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A Two-Stage Recourse Model for Production Planning with Stochastic Demand

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Book cover Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3483))

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Abstract

Production planning problems play a vital role in the supply chain management. The methodology of production planning problem can provide the quantity of production and the workforce level at each production plant to fulfil market demand. This paper develops a stochastic programming model with additional constraints. A set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of the proposed model.

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References

  1. Baykasoglu, A.: MOAPPS 1.0: Aggregate Production Planning Using the Multiple-Objective Tabu Search. International Journal of Production Research 39, 3685–3702 (2001)

    Article  MATH  Google Scholar 

  2. Dantzig, G.B.: Linear Programming Under Uncertainty. Management Science 1, 197–206 (1955)

    Article  MATH  MathSciNet  Google Scholar 

  3. Hung, Y.F., Hu, Y.C.: Solving Mixed Integer Programming Production Planning Problems with Setups by Shadow Price Information. Computers and Operations Research 25, 1027–1042 (1998)

    Article  MATH  Google Scholar 

  4. Hung, Y.F., Shih, C.C., Chen, C.P.: Evolutionary Algorithms for Production Planning Problems with Setup Decisions. Journal of the Operational Research Society 50, 857–866 (1999)

    MATH  Google Scholar 

  5. Masud, A.S.M., Hwang, C.L.: An Aggregate Production Planning Model and Application of Three Multiple Objective Decision Methods. International Journal of Production Research 18, 741–752 (1980)

    Article  Google Scholar 

  6. Mazzola, J.B., Neebe, A.W., Rump, C.M.: Multiproduct Production Planning in the Presence of Work-Force Learning. European Journal of Operational Research 10, 336–356 (1998)

    Article  Google Scholar 

  7. Nam, S.J., Logendran, R.: Aggregate Production Planning – A Survey of Models and Methodologies. European Journal of Operational Research 61, 255–272 (1992)

    Article  MATH  Google Scholar 

  8. Ozdamar, L., Bozyel, M.A., Birbil, S.I.: A Hierarchical Decision Support System for Production Planning (with Case Study). European Journal of Operational Research 104, 403–422 (1998)

    Article  Google Scholar 

  9. Wang, D., Fang, S.C.: A Genetics-Based Approach for Aggregated Production Planning in a Fuzzy Environment. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Human 27, 636–645 (1997)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management Sciences, City University of Hong Kong, Hong Kong

    K. K. Lai & Stephen C. H. Leung

  2. College of Business Administration, Hunan University, China

    K. K. Lai

  3. School of Management, University of Southampton, Southampton, UK

    Yue Wu

Authors
  1. K. K. Lai
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  2. Stephen C. H. Leung
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  3. Yue Wu
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore

    Heow Pueh Lee

  6. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  7. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  8. OptimaNumerics Ltd, Belfast, United Kingdom

    Chih Jeng Kenneth Tan

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© 2005 Springer-Verlag Berlin Heidelberg

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Lai, K.K., Leung, S.C.H., Wu, Y. (2005). A Two-Stage Recourse Model for Production Planning with Stochastic Demand. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_28

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  • DOI: https://doi.org/10.1007/11424925_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25863-6

  • Online ISBN: 978-3-540-32309-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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