The paper describes an eventual combination of discrete-event simulation and genetic algorithm to define the optimal inventory policy in stochastic multi-product inventory systems. The discrete-event model under consideration corresponds to the just-in-time inventory control system with a flexible reorder point. The system operates under stochastic demand and replenishment lead time. The utilized genetic algorithm is distinguished for a non-binary chromosome encoding, uniform crossover and two mutation operators. The paper contains a detailed description of the optimization technique and the numerical example of six- product inventory model. The proposed approach contributes to the field of industrial engineering by providing a simple, but still efficient way to compute nearly-optimal inventory parameters with regard to risk and reliability policy. Besides, the method may be applied in automated ordering systems.
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1. Alizadeh, M., Eskandari, H., Sajadifar, S. and Geiger, C. (2011) Analysing a stochastic inventory system for deteriorating items with stochastic lead time using simulation modelling. In: Proceedings of the 2011 winter simulation conference. Winter Simulation Conference, pp. 1650-1662.
2. Altiparmak, F., Gen, M., Lin, L. and Paksoy, T. (2006) A genetic algorithm approach for multi- objective optimization of supply chain networks. Computers & industrial engineering, 51(1), 196–215. DOI:10.1016/j.cie.2006.07.011
3. Bijvank, M. and Vis, I.F.A. (2011) Lost-sales inventory theory: A review. European Journal of Operational Research, 215(1), 1 – 13. DOI:10.1016/j.ejor.2011.02.004
4. Bookbinder, J.H. and Cakanyildirim, M. (1999) Random lead times and expedited orders in (Q, r) inventory systems. European Journal of Operational Research, 115(2), 300–313.
9. Juan, A.A., Faulin, J., Grasman, S.E., Rabe, M. and Figueria, G. (2015) A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspective, 2, 62–72. DOI:10.1016/j.orp.2015.03.001
10. Juan, A.A., Grasman, S.E., Caceres-Cruz, J. and Bektaş, T. (2014) A simheuristic algorithm for the single-period stochastic inventory-routing problem with stock-outs. Simulation Modelling Practice and Theory, 46, 40–52. DOI:10.1016/j.simpat.2013.11.008
11. Kotz, S. and Van Dorp, R.J. (2004) Beyond Beta: Other Continuous Families of Distributions with Bounded Support and Applications. World Scientific.
12. Kouki, C., Jemai, K. and Minner, S. (2015) A Lost Sales (r,Q) Inventory Control Model for Perishables with Fixed Lifetime and Lead Time. Int. J. Production Economics, 168, 143–157.
13. Luke, S. (2015) Essentials of Metaheuristics. A Set of Undergraduate Lecture Notes. Second Edition, 2.2, pp. 31-55.
14. Man, K.F., Tang, K.S. and Kwong, S. (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE transactions on Industrial Electronics, 43(5), 519–534. DOI:10.1109/41.538609
15. Michalewicz, Z. (1996) Evolution strategies and other methods. In: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin, Heidelberg, pp. 159-177.
16. Miller, B.L. and Goldberg, D.E. (1995) Genetic algorithms, tournament selection, and the effects of noise. Complex systems, 9(3), pp. 193–212. DOI:10.1162/evco.19184.108.40.206
17. Min, Z. and Lindu, Z. (2016) Arena Simulation of Multi-Level Medicine Inventory Control in Hospital Pharmacy. International Journal of Hybrid Information Technology, 9(6), pp.283-294.
18. Pasandideh, S.H.R. and Niaki, S.T.A. (2008) A genetic algorithm approach to optimize a multi- products EPQ model with discrete delivery orders and constrained space. Applied Mathematics and Computation, 195(2), 506–514. DOI:10.1016/j.amc.2007.05.007
19. Pidd, M. (1998) Computer simulation in management science. ISBN:0471979317.
20. Scherfke, S. (2014) Discrete-event simulation with SimPy. OFFIS – Institute for Information Technologie, USA.
22. Subramanian, D., Pekny, J.F. and Gintaras, V.R. (2000) A simulation—optimization framework for addressing combinatorial and stochastic aspects of an R&D pipeline management problem. Computers & Chemical Engineering, 24(2-7), 1005–1011.
23. Sinaga, S., Pertiwi, L.S. and Ardian, T. (2016) Inventory Simulation Optimization under Non Stationary Demand. International Journal of Applied Engineering Research, 11(1), pp. 524-529.
24. Williams, E.A. and Crossley, W.A. (1998) Empirically-derived population size and mutation rate guidelines for a genetic algorithm with uniform crossover. In: soft computing in engineering design and manufacturing. Springer, London, pp. 163-172.
25. Yeh, W.C. and Chuang, M.C. (2011) Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Systems with applications, 38(4), 4244–4253. DOI:10.1016/j.eswa.2010.09.091
26. Zipkin, P.H. (2000) Foundations of inventory management. McGrawHill. ISBN-13:978-0256113792. Zvirgzdiņa, B. and Tolujew, J. (2016) Experience in Optimization of Discrete Rate Models Using ExtendSim Optimizer. In: 9th International Doctoral Students Workshop on Logistics, June, 2016. Magdeburg, Otto von Guericke University, pp. 95-100.