Skip to main content

Advertisement

SpringerLink
Log in

Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts

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

Abstract

Today’s global environment is highly competitive. The competition between two firms means the competition in their supply chain. The prime objective of decision makers are to optimize the supply chain. In every stage of supply chain, the cost and delivery time are involved. These criteria are uncertain in nature. We express these criteria in either the triangular or trapezoidal fuzzy numbers. While optimizing the time and cost simultaneously, the problem becomes bi-objective. Such problems can be solved by using Multiple Objective Genetic Algorithm (MOGA). We get the different Pareto front at different α-cuts which is called as the Pareto decision space. According to the uncertainty in the market, decision maker has a choice to take decision among the set of alternatives. Here, the proposed solution methodology is fusion of fuzzy set theory and MOGA. Fuzzy set theory is used for uncertainty in parameters and MOGA is used for obtaining non-dominated solutions. The numerical example is given to illustrate the proposed methodology and industrial case study is solved by using proposed methodology.

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. Amiri A (2006) Designing a distribution network in a supply chain system: formulation and efficient solution procedure. Eur J Oper Res 171(2):567–576

    Article  MATH  Google Scholar 

  2. Back T, Fogel D, Michalewicz Z (1997) Handbook of evolutionary computation. Bristol: Institute of Physics publishing and New York Oxford University press

  3. Beamon B (1998) Supply chain design and analysis: models and methods. Int J Prod Econ 55(3):281–294

    Article  Google Scholar 

  4. Bhattacharya R, Bandyopadhyay S (2010) Solving conflicting bi-objective facility location problem by NSGA-II evolutionary algorithm. Int J Adv Manuf Technol 51:397–414

    Article  Google Scholar 

  5. Bandyopadhyay S, Bhattacharya R (2013) Applying modified NSGA-II for bi-objective supply chain problem. J Intell Manuf 24:707–716

    Article  Google Scholar 

  6. Chakraborty M, Ray A (2009) Parametric approach and genetic algorithm for multi-objective linear programming with imprecise parameters. OPSEARCH 47:73–92

    Article  MathSciNet  MATH  Google Scholar 

  7. Chen C, Lee W (2004) Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Comp Chem Eng 28:1131–1144

    Article  Google Scholar 

  8. Deb K (2000) An efficient constraint handling method for genetic algorithms. Computer Methods Appl Mech Eng 186:311–338

    Article  MATH  Google Scholar 

  9. Deb K (2005) Multi objective optimization using evolutionary algorithms. Wiley

  10. Diabat A, Deskoores R (2016) A hybrid genetic algorithm based heuristic for an integrated supply chain problem. J Manuf Syst 38:172–180

    Article  Google Scholar 

  11. Dutta P, Chakraborty D, Roy A (2005) A single-period inventory model with fuzzy random variable demand. Math Comput Model 41:915–912

    Article  MathSciNet  MATH  Google Scholar 

  12. Fonseca C, Fleming P (1993) Genetic algorithms for multi-objective optimization: formulations, discussion and generalization Proceedings of the Fifth International Conference: Morgan Kauffmann

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

    Google Scholar 

  14. Goldberg D, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. Proceedings of Second International Conference on Genetic Algorithms at Lawrence Erlbaum:41–49

  15. Goldberg D (1989) Genetic algorithms for search optimization, and machine learning. Addison Wesley, Reading, MA

    MATH  Google Scholar 

  16. Holland J (1975) Adaptation in natural and artificial systems. MIT press, Arbor, MI

    Google Scholar 

  17. Jayaraman V, Pirkul H (2001) Planning and coordination of production and distribution facilities for multiple commodities. Eur J Oper Res 133:394–408

    Article  MATH  Google Scholar 

  18. Jayaraman V, Ross A (2003) A simulated annealing methodology to distribution network design and management. Eur J Oper Res 144:629–645

    Article  MathSciNet  MATH  Google Scholar 

  19. Jiang D, Haitao L, Yang T, De Li (2016) Genetic algorithm for inventory positioning problem with general acyclic supply chain networks. Eur J Indust Eng 10(3):367–384

    Article  Google Scholar 

  20. Latpate R, Bajaj V (2011a) Fuzzy programming for multi-objective transportation and inventory management problem with retailer storage. Int J Agric Stat Sci 7(1):377–326

    MATH  Google Scholar 

  21. Latpate R, Bajaj V (2011b) Fuzzy multi-objective, multi-product, production distribution problem with manufacturer storage. Proc Int Congress PQROM 2011:340–355

    MATH  Google Scholar 

  22. Latpate R (2014) Fuzzy modified TOPSIS for supplier selection in supply chain management. Int J Innov Res Comput Sci Technol 3(4):22–28

