Scenario-Based Model for Multi-Product Multi-Level Supply Chain Management with Stochastic Demand and Waiting Time

Document Type : Research Paper

Authors

Department of Industrial Engineering, Yazd University, Yazd, Iran

Abstract

In this paper, a model is presented for managing multi-product multi-level supply chain by using predictive control of scenario-based model, which can handle supply chain with stochastic programming of the uncertainty that is caused by demand and random waiting time. In addition, it guarantees a certain level of customer service in the form of horizon at 95% level of confidence. Probabilistic waiting time may lead to lash effect throughout the entire chain, and causes shortages at different levels. Therefore, cost of facing shortages is considered in the model. After modeling, the problem is solved in probabilistic state, and to solve larger problems imperialist competitive algorithm is used. Results indicate that SCMPC is computationally very efficient, and offers significant advantage over the robust and stochastic optimization.

Keywords

Main Subjects


  1. Sarimveis, H., Patrinos, P., Tarantilis, C., and Kiranoudis, C. (2008). “Dynamic Modeling and Control of Supply Chain Systems”, Computers and Operations Research, Vol. 35, No. 11, PP. 3530–3561.
  2. Shapiro, J.F. (2007). “Modeling the Supply Chain”, South-Western College Pub, Inc., NJ, USA.
  3. Afshari, H., AminNayeri, M., and Ardestanijaafari, A. (2010). “Optimizing Inventory Decisions in Facility Location within Distribution Network Design”, Proceedings of the International Multiconference of Engineers and Computer Scientists,
  4. Sadjady, H., and Davoudpour, H. (2011). “Two-Echelon, Multi-Commodity Supply Chain Network Design with Mode SelectionLead-Times and Inventory Costs”, Computers & Operation Research, Vol. 39, No. 7, PP. 1266–1283.
  5. Lau H., Nakandala D., and Shum P. (2016). “A Case-Based Roadmap for Lateral Transshipment in Supply Chain Inventory Management”, J Inform Syst Technol Manag, Vol. 13, No. 1, PP. 27–44.
  6. Zhu, S. X. (2015). “Analysis of Dual Sourcing Strategies under Supply Disruptions”, International Journal of Production Economics, Vol. 170, No. 1, PP. 191-203.
  7. Melo, M. T., Nickel, S., and SaldanhaDaGama, F. (2009). “Facility Location and Supply Chain Management–A Review”, European Journal of Operational Research, Vol. 196, No. 2, PP. 401-412.
  8. Sabri, E. H., and Beamon, B. M. (2000). “A MultiObjective Approach to Simultaneous Strategic and Operational Planning in Supply Chain Design”, Omega, Vol. 28, No. 5, PP. 581-598.
  9. Snyder, L. V. (2006). “Facility Location Under Uncertainty: A Review”, IIE Transactions, Vol. 38, No. 7, PP. 547-564.
  10. Klibi, W., Martel, A., and Guitouni, A. (2010). “The Design of Robust Value-Creating Supply Chain Networks: A Critical Review”, European Journal of Operational Research, Vol. 203, No. 2, PP. 283-293
  11. Tsiakis, P., Nilay S., and Constantinos C. P. (2001). “Design of MultiEchelon Supply Chain Networks Under Demand Uncertainty”, Industrial and Engineering Chemistry Research, Vol. 40, No. 16, PP. 3585-3604
  12. Hinojosa, Y. (2008). “Dynamic Supply Chain Design with Inventory”, Computers and Operations Research, Vol. 35, No. 2, PP. 373-391.
  13. Zhang, R. Q, and RuPing W. (2009). “ScenarioBased Stochastic Capacity Planning Model and Decision Risk Analysis”, Systems EngineeringTheory and Practice. Vol. 29, No. 1, PP. 55-63.
  14. Mayne, D., Rawlings, J., Rao, C., and Scokaert, P. (2000). “Constrained Model Predictive Control: Stability and Optimality”, Automatica, Vol. 36, No. 6, PP. 789–814.
  15. PereaL´Opez, E., Ydstie, B., and Grossmann, I. (2003). “A Model Predictive Control Strategy for Supply Chain Optimization”, Computers and Chemical Engi Neering, Vol. 27, No. 8, PP. 1201–1218.
