Review of Inventory Control Models: A Classification Based on Methods of Obtaining Optimal Control Parameters

1. Amaran, S., Sahinidis, N., Sharda, B. and Bury, S. (2016) Simulation optimization: a review of algorithms and applications. Annals of Operations Research, 240(1), pp. 351-380.10.1007/s10479-015-2019-xSearch in Google Scholar

2. Andradottir, S. and Prudius, A. (2010) Adaptive random search for continuous simulation optimization. Naval Research Logistics, 57, pp. 583-604.10.1002/nav.20422Search in Google Scholar

3. Angun, E. (2011) A risk-averse approach to simulation optimization with multiple responses. Simulation Modeling Practice and Theory, 19, pp. 911-923.10.1016/j.simpat.2010.12.006Search in Google Scholar

4. Angun, E., Kleijnen, J., den Hertog, D. and Gurkan, G (2009) Response surface methodology with stochastic constraints for expensive simulation. Journal of the Operational Research Society, 60, pp. 735-746.10.1057/palgrave.jors.2602614Search in Google Scholar

5. Ansoff, H. and Slevin, D. (1968) An appreciation of industrial dynamics. Management science, 14(7), pp. 383-397.10.1287/mnsc.14.7.383Search in Google Scholar

6. Aras, N., Verter, V. and Boyaci, T. (2006) Coordination and priority decisions in hybrid manufacturing/remanufacturing systems. Production and Operations Management, 15(4), pp. 528-543.10.1111/j.1937-5956.2006.tb00161.xSearch in Google Scholar

7. Arreola-Risa, A., Gimenez-Garcia, V. and Martinez-Parra, J. (2011) Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis. European Journal of Operational Research, 213(1), pp. 107-118.10.1016/j.ejor.2011.02.031Search in Google Scholar

8. Arrow, K., Harris, T., and Marschak, J. (1951) Optimal Inventory Policy. Econometrica, 29, pp. 250-272.10.2307/1906813Search in Google Scholar

9. Arrow, K., Karlin, S., and Scarf, H. (1958) Studies in the Mathematical Theory of Inventory and Production. Stanford, CA: Stanford University Press.Search in Google Scholar

10. Axsater, S. (1985) Control theory concepts in production and inventory control. International Journal of Systems Science, 16, pp. 161-169.10.1080/00207728508926662Search in Google Scholar

11. Banks, J., Carson, J., Nelson, B., and Nicol, D. (2000) Discrete Event Systems Simulation. 3^rd edition. Prentice Hall, Englewood Cliffs, New Jersey.Search in Google Scholar

12. Bartmann, D. and Bach, M. (2012) Inventory control: models and methods. Springer Science & Business Media, p. 318.Search in Google Scholar

13. Becerril-Arreola, R., Leng, M. and Parlar, M. (2013) Online retailers’ promotional pricing, free-shipping threshold, and inventory decisions: A simulation-based analysis. European Journal of Operational Research, 230(2), pp. 272-283.10.1016/j.ejor.2013.04.006Search in Google Scholar

14. Bellman, R. (1957) Dynamic Programming. Princeton, Princeton University Press.Search in Google Scholar

15. Bendoly, E. (2004) Integrated inventory pooling for firms servicing both on-line and store demand. Computers & Operations Research, 31(9), pp. 1465-1480.10.1016/S0305-0548(03)00102-3Search in Google Scholar

16. Benjaafar, S., Gayon, J. and Tepe, S. (2010) Optimal control of a production–inventory system with customer impatience. Operations Research Letters, 38(4), pp. 267-272.10.1016/j.orl.2010.03.008Search in Google Scholar

17. Biles, W., Kleijnen, J., Van Beers, W. and Van Nieuwenhuyse, I. (2007) Kriging metamodeling in constrained simulation optimization: an explorative study. In: Proceedings of the 2007 Winter Simulation Conference. IEEE, pp. 355-362.10.1109/WSC.2007.4419623Search in Google Scholar

18. Blanning, R. (1975) The construction and implementation of metamodels, Simulation, 24, pp. 177-184.Search in Google Scholar

