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Information Journal Paper

Title

DESIGN OF A COMBINED LUMPY DEMANDS FORECASTING AND A MODEL PREDICTIVE SCHEME FOR REDUCTION OF BULLWHIP EFFECT

Pages

  171-190

Abstract

 This paper presents a MODEL PREDICTIVE CONTROL (MPC) approach for production and inventory control systems. MODEL PREDICTIVE CONTROL previously has been successfully applied to supply chain problems; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing more insights in the future demands. In this paper, we investigate the applicability of advanced and traditional methods to forecasting lumpy demand at supply chains. Neural networks can be better model for forecast the nonlinear and chaotic behavior of lumpy demands in comparison with traditional and usual methods such as exponential smoothing. This paper efficiently employs neural networks as the main core in the proposed control design. To highlight efficiency of the proposed method, real datasets are used for investigation and comparison studies.

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    APA: Copy

    YOUSEFI ZENOUZ, REZA, & MEHNAJ, MOHAMMAD BAGHER. (2011). DESIGN OF A COMBINED LUMPY DEMANDS FORECASTING AND A MODEL PREDICTIVE SCHEME FOR REDUCTION OF BULLWHIP EFFECT. JOURNAL OF INDUSTRIAL MANAGEMENT, 3(6), 171-190. SID. https://sid.ir/paper/139980/en

    Vancouver: Copy

    YOUSEFI ZENOUZ REZA, MEHNAJ MOHAMMAD BAGHER. DESIGN OF A COMBINED LUMPY DEMANDS FORECASTING AND A MODEL PREDICTIVE SCHEME FOR REDUCTION OF BULLWHIP EFFECT. JOURNAL OF INDUSTRIAL MANAGEMENT[Internet]. 2011;3(6):171-190. Available from: https://sid.ir/paper/139980/en

    IEEE: Copy

    REZA YOUSEFI ZENOUZ, and MOHAMMAD BAGHER MEHNAJ, “DESIGN OF A COMBINED LUMPY DEMANDS FORECASTING AND A MODEL PREDICTIVE SCHEME FOR REDUCTION OF BULLWHIP EFFECT,” JOURNAL OF INDUSTRIAL MANAGEMENT, vol. 3, no. 6, pp. 171–190, 2011, [Online]. Available: https://sid.ir/paper/139980/en

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