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A hybrid heuristic algorithm for the multistage supply chain network problem

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Abstract

In recent years, many developments in logistics were connected to the need for information in an efficient supply chain flow. The supply chain is often represented as a network called a supply chain network (SCN) that is comprised of nodes that represent facilities (suppliers, plants, distribution centers and customers). Arcs connect these nodes along with the production flow. A multistage SCN (MSCN) is a sequence of multiple SCN stages. The flow can only be transferred between two consecutive stages. The MSCN problem involves the choice of facilities (plants and distribution centers) to be opened and the distribution network design must satisfy the demand with minimum cost. In this paper, a revised mathematical model is first proposed to correct the fatal error appearing in the existing models. An efficient hybrid heuristic algorithm (HHA) was developed by combining a greedy method (GM), the linear programming technique (LP) and three local search methods (LSMs) (always used in solving the scheduling problem). The pair-wise exchange procedure (XP), the insert procedure (IP) and the remove procedure (RP) to solve the MSCN problem. Preliminary computational experiments demonstrate the efficiency and performance of the proposed HHA.

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References

  1. Syarif A, Yun YS, Gen M (2002) Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach. Comput Ind Eng 43:299–314

    Article  Google Scholar 

  2. Chopra S, Meindl P (2003) Supply chain management: strategy, planning and operation, 2nd edn. Prentice-Hall, New Jersey

    Google Scholar 

  3. Stadtler H, Kilger C (eds) (2002) Supply chain management and advanced planning. Springer, Berlin Heidelberg New York

  4. Poirier CC (1999) Advanced supply chain management: how to build a sustained competitive advantage. Berrett-Kochler, San Francisco, CA

    Google Scholar 

  5. Forgionne GA (1983) Corporate management science activities: an update. Interfaces 13(3):20–23

    Google Scholar 

  6. Tragantalerngsak S, Holt J, Ronnqvist M (1998) Lagrangian heuristics for the two-echelon, single-source, capacitate location problem. Eur J Oper Res 102:611–625

    Article  Google Scholar 

  7. Azevedo L, Sousa JP (2000) Order planning for networked make-to-order enterprises – a case study. J Oper Res Soc 51:1116–1127

    Article  Google Scholar 

  8. Kaufman L, Eede MV, Hansen P (2000) A plant and warehouse location problem. Oper Res Q 28:547–554

    Google Scholar 

  9. Lee CY (1993) A cross decomposition algorithm for a multi-product multi-type facility location problem. Comput Oper Res 20:527–540

    Article  Google Scholar 

  10. Yu H (1997) ILOG in the supply chain. ILOG technical report

  11. Gen M, Cheng R (1997) Genetic algorithm and engineering design. Wiley, New York

  12. Yeh WC (2001) An efficient branch-and-bound algorithm for the two-machine bicriteria flowshop scheduling problem. J Manuf Syst 20(2):113–123

    Google Scholar 

  13. Yeh WC (1999) A new branch-and-bound approach for the n/2/Flowshop/αF+βCmax flowshop scheduling problem. Comput Oper Res 26(13):1293–1310

    Article  Google Scholar 

  14. Gottlieb J, Julstrom BA, Raidl GR, Rothlauf F (2001) Prufer numbers: a poor representation of spanning trees for evolutionary search. SAP Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA, pp 343–350

    Google Scholar 

  15. Krishnamoorthy M, Ernst AT, Sharaiha YM (1999) Comparison of algorithms for the degree constrained minimum spanning tree. Technical report, CSIRO Mathematical and Information Sciences. Clayton, Australia

  16. Bazaraa MS, Jarvis JJ (1990) Linear programming and network flows, 2nd edn. Wiley, New York

  17. Dantzig GB (1963) Linear programming and extensions. Princeton University Press, Princeton, NJ

  18. Bland RG, Goldfarb D, Todd MJ (1981) The ellipsoid method: a survey. Oper Res 29:1039–1091

    Google Scholar 

  19. Karmarkar N (1984) A new polynomial-time algorithm for linear programming. Combinatorica 4:373–395

    Google Scholar 

  20. Yeh WC (2000) A memetic algorithm for the min k-cut problem. Control Intell Syst 28(2):47–55

    Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Applied Mathematics, e-Integration & Collaboration Laboratory, National Chiayi University, 67-100, Taichung, Taiwan, 408

    Wei-Chang Yeh

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  1. Wei-Chang Yeh
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Correspondence to Wei-Chang Yeh.

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Yeh, WC. A hybrid heuristic algorithm for the multistage supply chain network problem. Int J Adv Manuf Technol 26, 675–685 (2005). https://doi.org/10.1007/s00170-003-2025-z

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  • DOI: https://doi.org/10.1007/s00170-003-2025-z

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