher Education of Thailand and the Faculty of Engineering, Thammasat University, Thailand.
- 1.Bowersox DJ, Closs DJ, Cooper MB (2007) Supply chain logistics management. 2nd edn, International Edition, McGraw-Hill, New YorkGoogle Scholar
- 2.Kamal L, Sculfort J-L (2007) Fuzzy modeling of inventory control system in uncertain environment. In: International symposium on logistics and industrial informatics, 2007, pp 53–57Google Scholar
- 5.Rotshtein AP, Rakityanskaya AB (2006) Inventory control as an identification problem based on fuzzy logic. Cybernet Syst Anal 42(3):411–419Google Scholar
- 6.Park KS (1987) Fuzzy set theoretic interpretation of economic order quantity. IEEE Trans Syst Man and Cybernet 6 (17):1082–1084Google Scholar
- 10.Babai MZ, Dallery Y (2006) A dynamic inventory control policy under demand, yield and lead time uncertainties. In: Conf. Rec. 2006 IEEE international conference on service system and service management, pp 1439–1444Google Scholar
- 13.Parlar M, Perry D (1995) Analysis of a (Q,r,T) inventory policy with deterministic and random yields when future supply is uncertain. Eur J Oper Res 84:431–443Google Scholar
- 14.Wang C-H (2010) Some remarks on an optimal order quantity and reorder point when supply and demand are uncertain. Comput Ind Eng 58:809–813Google Scholar
- 15.Lin JL, Wang KS, Yan BH, Tang YS (2000) Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. J Mater Proc Technol 102:48–55Google Scholar
- 17.Yimer AD, Demirli K (2004) Fuzzy modeling and simulation of single item inventory system with variable demand. IEEE annual meeting of the North American Fuzzy Information Processing Society Banff, 2, Alberta, Canada, pp 985–989, 2004Google Scholar
- 18.Tantatemee T, Phruksaphanrat B (2012) Fuzzy inventory control system for uncertain demand and supply. In: : Proceedings of the international multiconference of engineers and computer scientists 2012, IMECS 2012, Hong Kong, 14–16 March 2012. Lecture notes in engineering and computer science, pp 1224–1229Google Scholar
- 19.Russell S, Taylor III W (2006) Operations management quality and competitiveness in a global environment. 5th edn, Wiley, New Jersey, pp 527–554Google Scholar
- 20.Sipper D, Bulfin RL (1998) Production planning, control and integration, McGraw-Hill International, SingaporeGoogle Scholar