%0 Journal Article
%T Simulation based optimization of multi-product supply chain under a JIT system
%J Journal of Industrial Engineering and Management Studies
%I Iran Center for Management Studies
%Z 2476-308X
%A Sajedinejad, Arman
%A Hassannayebi, Erfan
%A Asadi Lari, Mohammad Saviz
%D 2020
%\ 06/01/2020
%V 7
%N 1
%P 87-106
%! Simulation based optimization of multi-product supply chain under a JIT system
%K Supply chain management
%K Genetic Algorithm
%K optimization
%K Just In Time
%K discrete event simulation
%R 10.22116/jiems.2020.110009
%X It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved by regular exact models such as mathematical ones due to their complexity. The present paper is aimed at simulating lean multi-product supply chain system as well as optimization of the objectives of supply chain. Variables of the simulation model include two types of Kanbans namely withdrawal, and production to determine the inventory level, and batch size of delivery parts for each stage of supply chain. So, in this paper simulation model was developed for supply chains, taking into consideration the different production scenarios and were modeled and compared. A production scenario is adopted for each level of the chain in order to achieve the objectives. The use of meta-heuristic techniques leads us to optimization of these variables which helps decrease delay of both product delivery and inventory level of supply chain. In this case, Genetic Algorithm has applied to find the best variable values of each scenario (included in the right number of each Kanbans), aimed at decreasing the costs and delivery delays. An example based on a case study is given to illustrate the efficiency of the proposed approach. Considering each level of supply chain, the ratio between and among cost, inventory, and delivery delay variables were obtained.
%U http://jiems.icms.ac.ir/article_110009_fb0c2ce7aa1e0d4819b2a4da593cefc2.pdf