  16. Fakhrzad, M.B., Heydari, M., 2008. A Heuristic Algorithm for Hybrid Flow-shop Production Scheduling to Minimize the Sum of The Earliness ANDF Tardiness Costs. Journal of the Chinese Institute of Industrial Engineers. 25 (2), 105-115.
  17. Sarimveis, H., Patrinos, P., Tarantilis, C., and Kiranoudis, C. (2008). “Dynamic Modeling and Control of Supply Chain Systems”, Computers and Operations Research, Vol. 35, No. 11, PP. 3530–3561.
  18. Maestre, J. M., Mu˜noz De La Pe˜na, D., Camacho, E. F., and Alamo, T. (2011). “Distributed Model Predictive Control Based on Agent Negotiation”, Journal of Process Control, Vol. 21, No. 5, PP. 685–697.
  19. Fakhrzad, M.B., A. Sadri Esfahani, (2013). Modeling the time windows vehicle routing problem in cross-docking strategy using two meta-heuristic algorithms. International Journal of Engineering-Transactions A: Basics 27 (7), 1113.
  20. Jurado, I., Maestre, J. M., Velarde, P., Ocampo-Martinez, C., Fern´Andez, I., Isla Tejera, B., and Del Prado, J. (2016). “Stock Management in Hospital Phar Macy Using ChanceConstrained Model Predictive Control”, Computers in Bi Ology and Medicine, Vol. 72, No. 1, PP. 248-255.
  21. Wang, W., Rivera, D. E., and Kempf, K. G. (2005). “A Novel Model Predictive Control Algorithm for Supply Chain Management in Semiconductor Manufacturing”, in: American Control Conference. Portland (OR), United States.
  22. Grosso, J., OcampoMart´Inez, C., Puig, V., and Joseph, B. (2014). “Chance Constrained Model Predictive Control for Drinking Water Networks”, Journal of Process Control, Vol. 24, No.5, PP. 504–516.
  23. Li, X., Marlin, T., (2009). Robust supply chain performance via model predictive control. Computers & Chemical Engineering, Vol .33, No.1, PP. 2134–2143.
  24. Sohdi, M., and Tang, C. (2012). “Managing Supply Chain Risk”, Springer, NewYork et al.
  25. Schildbach, G., and Morari, M. (2016). “Scenario-Based Model Predictive Control for Multi-Echelon Supply Chain Management”, European Journal of Operational Research, Vol. 252, No. 2, PP. 540-549.
  26. Campi, M., and Garatti, S. (2008). “The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs”, SIAM Journal on Optimization, Vol. 19, No. 3, PP. 1211–1230.
  27. Schildbach, G., Fagiano, L., Frei, C., and Morari, M. (2014). “The Sce Nario Approach for Stochastic Model Predictive Control with Bounds on ClosedLoop Constraint Violations”, Automatica, Vol. 50, No. 12, PP. 3009-3018.
  28. Bitran, G., and Yanasse, H. (1984). “Deterministic Approximations to Stochastic Production Problems”, Operations Research, Vol. 32, No, 5. PP. 999–1018.
  29. Bookbinder, J., and Tan, J. Y. (1988). “Strategies for the Probabilistic LotSizing Problem with ServiceLevel Constraints”, Management Science, Vol. 34, No.5, PP. 1096–1108.
  30. Oke, A., and Gopalakrishnan, M. (2009). “Managing Disruptions in Supply Chains: A Case Study of A Retail Supply Chain”, International Journal of Production Economics, Vol. 118, No. 1, PP. 168–174.
  31. AtashpazGargari, E., and Lucas, C. (2007). “Imperialist Competitive Algorithm. an Algorithm for Optimization Inspired by Imperialist Competitive”, IEEE Congress on Evolutionary Computation, Singapore.2007.
  32. Kermani.A. A. K, Aliahmadi. A., Salamat. A., Barzinpour. F., and Hadiyan, E. (2014). “Supplier Selection in a SingleEchelon Supply Chain with Horizontal Competition Using Imperialist Competitive Algorithm”, International Journal of Computer Integrated Manufacturing, Vol. 28. No. 6, PP. 628-638.
  33. Fakhrzad, M.B., Moobed, M., (2010), A GA model development for decision making under reverse logistics. International Journal of Industrial Engineering and Production Research.21 (4)211-220.