Mixture Inventory Model of Demand with the Mixtures of Normal Distribution for Controllable Lead Time.

• Main Authors: Hui-Yin Tsai, 蔡慧瑩
• Other Authors: Jong-Wuu Wu
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/04948154089224444879

碩士 === 淡江大學 === 統計學系 === 88 === Most of the authors (such as Azoury and Brill (1994), Chiu (1995), Foote et al. (1988), Kim and Park (1985), Liberatore (1977), Magson (1979), Naddor (1966), and Silver and Peterson (1985), etc.) have been used deterministic or probabilistic models, lead time is viewed as a prescribed constant or a stochastic variable, which therefore, is not subject to control for dealing with inventory problems. Firstly, Liao and Shyu (1991) presented a probabilistic model in which the order quantity is predetermined and the lead time is a unique decision variable. Secondly, Ben-Daya and Raouf (1994) have also extended the Liao and Shyu (1991) model by considering the demand during the lead time is normal distribution and both the lead time and the order quantity as decision variables where the shortages are neglected. Then, Ouyang et al. (1996) assumed that the shortages are allowed and extended the Ben-Daya and Raouf (1994) model by adding the stockout cost. In a recent paper, Moon and Choi (1998) and Hariga and Ben-Daya (1999) corrected and improved the model of Ouyang et al. (1996) by simultaneously optimizing the order quantity, the reorder point, and the lead time. They thought the algorithm of Ouyang et al. (1996) cannot find the optimal solution due to the flaws in the modeling and the solution procedure.Thus, they presented a continuous review inventory model in which they considered the lead time, the reorder point, and the order quantity as decision variables. When the demands of the different customers are not identical in the lead time, then we can''t use only a distribution (such as Ouyang et al. (1996), Moon and Choi (1998), and Hariga and Ben-Daya (1999) using normal distribution) to describe the demand of the lead time. Hence, in chapter 2, we extend the model of Ouyang et al. (1996) by considering the mixtures of normal distribution (see Everitt and Hand (1981)). In addition, we also still assume that the shortages are allowed. Moreover, the total amount of stockout is considered a mixture of backorders and lost sales during the stockout period. Moreover, we also develop an algorithmic procedure to find the optimal order quantity and optimal lead time and the effects of parameters are also studied. Further, in chapter 3, we also assume the reorder point as a decision variable (that is, the service level is not fixed) and extend and correct the complete procedures of Moon and Choi (1998) and Hariga and Ben-Daya (1999) to find the optimal solution for the model. In addition, we also develop an algorithmic procedure to find the optimal lead time, the reorder point, and the order quantity. Moreover, a significant amount of savings over the model can be achieved. Finally, the conclusion of the above chapter 2 and 3 and the future research are also given in chapter 4.