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A Novel Hybrid Modelling for Aggregate Production Planning in a Reconfigurable Assembly Unit for Optoelectronics

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9490))

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Abstract

In this work it is presented a novel fuzzy multi-objective linear programming (FMOLP) model based on hybrid fuzzy inference systems for solving the general management framework fort the integration of a self-contained robotic reconfigurable assembly unit in a pre-existing shop-floor. It is developed an aggregate production planning in a fuzzy environment where the product price, unit inventory cost, unit assembly cost, resource-product suitability cost, availability of the assembly line, the assembly time and the 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 nominal capacity and inventory towards zero. Pareto solutions optimization is computed with different techniques and results are presented and discussed with interesting practical implications.

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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. Accepted at 1st IEEE international forum on RTSI 2015. In press on IEEE Xplore digital library

    Google Scholar 

  3. Sisca, F.G., Fiasché, M., Ripamonti, G., Taisch, M.: A novel hybrid fuzzy multi-objective linear programming model for APP in a shop-floor employing white’R for HMLV optoelectronics. Accepted at WIRN 2015, Springer Smart Innovation, Systems and Technologies, in Press

    Google Scholar 

  4. Kahraman, C., Kaya, I.: Fuzzy multiple objective linear programming. In: Kahraman, C. (ed.) Fuzzy Multi-Criteria Decision Making. Springer Optimization and Its Applications, vol. 16, pp. 325–337. Springer, US (2008)

    Chapter  Google Scholar 

  5. Zimmerman, H.-J.: Description and optimization of fuzzy systems. Int. J. Gen. Syst. 2, 209–215 (1976)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    MathSciNet  Google Scholar 

  8. 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 

  9. Sakawa, M.: An interactive fuzzy satisficing method for multiobjective linear programming problems. Fuzzy Sets Syst. 28, 129–144 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  10. Zimmermann, H.J.: Fuzzy Set Theory and its Application. Kluwer, Boston (1996)

    Book  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  12. 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 

  13. Wang, R.-C., Liang, T.-F.: Application of fuzzy multi-objective linear programming to aggregate production planning. Comput. Ind. