Skip to main content

Advertisement

SpringerLink
Log in

A solution procedure for integrated supply chain planning problem in open business environment using genetic algorithm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Enterprise applications are increasingly becoming capable of sharing information and coordinating decisions autonomously without any human intervention. This open business environment gives rise to several new supply chain planning problems that have to be solved by individual entities of a supply chain. This paper first mathematically formulates the supply chain planning problems emerging in the open business environment and second proposes a heuristic solution procedure based on the framework of genetic algorithm applicable to large-scale problems. The procedure has two stages as the original problem is decomposed into two sub-problems in an effort to reduce the overall problem complexity. The performance of the proposed algorithm is empirically proven to be effective in both solution quality and search time in various problem sizes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Amaro ACS, Barbosa-Povoa APFD (2008) Supply chain management with optimal scheduling. Ind Eng Chem Res 47(1):116–132

    Article  Google Scholar 

  2. Blomqvist E, Levashova T, Öhgren A, Sandkuhl K, Smirnov A, Tarassov V (2005) Configuration of dynamic SME supply chains based on ontologies. In: Proceedings of the 2nd International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS), Copenhagen, Denmark, August 2005, pp 246–256

  3. Chu PC, Beasley JE (1998) A genetic algorithm for the multidimensional knapsack problem. J Heuristics 4(1):63–86

    Article  MATH  Google Scholar 

  4. Chu PC, Beasley JE (1998) Constraint handling in genetic algorithms: the set partitioning problem. J Heuristics 4(4):323–357

    Article  MATH  Google Scholar 

  5. Falkenauer E, Bouffouix S (1991) A genetic algorithm for job-shop. Proceedings of the 1991 IEEE International Conference on Robotics and Automatisation, Sacramento, California, USA

  6. Gavish B, Pirkul H (1985) Efficient algorithms for solving multiconstraint zero-one knapsack problems to optimality. Math Program 31(1):78–105

    Article  MathSciNet  MATH  Google Scholar 

  7. Ghiassi M, Spera C (2003) Defining the Internet-based supply chain system for mass customized markets. Comput Ind Eng 45:17–41

    Article  Google Scholar 

  8. Gunasekaran A, Ngai EWT (2004) Information systems in supply chain integration and management. Eur J Oper Res 159:269–295

    Article  MathSciNet  MATH  Google Scholar 

  9. Guo ZX, Wong WK, Leung SYS, Fan JT, Chan SF (2008) A genetic-algorithm-based optimization model for solving the flexible assembly line balancing problem with work sharing and workstation revisiting. IEEE Trans Syst, Man, Cybern, Part C: Appl Rev 38(2):218–228

    Article  Google Scholar 

  10. Jeong H, Chang TW (2008) A study on business process patterns for integrated supply chain planning: based on analysis of RFQ types. J Inf Technol Appl Manag 15(1):83–97

    Google Scholar 

  11. Kim DS (2006) Process chain: a new paradigm of collaborative commerce and synchronized supply chain. Bus Horiz 49(5):359–367

    Article  Google Scholar 

  12. Lei L, Zhong H, Chaovalitwongse WA (2009) On the integrated production and distribution problem with bidirectional flows. INFORMS J Comput 21:585–598. doi:10.1287/ijoc.1080.0311

    Article  Google Scholar 

  13. Lin HK, Harding JA, Shahbaz M (2004) Manufacturing system engineering ontology for semantic interoperability across extended project teams. Int J Prod Res 42:5099–5118

    Article  Google Scholar 

  14. Nagar L, Fain K (2008) Supply chain planning using multi-stage stochastic programming. Supply Chain Manag: An Int J 13(3):251–256

    Article  Google Scholar 

  15. Ouhimmou M, D'Amours S, Beauregard R, Ait-Kadi D, Chauhan SS (2008) Furniture supply chain tactical planning optimization using a time decomposition approach. Eur J Oper Res 189:952–970

    Article  MATH  Google Scholar 

  16. Park BJ, Choi HR, Kang MH (2007) A multi-agent system based on genetic algorithm for integration planning in a supply chain management. J Korea Intell Inf Syst Soc 13(3):47–61

    Google Scholar 

  17. Selim H, Araz C, Ozkarahan I (2008) Collaborative production-distribution planning in supply chain: a fuzzy goal programming approach. Transp Res Part E 44:396–419

    Article  Google Scholar 

  18. Sywerda G (1989) Uniform crossover in genetic algorithms. Proceedings of the third International Conference on Genetic Algorithms, pp 2–9

  19. Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Set Syst 159:193–214

    Article  MathSciNet  MATH  Google Scholar 

  20. Yao Y, Evers PT, Dresner ME (2007) Supply chain integration in vendor-managed inventory. Decis Support Syst 43(2):663–674

    Article  Google Scholar 

  21. Ye Y, Yang D, Jiang Z, Tong L (2008) Ontology-based semantic models for supply chain management. Int J Adv Manuf Technol 37:1250–1260. doi:10.1007/s00170-007-1052-6

    Article  Google Scholar 

  22. Yildiz AR (2009) A new design optimization framework based on immune algorithm and Taguchi method. Comput Ind 60(8):613–620

    Article  Google Scholar 

  23. Yildiz AR, Öztürk N, Kaya N, Öztürk F (2003) Integrated optimal topology design and shape optimization using neural networks. Struct Multidiscip Optim 25(4):251–260

    Article  Google Scholar 

  24. Yildiz AR, Saitou K (2011) Topology synthesis of multicomponent structural assemblies in continuum domains. ASME J Mech Des 133(1). doi:10.1115/1.4003038

Download references

Author information

Authors and Affiliations

  1. Automation and System Research Institute, Seoul National University, Seoul, Republic of Korea

    Jinwu Seo, Dongmyung Lee & Jinwoo Park

  2. Department of IT Business Engineering, Daejeon University, Daejeon, Republic of Korea

    Hanil Jeong

  3. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Seokcheon Lee

Authors
  1. Jinwu Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanil Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seokcheon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dongmyung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinwoo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hanil Jeong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Seo, J., Jeong, H., Lee, S. et al. A solution procedure for integrated supply chain planning problem in open business environment using genetic algorithm. Int J Adv Manuf Technol 62, 1115–1133 (2012). https://doi.org/10.1007/s00170-011-3863-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3863-8

Keywords

Search

Navigation