Skip to main content
SpringerLink
Log in

A Novel Hybrid Fuzzy Multi-objective Linear Programming Method of Aggregate Production Planning

  • Conference paper
  • First Online:
Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

Included in the following conference series:

Abstract

In this work a novel fuzzy multi-objective linear programming (FMOLP) method based on hybrid fuzzy inference systems is proposed for solving the general framework of integration of self-contained assembly unit in a fuzzy environment where the product price, unit cost of not utilization of resources, work force level, production capacity and market demands are fuzzy in nature. The proposed model attempts to minimize total production costs, maximizing the shop floor resources utilization and the profits, considering inventory level, and capacity. Pareto solutions optimization is computed with different techniques and results are presented and discussed with interesting practical implications.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mahoney, R.M.: High-Mix Low-Volume. Hewlett Packard (1997)

    Google Scholar 

  2. Fiasché, M., Ripamonti, G., Sisca, F.G., Taisch, M., Valente, A.: Management integration framework in a shop-floor employing self-contained assembly unit for optoelectronic products. In: Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2015 IEEE 1st International Forum, pp. 569–578. Turin (2015). doi: 10.1109/RTSI.2015.7325159

  3. Hannan, E.L.: Linear programming with multiple fuzzy goals. Fuzzy Sets Syst. 6, 235–248 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manage. Sci. 17, 141–164 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  5. Zimmerman, H.-J.: Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst. 2, 209–215 (1978)

    MathSciNet  Google Scholar 

  6. Leberling, H.: On finding compromise solutions in multicriteria problems using the fuzzy min-operator. Fuzzy Sets Syst. 6, 105–118 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  7. Sakawa, M.: An interactive fuzzy satisfiying method for multiobjective linear programming problems. Fuzzy Sets Syst. 28, 114–129 (1988)

    Article  MathSciNet  Google Scholar 

  8. Zimmermann, H.J.: Fuzzy Set Theory and ITS application. Kluwer, Boston (1996)

    Book  MATH  Google Scholar 

  9. Zimmermann, H.J., Zysno, P.: Latent connectives in human decision making. Fuzzy Sets Syst. 4, 37–51 (1980)

    Article  MATH  Google Scholar 

  10. Zimmermann, H.-J.: Fuzzy linear programming. In: Gal, T., Greenberg, H.J. (eds.) Advances in Sensitivity Analysis and Parametric Programming, pp. 15.1–15.40. Kluwer, Boston (1997)

    Google Scholar 

  11. Wang, R.-C., Liang, T.-F.: Application of fuzzy multi-objective linear programing to aggregate production planning. Comput. Ind. Eng. 46, 17–41 (2004)

    Article  Google Scholar 

  12. Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631 (1998). doi:10.1137/S1052623496307510

    Article  MathSciNet  MATH  Google Scholar 

  13. Motta, R.S., Afonso, S.M.B., Lyra, P.R.M.: A modified NBI and NC method for the solution of N-multiobjective optimization problems. Struct. Multi. Optim. (2012). doi:10.1007/s00158-011-0729-5

    Google Scholar 

  14. Messac, A., Ismail-Yahaya, A., Mattson, C.A.: The normalized normal constraint method for generating the Pareto frontier. Struct. Multi. Optim. 25(2), 86–98 (2003). doi:10.1007/s00158-002-0276-1

    Article  MathSciNet  MATH  Google Scholar 

  15. Messac, A., Mattson, C.A.: Normal constraint method with guarantee of even representation of complete Pareto frontier. AIAA J 42(10), 2101–2111 (2004). doi:10.2514/1.8977

    Article  Google Scholar 

  16. Mueller-Gritschneder, D., Graeb, H., Schlichtmann, U.: A successive approach to compute the bounded pareto front of practical multiobjective optimization problems. SIAM J. Optim. 20(2), 915–934 (2009). doi:10.1137/080729013

    Article  MathSciNet  MATH  Google Scholar 

  17. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182 (2002). doi:10.1109/4235.996017

    Article  Google Scholar 

  18. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the performance of the strength pareto evolutionary algorithm, Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich (2001)

    Google Scholar 

  19. Sindhya, K., Deb, K., Miettinen, K.: A local search based evolutionary multi-objective optimization approach for fast and accurate convergence. In: Parallel Problem Solving from Nature—PPSN X. Lecture Notes in Computer Science, vol. 5199. p. 815 (2008). doi:10.1007/978-3-540-87700-4_81

    Google Scholar 

  20. Fiasché, M.: A quantum-inspired evolutionary algorithm for optimization numerical problems. In: ICONIP 2012, Part III, LNCS, vol. 7665, pp. 686–693. (Part3) (2012). doi:10.1007/978-3-642-34487-9_83

    Google Scholar 

  21. Fiasché, M., Taisch, M.: On the use of quantum-inspired optimization techniques for training spiking neural networks: a new method proposed. In: Smart Innovation, Systems and Technologies, vol. 37, pp. 359−368 (2015). doi:10.1007/978-3-319-18164-6_35

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milano, Italy

    Maurizio Fiasché, Gaia Ripamonti, Francesco G. Sisca, Marco Taisch & Giacomo Tavola

Authors
  1. Maurizio Fiasché
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaia Ripamonti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco G. Sisca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Taisch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giacomo Tavola
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maurizio Fiasché .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Fiasché, M., Ripamonti, G., Sisca, F.G., Taisch, M., Tavola, G. (2016). A Novel Hybrid Fuzzy Multi-objective Linear Programming Method of Aggregate Production Planning. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33747-0_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33746-3

  • Online ISBN: 978-3-319-33747-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation