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A periodic inventory control system with adaptive reference stock level for long supply delay

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Modern inventory control is anchored in vastly advanced and complex models, which require considerable computational efforts. In this paper, we use a mathematical model of an inventory system with large supply delay and control system in order to optimize goods flow in inventory systems. The paper proposes the use of automatic control systems to control the system for supplementary orders. A discrete-time, dynamic model of the warehouse system is assumed for the analysis. For the given model, two automatic control systems: adaptive and classical non-adaptive periodic inventory systems , are analyzed. The non-adaptive control system is well known in the literature and the second one is its extension. The parameters of the control system are tuned by minimizing the cost function using a genetic algorithm for the assumed scenario for the market demand. Results of numerical simulations of the dynamical system and selected results in the objective space are presented in the paper.
Wydawca
Rocznik
Strony
568--572
Opis fizyczny
Bibliogr. 20 poz., rys., wykr., wzory
Twórcy
  • West Pomeranian University of Technology Szczecin, Szczecin, Poland
autor
  • West Pomeranian University of Technology Szczecin, Szczecin, Poland
Bibliografia
  • [1] Abdelmaguid T. F., Dessouky M. M.: A genetic algorithm approach to the integrated inventory-distribution problem. International Journal of Production Research, 44.21, 2006, 4445-4464.
  • [2] Aviv Y.: A time-series framework for supply-chain inventory management. Operations Research, 51.2, 2003, 210-227.
  • [3] Aviv Y.: The effect of collaborative forecasting on supply chain performance. Management science, 47.10, 2001, 1326-1343.
  • [4] Baganha M. P., Cohen M. A.: The stabilizing effect of inventory in supply chains. Operations Research, 46.3-supplement-3, 1998, p. 72- S83.
  • [5] Blanchard, O. J.: The Production and Inventory Behavior of the American Automobile Industry. Journal of Political Economy, 92, 1983, 365-400.
  • [6] Blinder, A. S.: Retail Inventory Behavior and Business Fluctuations. Brookings Papers on Economic Activity, 1981, 443-505.
  • [7] Buchmeister B., et al.: Bullwhip effect problem in supply chains. Advances in production engineering & management, 3.1, 2008, 45-55.
  • [8] Chen L., Lee H. L.: Bullwhip effect measurement and its implications. Operations Research, 60.4, 2012, 771-784.
  • [9] Hašková S., Zeman R.: Storage Efficiency As a Managerial Optimization Problem. China-USA Business Review, February 2015, Vol. 14, No. 2, 59-66.
  • [10] Chen L., Lee. H. L. .: Information sharing and order variability control under a generalized demand model. Management Science, 55.5, 2009, 781-797.
  • [11] Chołodowicz E., Orłowski P.: Dynamiczny dyskretny model systemu magazynowego ze zmiennym w czasie opóźnieniem. Logistyka, No. 4, 2015, 31–35.
  • [12] Daniel J., Chandrasekharan R.: A simulation‐based genetic algorithm for inventory optimization in a serial supply chain. International Transactions in Operational Research, 12.1, 2005, 101-127.
  • [13] Disney S.M., Naim M.M., Towill D.R.: Genetic algorithm optimisation of a class of inventory control systems. International Journal of Production Economics, 68.3, 2000, 259-278.
  • [14] Forrester JW, Industrial dynamics. Waltham, MA: Pegasus Communications, 1961.
  • [15] Orłowski P., Grzyb S.: Congestion feedback control for computer networks with bandwidth estimation. Proc. 20th Int. Conf. on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 2015.
  • [16] Lee H. L., Padmanabhan V., Whang S.: The bullwhip effect in supply chains1. Sloan management review, 38.3, 1997, 93-102.
  • [17] Liu B., Esogbue A. O.: Decision criteria and optimal inventory processes. Vol. 20. Springer Science & Business Media, 2012.
  • [18] Metters R.: Quantifying the bullwhip effect in supply chains. Journal of operations management, 15.2, 1997, 89-100.
  • [19] Muller M.: Essentials of inventory management. AMACOM Div American Mgmt Assn, 2011.
  • [20] Pasandideh S. H. R., Niaki S. T. A., Nia A. R.: A genetic algorithm for vendor managed inventory control system of multi-product multiconstraint economic order quantity model. Expert Systems with Applications, 38.3, 2011, 2708-2716.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-26dc37f9-e3a3-46d2-986c-5691d7da027d
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