19. Bollapragada, R. and Rao, U. (2006) Replenishment planning in discrete-time, capacitated, non-stationary, stochastic inventory systems. IIE Transactions, 38(7), pp. 605-617.10.1080/07408170600634533Search in Google Scholar

20. Bushuev, M., Guiffrida, A., Jaber, M. and Khan, M. (2015) A review of inventory lot sizing review papers. Management Research Review, 38(3), pp. 283-298.10.1108/MRR-09-2013-0204Search in Google Scholar

21. Can, B. and Heavey, C. (2012) A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Computers & Operations Research, 39, pp. 424-436.10.1016/j.cor.2011.05.004Search in Google Scholar

22. Chen, Y., Pekny, J. and Reklaitis, G. (2012) Integrated planning and optimization of clinical trial supply chain system with risk pooling. Industrial & engineering chemistry research, 52(1), pp.152-165.10.1021/ie300823bSearch in Google Scholar

23. Cimen, M. and Kirkbride, C. (2017) Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems. International Journal of Production Research, 55(7), pp. 2034-2050.10.1080/00207543.2016.1264643Search in Google Scholar

24. Clark, A and Scarf, H. (1960) Optimal policies for a multi-echelon inventory problem. Management Science, pp. 475-490.10.1287/mnsc.6.4.475Search in Google Scholar

25. Ding, H., Benyoucef, L. and Xie, X. (2009) Stochastic multi-objective production-distribution network design using simulation-based optimization. International Journal of Production Research, 47(2), pp. 479-505.10.1080/00207540802426540Search in Google Scholar

26. Domschke, W., Drexl, A., Klein, R. and Scholl, A. (2015) Einführung in Operations Research, 9, Berlin, Heidelberg: Springer Gabler.10.1007/978-3-662-48216-2Search in Google Scholar

27. Duan, Q. and Liao, T. (2013) Optimization of replenishment policies for decentralized and centralized capacitated supply chains under various demands. International Journal of Production Economics, pp. 194-204.10.1016/j.ijpe.2012.11.004Search in Google Scholar

28. Dubois, G. (2018) Modeling and Simulation: Challenges and Best Practices for Industry. CRC Press.Search in Google Scholar

29. Edgeworth, F. (1888) The Mathematical Theory of Banking. Journal of Royal Statistical Society, 51(1), pp. 113-127.Search in Google Scholar

30. Forrester, J. (1961) Industrial Dynamics. Cambridge, MA, MIT Press, 36, pp. 37-66.Search in Google Scholar

31. Forrester, J. (1973) World Dynamics. Cambridge, MA, MIT Press, pp. 17-29.Search in Google Scholar

32. Fu, M. (1994) Sample path derivatives for (s, S) inventory systems. Operations Research, 42(2), pp. 351-364.10.1287/opre.42.2.351Search in Google Scholar

33. Gosavi, A. (2015) Simulation-based optimization. Berlin: Springer.10.1007/978-1-4899-7491-4Search in Google Scholar

34. Harris, F. (1913) Operations and Cost. Factory Management Series. Chicago: A.W. Shaw and Company.Search in Google Scholar

35. Hong, L. and Nelson, B. (2009) A brief introduction to optimization via simulation. In: Proceedings of the 2009 Winter Simulation Conference, pp. 314-322.10.1109/WSC.2009.5429321Search in Google Scholar

36. Jackson, I. (2019a) Simulation-Optimisation Approach to Stochastic Inventory Control with Perishability. Information Technology & Management Science, 22, pp. 9-14.10.7250/itms-2019-0002Search in Google Scholar

37. Jackson, I. (2019b) Neuroevolutionary Approach to Metamodeling of Production-Inventory Systems with Lost-Sales and Markovian Demand. In: Proceedings of the International Conference on Reliability and Statistics in Transportation and Communication. Springer, Cham, pp. 90-99.10.1007/978-3-030-44610-9_10Search in Google Scholar

38. Jad, F. and Owayjan, M. (2017) ERP Neural Network Inventory Control. Procedia Computer Science, 114, pp. 288-295.Search in Google Scholar

