References
[1] Li, H. B., Pedrielli, G., Lee, L. H., Chew, E. P. (2017). Enhancement of Supply Chain Resilience Through In-ter-Echelon Information Sharing. Flexible Services and Manufacturing Journal, 29, 260-285.
[2] Parra, J. F., Jaramillo, P., Aramburo, S. A. (2018). Metaheuristic Optimization Methods for Calibration of System Dynamics Models. Journal of Simulation, 12, 190-209.
[3] Chen, L., Lee, H. L. (2012). Bullwhip Effect Measurement and Its Implications. Operations Research, 60, 771-784.
[4] Choi, T., Li, J., Wei, Y. (2013). Will A Supplier Benefit From Sharing Good Information With A Retailer? Decision Support Systems, 56, 131-139.
[5] Cui, R., Allon, G., Bassamboo, A., Mieghem, J. V. (2015). Information Sharing in Supply Chains: An Empirical and Theoretical Valuation. Management Science, 61, 2803-2824.
[6] Khosroshahi, H., Husseini, S. M. M., Marjani, M. R. (2016). The Bullwhip Effect in A 3-Stage Supply Chain Con-sidering Multiple Retailers Using A Moving Average Method For Demand Forecasting. Applied Mathematical Modelling, 40, 8934-8951.
[7] Costantino, F., Gravio, G. D., Shaban, A., et al. (2015). SPC Forecasting System to Mitigate the Bullwhip Effect and Inventory Variance in Supply Chains. Expert Systems with Applications, 42, 1773-1787.
[8] Hassanzadeh, A., Jafarian, A., Amiri, M. (2014). Modeling and Analysis of the Causes of Bullwhip Effect in Cen-tralized and Decentralized Supply Chain Using Response Surface Method. Applied Mathematical Modelling, 38, 2353-2365.
[9] Nepal, B., Murat, A., Chinnam, R. B. (2012). The Bullwhip Effect In Capacitated Supply Chains With Considera-tion for Product Life-Cycle Aspects. International Journal of Production Economics, 136, 318-331.
[10] Isaksson, O. D., Seifert, R. W. (2016). Quantifying The Bullwhip Effect Using Two-Echelon Data: A Cross-Industry Empirical Investigation. International Journal of Production Economics, 171, 311-320.
[11] Naim, M. M., Spiegler, V. L., Wikner, J., Vikner, J., Towill, D. R. (2017). Identifying the Causes of the Bullwhip Effect by Exploiting Control Block Diagram Manipulation with Analogical Reasoning. European Journal of Oper-ational Research, 263, 240-246.
[12] Linnéusson, G., Ng, A. C., Aslam, T. (2018). Quantitative Analysis of A Conceptual System Dynamics Mainten-ance Performance Model Using Multi-Objective Optimisation. Journal of Simulation, 12, 171-189.
[13] Agrawal, S., Sengupta, R. N., Shanker, K. (2009). Impact of Information Sharing and Lead Time on Bullwhip Ef-fect and On-hand Inventory. European Journal of Operational Research, 192, 576-593.
[14] Dragovic, B., Tzannatos, E., Park, N. K. (2017). Simulation Modelling in Ports and Container Terminals: Literature Overview and Analysis by Research Field, Application Area and Tool. Flexible Services and Manufacturing Jour-nal, 29, 4-34.
[15] Warren, L. T., Chang, P. C. (2010). Impacts of Forecast, Inventory Policy, and Lead Time on Supply Chain Inven-tory: A Numerical Study. International Journal of Production Economics, 128, 527-537.
[16] Hussain, M., Saber, H. (2012). Exploring The Bullwhip Effect Using Simulation And Taguchi Experimental Design. International Journal of Logistics: Research and Applications, 15, 1-19.
[17] Heshmat, M., Eltawil, A. (2018). A System Dynamics-Based Decision Support Model For Chemotherapy Planning. Journal of Simulation, 12, 283-294.
[18] Shamsaei, F., Vyve, M. V. (2017). Solving Integrated Production and Condition-Based Maintenance Planning Problems by MIP Modelling. Flexible Services and Manufacturing Journal, 29, 184-202.