Skip to main content

Advertisement

SpringerLink
Log in

Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This study focuses on solving the multi-objective master planning problem for supply chains by considering product structures with multiple final products using substitutions, common components, and recycled components. This study considers five objectives in the planning process: (1) minimizing the delay cost, (2) minimizing the substitution priority, (3) minimizing the recycling penalty, (4) minimizing the substitution cost, and (5) minimizing the cost of production, processing, inventory holding and transportation. This study proposes a heuristic algorithm, called the GA-based Master Planning Algorithm (GAMPA), to solve the supply-chain master planning problem efficiently and effectively. GAMPA first transforms the closed-loop supply chain into an open-loop supply chain that plans and searches the sub-networks for each final product. GAMPA then uses a genetic algorithm to sort and sequence the demands. GAMPA selects the chromosome that generates the best planning result according to the priority of the objectives. GAMPA plans each demand sequentially according to the selected chromosome and a randomly-selected production tree. GAMPA tries different production trees for each demand and selects the best planning result at the end. To show the effectiveness and efficiency of GAMPA, a prototype was constructed and tested using complexity analysis and computational analysis to demonstrate the power of GAMPA.

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

  • Ashley S. (1993) Designing for the environment. Mechanical Engineering 115(3): 52–55

    Google Scholar 

  • Awadh B., Sepehri N., Hwaaleshka O. (1995) A computer-aided process planning model based on genetic algorithms. Computers & Operations Research 22(8): 841–856

    Article  Google Scholar 

  • Balakrishnan A., Genunes J. (2000) Requirements planning with substitutions: Exploiting bill-of-materials flexibility in production planning. Manufacturing & Service Operations Management 2(2): 166–185

    Article  Google Scholar 

  • Beamon B. M. (1998) Supply chain design and analysis: Models and methods. International journal of Production Economics 55(3): 281–294

    Article  Google Scholar 

  • Brailsford S. C., Potts C. N., Smith B. M. (1999) Constraint satisfaction problems: Algorithms and applications. European Journal of Operational Research 119: 557–581

    Article  Google Scholar 

  • Chern C. C., Hsieh J. S. (2007) A heuristic algorithm for master planning that satisfies multiple objectives. Computers and Operatons Research 34(11): 3491–3513

    Article  Google Scholar 

  • Chern, C. C., & Huang K. Y. (2007). A heuristic master planning algorithm for green supply chain management. In Proceedings of the 38th annual meeting of decision science institute (pp. 361-366), San Francisco.

  • Chern, C. C., & Yang, I. C. (2006). A heuristic master planning algorithm for supply chains that considers substitutions and commonalities. In Proceedings of the 37th annual meeting of decision science institute (pp. 25421–25426), San Antonio, Texas, USA.

  • Demirtas E. A., Ustun O. (2009) Analytic network process and multi-period goal programming integration in purchasing decisions. Computer & Industrial Engineering 56: 677–690

    Article  Google Scholar 

  • Erenguç S. S., Simpson N. C., Vakharia A. J. (1999) Integrated production/distribution planning in supply chains: An invited review. European Journal of Operational Research 115(2): 219–236

    Article  Google Scholar 

  • Evans G. (1984) An overview of techniques for solving multi-objective mathematical programs. Management Science 30(11): 1268–1282

    Article  Google Scholar 

  • Fleischmann M., Krikke H. R., Dekker R. (2002) Controlling inventory with stochastic item returns: A basic model. European Journal of Operational Research 138: 63–75

    Article  Google Scholar 

  • Fleischmann M., Krikke H. R., Dekker R., Flapper S. D. P. (2000) A characterization of logistics network for product recovery. Omega 28: 653–666

    Article  Google Scholar 

  • Genues J. (2003) Solving large-scale requirements planning problems with component substitution options. Computer & Industrial Engineering 44: 475–491

    Article  Google Scholar 

  • Jayaraman V., Pirkul H. (2001) Planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operational Research 133: 394–408