39. Jalali, H. and Nieuwenhuyse, I. (2015) Simulation optimization in inventory replenishment: a classification. IIE Transactions, 47(11), pp. 1217-1235.10.1080/0740817X.2015.1019162Search in Google Scholar

40. Jammernegg, W. (1981) On the optimality of generalized (s, t)-policies in production-inventory systems. Methods of Operations Research, 41, pp. 361-364.Search in Google Scholar

41. Kapuscinski, R. and Tayur, S. (1998) A capacitated production-inventory model with periodic demand. Operations Research, 46(6), pp. 899-911.10.1287/opre.46.6.899Search in Google Scholar

42. Karlin, S. (1960) Dynamic Inventory Policy with Varying Stochastic Demands. Management Science, 6(3), pp. 231-258.10.1287/mnsc.6.3.231Search in Google Scholar

43. Khmelnitsky, E. and Gerchak, Y. (2002) Optimal control approach to production systems with inventory-level-dependent demand. Transactions on Automatic Control, 47(2), IEEE, pp. 289-292.10.1109/9.983360Search in Google Scholar

44. Koulouriotis, D., Xanthopoulos, A., and Tourassis, V. (2010) Simulation optimization of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms. International Journal of Production Research, 48, pp. 2887-2912.10.1080/00207540802603759Search in Google Scholar

45. Law, A. and Kelton, W. (2000) Simulation modeling and analysis. New York, McGraw-Hill.Search in Google Scholar

46. Lechevalier, D., Hudak, S., Ak, R., Lee, Y. T., and Foufou, S (2015) A neural network meta-model and its application for manufacturing. In: Proceedings of the IEEE International Conference on Big Data, Santa Clara, USA.10.1109/BigData.2015.7363903Search in Google Scholar

47. Lehmer, D. (1951) Mathematical methods in large-scale computing units. Annual Computational Laboratory, 26, Harvard University, pp. 141-146.Search in Google Scholar

48. Lin, Y., Shie, J. and Tsai, C. (2009) Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication. Expert Systems with Applications, 36(2), pp. 3421-3427.10.1016/j.eswa.2008.02.009Search in Google Scholar

49. Lukinskiy, V. and Lukinskiy, V. (2017) Models of Inventory Management in Multi-level Distribution Systems. In: International Conference on Reliability and Statistics in Transportation and Communication, Springer, Cham, pp. 313-319.Search in Google Scholar

50. Mahdavi, A. and Wolfe-Adam, T. (2019) Artificial Intelligence and Simulation in Business. White Paper, AnyLogic company, pp. 3-32.Search in Google Scholar

51. Nezhad, A. and Mahlooji, H. (2014) An artificial neural network meta-model for constrained simulation optimization. Journal of the Operational Research Society, 65, pp. 1232-1244.10.1057/jors.2013.73Search in Google Scholar

52. Olafsson, S. (2006) Metaheuristics. Handbooks in Operations Research and Management Science, 13, North Holland. Elsevier, pp. 633-654.10.1016/S0927-0507(06)13021-2Search in Google Scholar

53. Pasupathy, R. and Ghosh, S. (2013) Simulation optimization: A concise overview and implementation guide. Operations Research, 7, INFORMS, pp. 122-150.10.1287/educ.2013.0118Search in Google Scholar

54. Peirleitner, A., Altendorfer, K. and Felberbauer, T. (2016) A simulation approach for multi-stage supply chain optimization to analyze real world transportation effects. In: Proceedings of the 2016 Winter Simulation Conference, IEEE, pp. 2272-2283.10.1109/WSC.2016.7822268Search in Google Scholar

55. Pidd, M. (1998) Computer simulation in management science. Chichester: Wiley.Search in Google Scholar

56. Powell, J. and Bradford, J. (2000) Targeting intelligence gathering in a dynamic competitive environment. International Journal of Information Management, 20, pp. 181-195.10.1016/S0268-4012(00)00004-9Search in Google Scholar

57. Prestwich, S. D., Tarim, S. A., Rossi, R. and Hnich, B. (2012) A neuroevolutionary approach to stochastic inventory control in multi-echelon systems. International Journal of Production Research, 50, pp. 2150-2160.10.1080/00207543.2011.574503Search in Google Scholar