    Google Scholar 

  23. Liang TF (2011) Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains. Inf Sci 181:842–854

    Article  MATH  Google Scholar 

  24. Liang TF (2008) Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Comput Indust Eng 55:676–694

    Article  Google Scholar 

  25. Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32

    Article  Google Scholar 

  26. Mitchelle M (1996) Introduction to genetic algorithms. MIT press, Ann Arbor, MI

    Google Scholar 

  27. Moncayo-Martinez LA, Ramirez-Lopez A, Recio G (2016) Managing inventory levels and time to market in assembly supply chains by swarm intelligence algorithms. Int J Manuf Technol 82:419–433

    Article  Google Scholar 

  28. Moncayo-Martinez LA, Zhang DZ (2011) Multi-objective ant colony optimisation: a meta-heuristic approach to supply chain design. Int J Prod Econ 131:407–420

    Article  Google Scholar 

  29. Moore G, Revelle C (1982) The hierarchical service location problem. Manag Sci 28(7):775–780

    Article  MATH  Google Scholar 

  30. Murata T, Ishibuchi H (1995) MOGA: Multi-objective genetic algorithms. In: Proceedings of the second IEEE International Conference on Evolutionary Computation, pp 289–294

  31. Osyczka A, Kundu S (1995) A new method to solve generalised multicriteria optimization problems using the simple genetic algorithm. Struct Optim 10:94–99

    Article  Google Scholar 

  32. Razaei J, Davoodi M, Tavasszy L, Davarynejad M (2016) A multi-objective model for lot sizing with supplier selection for an assembly system. Int J Logist Res Appl 19(2):125–142

    Article  Google Scholar 

  33. Sabri E, Beamon B (2000) A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega 28:581–598

    Article  Google Scholar 

  34. Sadeghi J, Sadeghi S, Niaki STA (2014) A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA--II with tuned parameters. Comput Oper Res 41:53–64

    Article  MathSciNet  MATH  Google Scholar 

  35. Shen Z (2007) Integrated supply chain design models: a survey and future research directions. J Indust Manag Optim 3(1):1–19

    Article  MathSciNet  MATH  Google Scholar 

  36. Syam S (2002) Model and methodologies for the location problem with logistical components. Comp Op Res 29:1173–1193

    Article  MathSciNet  MATH  Google Scholar 

  37. Syarif A, Yun Y, Gen M (2002) Study on multi-stage logistics chain network: a spanning tree-based genetic algorithm approach. Comput Indust Eng 43:299–314

    Article  Google Scholar 

  38. Tan KC, Lee TH, Khor EF (2002) Evolutionary algorithms for multi-objective optimization: performance assessments and comparison. Artif Intell Rev 17:253–290

    Article  MATH  Google Scholar 

  39. Truong TH, Azadivar F (2005) Optimal design methodologies for configuration of supply chains. Int J Prod Res 43(11):2217–2236

    Article  Google Scholar 

  40. Tsai J, Liu T, Chou J (2004) Hybrid Taguchi Genetic algorithm for global numerical optimization. IEEE Trans Evol Comput 8(4):365–377

    Article  Google Scholar 

  41. Vidal C, Goetschalckx M (1997) Strategic production-distribution models: a critical revies with emphasis on global supply chain models. Eur J Oper Res 98(1):1–18

    Article  MATH  Google Scholar 

  42. Vose M (1999) Simple genetic algorithms: foundation and theory. MIT press, Ann Arbor, MI

  43. Weber A (1909) Uber den standort der industries, translated as Alfred Weber’s theory of location of industries. University of Chicago press, Chicago

  44. Yan H, Yu Z, Cheng TCE (2003) A strategic model for supply chain design with logical constraints: formulation and solution. Comput Oper Res 30(14):2135–2155

    Article  MATH  Google Scholar 

  45. Wei-Chang Y, Mei-Chi C (2011) Using multi-objetcive genetic algorithm for partner selection in green supply chain problems. Expert Syst Appl 38:4244–4253

    Article  Google Scholar 

  46. Yuce B, Pham DT, Packianather M, Mastrocinque E (2015) An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous type optimisation problem. Prod Manuf Res 3(1):3–19

    Google Scholar 

  47. Yuce B, Mastrocinque E, Lambiase A, Packianather M, Pham DT (2014) A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy. Swarm Evol Comput 18:71–82

    Article  Google Scholar 

  48. Zadeh L (1965) Fuzzy sets. Inf Control 81:338–352

    Article  MATH  Google Scholar 

  49. Zadeh L (1978) Fuzzy sets as basis for a theory of possibility. Fuzzy Sets Syst 1:3–28

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, Savitribai Phule Pune University, Pune, 411007, India

    Raosaheb V. Latpate & Sandesh S. Kurade

Authors
  1. Raosaheb V. Latpate
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandesh S. Kurade
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raosaheb V. Latpate.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Latpate, R.V., Kurade, S.S. Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts. Int J Adv Manuf Technol 91, 3861–3876 (2017). https://doi.org/10.1007/s00170-016-9966-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9966-5

Keywords

Search

Navigation