    Article  Google Scholar 

  • Kelbel J., Hanzálek Z. (2011) Solving production scheduling with earliness/tardiness penalties by constraint programming. Journal of Intelligent Manufacturing, 22(4): 553–562

    Google Scholar 

  • Lakhal S., Martel A., Kettani O., Oral M. (2001) On the optimization of supply chain networking decisions. European Journal of Operational Research 129: 259–270

    Article  Google Scholar 

  • Lee Y. H., Jeong C. S., Moon C. (2002) Advanced planning and scheduling with outsourcing in manufacturing supply chain. Computer & Industrial Engineering 43: 351–374

    Article  Google Scholar 

  • Lee Y. H., Kim S. H. (2002) Production—distribution planning in supply chain considering capacity constraints. Computers & Industrial Engineering 43: 169–190

    Article  Google Scholar 

  • Lyon P., Milne R. J., Orzell R., Rice R. (2001) Matching assets with demand in supply-chain management at IBM microelectronics. Interfaces, 31(1): 108–124

    Article  Google Scholar 

  • Mabini M. C., Gelders L. F. (1991) Repairable item inventory system: A literature review. Belgian Journal of Operations Research, Statistics and Computer Science 30(4): 57–69

    Google Scholar 

  • Moon C., Kim J., Hur S. (2002) Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain. Computers & Industrial Engineering 43: 331–349

    Article  Google Scholar 

  • Moon C., Seo Y., Yun Y., Gen M. (2006) Adaptive genetic algorithm for advanced planning in manufacturing supply chain. Journal of Intelligent Manufacturing 17(4): 509–522

    Article  Google Scholar 

  • Okamoto A., Gen M., Sugawara M. (2006) Integrated data structure and scheduling approach for manufacturing and transportation using hybrid genetic algorithm. Journal of Intelligent Manufacturing 17(4): 411–421

    Article  Google Scholar 

  • Pirkul H., Jayaraman V. (1998) A multi-commodity, multi-plant, capacitated facility location problem: Formulation and efficient heuristic solution. Computers & Operations Research 25(10): 869–878

    Article  Google Scholar 

  • Richter K. (1996) The EOQ repair and waste disposal model with variable setup numbers. European Journal of Operational Research 95(4): 313–324

    Article  Google Scholar 

  • Richter K. (1996) The extended EOQ repair and waste disposal model. International Journal of Production Economics 45(1–3): 443–448

    Article  Google Scholar 

  • Sawik T. (2007) Multi-objective master production scheduling in make-to-order manufacturing. International Journal of Production Research 45(12): 2629–2653

    Article  Google Scholar 

  • Schultmann F., Engels B., Rentz O. (2003) Close-loop supply chains for spent batteries. Interface 33(6): 57–71

    Article  Google Scholar 

  • Sheu J. B., Chou Y. H., Hu C. C. (2005) An integrated logistics operational model for green-supply chain management. Transportation Research Part E 41: 287–313

    Article  Google Scholar 

  • Stock, J. R. (1992). Reverse logistics. Oak Brook, Illinois: Council of Logistics Management.

  • Vergara F. E., Khouja M., Michalewicz Z. (2002) An evolutionary algorithm for optimizing material flow in supply chains. Computers & Industrial Engineering 42: 407–421

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, National Taiwan University, 50, Lane 144, Sec. 4, Keelung Road, Taipei, 10625, Taiwan

    Ching-Chin Chern & Seak-Tou Lei

  2. Institute of Industrial Engineering, National Taiwan University, No. 1, Sec 4, Roosevelt Road, Taipei, 106, Taiwan

    Kwei-Long Huang

Authors
  1. Ching-Chin Chern
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seak-Tou Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kwei-Long Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ching-Chin Chern.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chern, CC., Lei, ST. & Huang, KL. Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network. J Intell Manuf 25, 1–25 (2014). https://doi.org/10.1007/s10845-012-0667-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0667-0

Keywords

Search

Navigation