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Integrated Inventory Model with Controllable Lead Time Involving Investment for Quality Improvement in Supply Chain System
Author(s) -
M. Vijayashree,
R. Uthayakumar
Publication year - 2015
Publication title -
doaj (doaj: directory of open access journals)
Language(s) - English
DOI - 10.22034/2015.1.05
Subject(s) - lead time , supply chain , lead (geology) , quality (philosophy) , investment (military) , business , chain (unit) , process engineering , computer science , operations management , engineering , marketing , philosophy , epistemology , geomorphology , politics , political science , law , geology , physics , astronomy
The purpose of this article is to investigate a two-echelon supply chain inventory problem consisting of a single-vendor and a single-buyer with controllable lead time and investment for quality improvements. This paper presents an integrated vendor-buyer inventory model in order to minimize the sum of the ordering cost, holding cost, setup cost, investment for quality improvement and crashing cost by simultaneously optimizing the optimal order quantity, process quality, lead time and number of deliveries. Here the lead-time crashing cost has been assumed to be an exponentially function of the lead-time length. The main contribution of proposed model is an efficient iterative algorithm developed to minimize integrated total relevant cost for the single vendor and the single buyer systems with controllable lead time reduction and investment for quality improvements. It can be obtained simultaneously by optimizing the optimal solution, mathematical modelling and solution procedure are employed in this study for optimizing the order quantity, lead time, process quality and the number of deliveries from the vendor to the buyer in one production run with the objective of minimizing total relevant cost. Graphical representation is also presented to illustrate the proposed model. Numerical examples are presented to illustrate the procedures and results of the proposed algorithm. Matlab coding is also developed to derive the optimal solution and present numerical examples to illustrate the model.

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