بازنگری تعیین اندازه دسته تولید برای یک سیستم موجودی با بهبود محصول
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22760||2010||7 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Mathematics with Applications, Volume 59, Issue 8, April 2010, Pages 2933–2939
This note investigates the study by Teunter (2004)  on lot sizing for inventory systems with product recovery where lot sizing formulae for two recovery policies ((1,R)(1,R) and (P,1)(P,1)) are derived. Instead of applying the classical optimization technique, we develop an integrated solution procedure for each of the two policies using algebraic approaches. Numerical analysis show that our examples result in a lower total cost for both policies.
Teunter  developed two lot sizing models with product recovery. In the first model, one production lot is alternated with RR recovery lots, or (1,R)(1,R) policy. For the other model, PP production lot is alternated with one recovery lot, or (P,1)(P,1) policy. He derived two integrated total production inventory costs and three decision variables. They are the optimal production lot size (Q∗pQ∗p), the optimal recovery lot size (Q∗rQ∗r) and the number of recovery (RR) or the production lots (PP). The values of Q∗pQ∗p and Q∗rQ∗r are solved using partial differential equations. Corresponding to Q∗pQ∗p and Q∗rQ∗r, the values of RR or PP are then derived. Since RR and PP must be discrete, the author modified Q∗pQ∗p and Q∗rQ∗r, so that RR or PP was discrete. In this paper, we suggest an integrated solution procedure to solve Q∗pQ∗p and Q∗rQ∗r using a simple algebraic method without derivative. This method is simple and it is helpful for students who are not familiar with calculus. There has been some research on solving an optimal solution without derivative and three methods are used widely. The methods are algebraic approach, cost-difference comparison method and arithmetic–geometric mean inequality. Grubbstorm  was the first to show that a standard economic order quantity model could be solved using an algebraic approach or without using derivative. Grubbstorm and Erdem  extended the approach by allowing backorder and Cardenas-Barron  applied the algebraic approach to solve the classical economic production quantity (EPQ) model with shortage. Yang and Wee  developed an integrated vendor–buyer inventory system derived without derivatives. Wee et al.  developed an EOQ model with temporary sale price derived without derivatives. Other researchers who used the algebraic approaches are Chang et al.  who solved EOQ and EPQ model with shortage, Sphicas  who solved EOQ and EPQ with linear and fixed backorder cost, Wee and Chung  who solved the economic lot size for an integrated vendor–buyer inventory system, Cardenas-Barron  who used the approach to solve an NN-stage-multi-customer supply chain model and Cardenas-Barron  who solved inventory policies of immediate rework process model and NN-cycle rework process model. Chung and Wee  developed an optimal economic lot size for a three-stage supply chain with backordering derived without derivatives. Cost-difference comparison method was introduced by Minner  and Wee et al.  extended the method by simplifying the solution procedure. Teng  was among the first researchers to derive EOQ using arithmetic–geometric mean inequality. Cardenas-Barron  extended the method and solved EOQ and EPQ model with backorder and Cardenas-Barron  presented a discussion on the use of arithmetic–geometric mean method.
نتیجه گیری انگلیسی
This note has explored the inventory models with product recovery from . The main purpose of this note is to suggest an integrated solution based on algebraic approach for determining the number of production and recovery lot sizes with product recovery. The study not only provides an easy to follow approach to derive the optimal solution, it also results in a lower total cost as seen in Table 1 and Table 2. One limitation of this study is the assumption that all return items are redeemable or as-good-as-new, and the demand rate and return fraction are deterministic. Future work can be done to consider stochastic demand rate and return fraction, as well as deleting the assumption that all return items can be redeemed.