58. Sarimveis, H., Patrinos, P., Tarantilis, C. and Kiranoudis, C. (2008) Dynamic modeling and control of supply chain systems: A review. Computers & operations research, 35(11), pp. 3530-3561.10.1016/j.cor.2007.01.017Search in Google Scholar

59. Scarf, H. (1960) The Optimality of (s, S) Policies in the Dynamic Inventory Problem. Mathematical Methods in the Social Sciences. Stanford, CA: Stanford University Press, pp. 196-202.Search in Google Scholar

60. Schenk, M., Tolujew, J. and Reggelin, T. (2010) A mesoscopic approach to the simulation of logistics systems. In: International Heinz Nixdorf Symposium. Springer, Berlin, Heidelberg, pp. 15-25.10.1007/978-3-642-12494-5_2Search in Google Scholar

61. Sethi, S. and Thompson, G. (1981) Dynamic optimal control models in advertising: recent developments. Management Science, 40, pp. 195-226.Search in Google Scholar

62. Shreve, S. (1976) Abbreviated proof in the lost sales case. Dynamic Programming and Stochastic Control. New York: Academic Press, pp. 105-106.Search in Google Scholar

63. Simchi-Levi, D. and Zhao, Y. (2003) The value of information sharing in a two-stage supply chain with production capacity constraints. Naval Research Logistics, 50, pp. 888-916.10.1002/nav.10094Search in Google Scholar

64. Simon, H. (1952) On the application of servomechanism theory in the study of production control. Econometrica, 20, pp. 247-268.10.2307/1907849Search in Google Scholar

65. Taleizadeh, A., Zarei, H. and Sarker, B. (2017) An optimal control of inventory under probabilistic replenishment intervals and known price increase. European Journal of Operational Research, 257(3), pp. 777-791.10.1016/j.ejor.2016.07.041Search in Google Scholar

66. Tocher, K. and Owen, D. (1960) The automatic programming of simulations. In: Proceedings of the 2^nd International Conference on Operational Research. London. The English Universities Press Ltd.Search in Google Scholar

67. Tsai, S. and Zheng, Y. (2013) A simulation optimization approach for a two-echelon inventory system with service level constraints. European Journal of Operational Research, 229, pp. 364-374.10.1016/j.ejor.2013.03.010Search in Google Scholar

68. Veinott, A. (1966) On the Optimality of (s, S) Inventory Policies: New Conditions and a New Proof. SIAM Journal on Applied Mathematics, 14(5), pp. 1067-1083.10.1137/0114086Search in Google Scholar

69. Wikner, J. (1994) Dynamic modelling and analysis of information flows in production-inventory and supply chain systems. Profil, Linkoping.Search in Google Scholar

70. Wolstenholme, E. (1982) Managing international mining - case study concerning complex ownership systems. European Journal of Operational Research, 9, pp. 133-143.10.1016/0377-2217(82)90065-0Search in Google Scholar

71. Zahedi-Hosseini, F. (2018) Modeling and simulation for the joint maintenance-inventory optimization of production systems. In: Proceedings of the 2018 Winter Simulation Conference, IEEE Press, pp. 3264-3274.10.1109/WSC.2018.8632283Search in Google Scholar

72. Zeballos, A., Seifert, R. and Protopappa-Sieke, M. (2013) Single product, finite horizon, periodic review inventory model with working capital requirements and short-term debt. Computers & Operations Research, 40(12), pp. 2940-2949.10.1016/j.cor.2013.07.008Search in Google Scholar

73. Zipkin, P. (2008) Old and New Methods for Lost-Sales Inventory Systems. Operations Research, 56(5), pp. 1256-1263.10.1287/opre.1070.0471Search in Google Scholar

74. Zvirgzdina, B. and Tolujew, J. (2016) Experience in Optimization of Discrete Rate Models Using ExtendSim Optimizer. In: Proceedings of the 9^th International Doctoral Students Workshop on Logistics, June, 2016.Magdeburg, Otto von Guericke University, pp. 95-101.Search in Google Scholar