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    Abstract
2013 (Vol. 6, Issue: 11)
Article Information:

Improvement of Inventory Control for Defective Goods Supply Chain with Imperfect Quality of Commodity Components in Uncertain State

Salah Alden Ghasimi, Rizauddin Ramli and Nizaroyani Saibani
Corresponding Author:  Salah Alden Ghasimi 

Key words:  Defective goods, inventory control, LINGO, MINOS, supply chain, uncertain model,
Vol. 6 , (11): 1920-1927
Submitted Accepted Published
October 22, 2012 February 01, 2013 July 25, 2013
Abstract:

In this study, we proposed a mathematical model for four-level defective goods supply chain with imperfect quality of commodity components in an uncertain state to maximize profit of supply chain. It is assumed that the inspection of incoming parts in suppliers is randomly done and incomplete. This lead some of the manufactured products will not be properly manufactured because of defective parts and are considered as defective goods and in most cases, the defective products can be repaired by replacing with the good parts. The defective parts will be collected and then returned to the suppliers for repairing. Out proposed model considers defective parts problem by optimizing the costs of production, maintenance, shipping, reworking on the defective goods and parts, shortage in retailers due to the production of defective goods and cost of capital incurred by the companies. The model can anticipate the active suppliers/manufacturers/distributors and the quantity of parts and goods that must be exchanged between them. Our proposed model is novel and we used MINOS solver and LINGO software to solve the problem. The results ascertained the correctness and fine function of the proposed model.
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  Cite this Reference:
Salah Alden Ghasimi, Rizauddin Ramli and Nizaroyani Saibani, 2013. Improvement of Inventory Control for Defective Goods Supply Chain with Imperfect Quality of Commodity Components in Uncertain State.  Research Journal of Applied Sciences, Engineering and Technology, 6(11): 1920-1927.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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