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Collaborative production planning of supply chain under price and demand uncertainty

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  • Zhang, Guoquan
  • Shang, Jennifer
  • Li, Wenli
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    Abstract

    This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 215 (2011)
    Issue (Month): 3 (December)
    Pages: 590-603

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    Handle: RePEc:eee:ejores:v:215:y:2011:i:3:p:590-603

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Supply chain management Uncertainty modeling Production planning Fuzzy sets Stochastic chance-constrained programming Evolutionary computations;

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    Cited by:
    1. Volling, Thomas & Matzke, Andreas & Grunewald, Martin & Spengler, Thomas S., 2013. "Planning of capacities and orders in build-to-order automobile production: A review," European Journal of Operational Research, Elsevier, vol. 224(2), pages 240-260.
    2. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
    3. Sun, Wei & Huang, Guo H. & Lv, Ying & Li, Gongchen, 2013. "Inexact joint-probabilistic chance-constrained programming with left-hand-side randomness: An application to solid waste management," European Journal of Operational Research, Elsevier, vol. 228(1), pages 217-225.

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