APPLICATION OF MINIMAX DISTRIBUTION FREE PROCEDURE AND CHEBYSHEV APPROACH IN MIXED INVENTORY MODEL INVOLVING REDUCIBLE LEAD-TIME AND SETUP COST WITH IMPRECISE DEMAND

2013 ◽  
Vol 30 (04) ◽  
pp. 1350009 ◽  
Author(s):  
DHARMENDRA YADAV ◽  
S. R. SINGH ◽  
RACHNA KUMARI

In this paper, we extend Lin [Lin, YJ (2008). Minimax distribution free procedure with backorder price discount. International Journal of Production Economics, 111, 118–128] model by fuzzifying the demand rate, based on triangular fuzzy number to increase its applicability and solve the problem by using an alternative approach i.e., Chebyshev approach. We prove the concavity and convexity of the estimate of total variable cost per unit time in fuzzy sense. To compare the expected annual cost of proposed model, defuzzification is performed by two different methods namely signed distance and centroid method. We provide a solution procedure to find the optimal values of lead-time, the order quantity and the backorder price discount by using minimax distribution free procedure and Chebyshev approach. Through numerical example it is shown that there is a significant saving in cost due to crashing cost to reduce the lead-time and the setup cost. We also compare our results with Cheng et al. [Cheng, TL, CK Huang and KC Chen (2004). Inventory model involving lead-time and setup cost as decision variables. Journal of Statistics and Management Systems, 7, 131–141] model and show that Ben-Daya and Raouf's [Ben-Daya, M and A Raouf (1994). Inventory model involving lead-time as a decision variable. Journal of the Operational Research Society, 45, 579–582] model is the special case of our proposed model.

2004 ◽  
Vol 14 (2) ◽  
pp. 247-258 ◽  
Author(s):  
Bor-Ren Chuang ◽  
Liang-Yuh Ouyang ◽  
Yu-Jen Lin

In a recent paper, Ouyang et al. [10] proposed a (Q, r, L) inventory model with defective items in an arrival lot. The purpose of this study is to generalize Ouyang et al.?s [10] model by allowing setup cost (A) as a decision variable in conjunction with order quantity (Q), reorder point (r) and lead time (L). In this study, we first assume that the lead time demand follows a normal distribution, and then relax this assumption by only assuming that the first two moments of the lead time demand are given. For each case, an algorithm procedure of finding the optimal solution is developed.


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