Journal of Industrial Engineering and Management Studies

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Indexing and Abstracting

Related Links

FAQ

Double-Blind Peer Review

News

Metrics

Simulation based optimization of multi-product supply chain under a JIT system

Document Type : Original Article

Authors

1 Iranian Research Institute for Information Science and Technology (IRANDOC).

2 Industrial Engineering Department, Sharif University of Technology, Tehran, Iran.

3 Engineering Department, Payame Noor University (PNU), Tehran, Iran.

Abstract

 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.

Keywords

References
Alinaghian M., and Goli A., (2017). "Location, Allocation and Routing of Temporary Health Centers in Rural Areas in Crisis, Solved by Improved Harmony Search Algorithm", International Journal of Computational Intelligence Systems, Vol. 10, No. 1, pp. 894-913.
Akturk, M. S., and Erhun, F., (1999). "An overview of design and operational issues of Kanban systems", International journal of production Research, Vol. 37, No. 17, pp. 3859-3881.
Azadeh, A., Bidokhti, B., and Sakkaki, S.M.R. (2005). "Design of practical optimum JIT systems by integration of computer simulation and analysis of variance", Computers & Industrial Engineering, Vol. 49, pp. 504-519.
Cheng, T.C.E., (1990). "Simulation Study of Production Inventory Management in a Mylar Capacitor Manufacturing Company", Engineering Costs and Production Economics, Vol. 20, pp. 43-50.
Chaharsooghi, S.K., and Sajedinejad, A., (2010). "Determination of the number of Kanbans and batch sizes in a JIT supply chain system", Scientia Iranica. Transaction E, Industrial Engineering, Vol.17 No. 2, pp. 143-149.
Darestani S. A., and Hemmati M., (2019). "Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system", Computers & Industrial Engineering, Vol. 136, pp. 277-292.
Goli A., and Davoodi S. M. R. (2018). "Coordination policy for production and delivery scheduling in the closed loop supply chain", Production Engineering, Vol. 12, pp. 621–631.
Goli A., Tirkolaee B. E., and Soltani M. (2019). "A robust just-in-time flow shop scheduling problem with outsourcing option on subcontractors", Production & Manufacturing Research, Vol. 7, No. 1, pp. 294-315.
Hu, W., Kim, S., and Banerjee, A., (2008). "An inventory model with partial backordering and unit backorder cost linearly increasing with the waiting time", European Journal of Operational Research, Production, Manufacturing and Logistics, Vol. 197, No. 2, pp. 581-587.
Hum, S.H., and Lee, C., (1997). "JIT Scheduling Rules: a Simulation Evaluation", Omega, International journal of Management Science, Vol. 26, No. 3, pp. 381-395.
Kelton, W.D., Sadowski, R.P., and Sturrock, D.T., (2004). Simulation with Arena, Thirth Edition. Mc Graw Hill publication.
Kim, Y.D., Lee, D., and Kim, J., (1998). "A Simulation Study on Lot Release Control, Mask Scheduling, and Batch Scheduling in Semiconductor Wafer Fabrication Facilities", Journal of Manufacturing Systems, Vol. 17, No. 2, pp. 107–117.
Kojima, M., Nakashima, K., and Ohno, K., (2008). "Performance evaluation of SCM in JIT environment", International journal of Production Economics, Vol. 115, pp. 439– 443.
Lee, H., and Seo, D., (2016). "Performance evaluation of WIP-controlled line production systems with constant processing times", Computers & Industrial Engineering, Vol. 94, pp. 138–146.
Li, Y., Ipa, W.H., and Wang, D.W. (1998). "Genetic algorithm approach to earliness and tardiness production scheduling and planning problem", International journal of Production Economics, Vol 54, pp. 65-76.
Matsui, Y., (2007). "An empirical analysis of just-in-time production in Japanese manufacturing companies", International journal of Production Economics, Vol. 108, pp. 153–164.
Mirzapour, S.M.J., Malekly, H., and Aryanezhad, M.B., (2011). "A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty", International journal of Production Economics, Vol. 134, No. 1, pp. 28–42.
Negahban, A., and Smith, J.S., (2014). "Simulation for manufacturing system design and operation: Literature review and analysis", Journal of Manufacturing Systems, Vol. 33, No. 2, pp. 241–261.
Naimi-Sadigh, A., Chaharsooghi, S.K., and Sheikhmohammady M., (2016). "Game-theoretic analysis of coordinating pricing and marketing decisions in a multi-product multi-echelon supply chain", Scientia Iranica. Transaction E, Industrial Engineering, Vol. 23, No. 3, pp. 1459-1473.
Naimi-Sadigh, A., Karimi, B., and Farahani, R.Z., (2011). "A game theoretic approach for two echelon supply chains with continuous depletion", International Journal of Management Science and Engineering Management, Vol. 6, No. 6, pp. 408-412.
Ohno, K., Nakashima, K., and Kojima, M., (1995). "Optimal numbers of two kinds of Kanbans in a JIT production system". International Journal of Production Research, Vol. 33, No. 5, pp 1387–1401.
Puchkova, A., Romancer, J.L., and McFarlane, D., (2016). "Balancing Push and Pull Strategies within the Production System", IFAC-Papers OnLine, Vol. 49, No. 2, pp. 66–71.
Sajedinejad, A. (2018). "Developing the Functional Framework of Information Science Management: Case of Iranian Institute for Information Science and Technology (IRANDOC) ". Iranian Journal of Information processing and Management, Vol.34, No. 4, pp. 1481-1504.
Sajedinejad, A., and Chaharsooghi, S.K., (2018). "Multi-Criteria Supplier Selection Decisions in Supply Chain Networks: A Multi-Objective Optimization Approach", Industrial Engineering & Management Systems, Vol. 17, No. 3. pp. 392-406.
Sajedinejad, A., and Naimi-Sadigh, A., (2018). "E-Functionality of scientific and technological information systems management in Iranian Science and Research Value Chain". Iranian Journal of Information Processing Management, Vol. 33, No. 2, pp. 727-744.
Sangaiah, A. K., Tirkolaee E. B., Goli A., Dehnavi-Arani S., (2019). "Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem", soft computing, pp. 1–21.
Sawaya, W.J., (2007), "Using empirical demand data and common random numbers in an agent based simulation of a distribution network", Proceedings of the 39th conference on winter simulation. pp. 1947-1952.
Sawer, J.T., and Brann, D.M., (2008). "How to build better models: Applying agile techniques to simulation", Proceedings of the 40th conference on winter simulation.
Shah, R., and Ward, P.T., (2007). "Defining and developing measures of lean production", Journal of Operations Management, Vol. 25, pp. 785–805.
Tirkolaee E.B., Mahmoodkhani J., Bourani M.R., and Tavakkoli-Moghaddam R. (2019). "A Self-Learning Particle Swarm Optimization for Robust Multi-Echelon Capacitated Location–Allocation–Inventory Problem", Journal of Advanced Manufacturing Systems, Vol. 18, No. 04, pp. 677-694.
Tregubov, A., and Laneb, J.A. (2015). "Simulation of Kanban-based Scheduling for Systems of Systems: Initial Results", Procedia Computer Science, Vol. 44, pp. 224–233.
Wang S., Sarker, B. R. (2005). "An assembly-type supply chain system controlled by Kanbans under a just-in-time delivery policy", European Journal of Operational Research, Vol. 162, pp. 153–172.
Journal of Industrial Engineering and Management Studies
Volume 7, Issue 1
June 2020
Pages 87-106
Files
Share
How to cite
Statistics