Volume 21, Issue 4 (IJIEPM 2011)                   2011, 21(4): 154-165 | Back to browse issues page

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Systems Engineering Program, Systems & Control Engineering Dept. KN T University of Technology, Tehran, Iran , tahsiri@kntu.ac.ir
Abstract:   (14971 Views)

  In a general sense, the main concept of Just-In-Time methodology is built on the concept of availability of an exact amount and type of commodity throughout a production line when it is really required. Under such circumstances, there is no need to situate any warehouse before any of the work station within the production plant. This method of manufacturing operations with the help of Kanban tool has been able to provide a beneficial profile in term of cost reduction to many industries around of the world. It is now agreed that In the recent years particularly where rapid changing and uncertain environment is a dominant factor, this method must be implemented to manufacturing modeling and operations with more caution. Under conditions of nowadays marketplace, maintaining a balance between the diversities comes from market requirements and a designated scheduling batch size of traveling commodities comes from such a plant manufacturing strategy is rarely possible . In addition, it is not rational thinking of manipulating the outsource commodities in response to the ongoing production line requirements. Therefore a production scheduling policy that could provide more inside relaxation to supply the required items before any of the work stations is more reliable. This research patches up the current concept of Just-In-Time philosophy with some new assumptions which as a buffer permits a temporary intermediate stock before any of stages through the supply chain. The criteria for setting the right size and time of the batches traveling between each of the two sequential stages come from a new cost-effective control method. This cost model considers all elements of the total production operations within the chain and formulates them by the use of mixed integer nonlinear programming . A genetic algorithm is also developed to solve the proposed model. The results of a comparison performance of this modified JIT method with that Sarker & Wang applied in their paper (2004) show a noticeable decrease in the total cost of the chain as well as its level of WIP and its number of kanbans using within in .

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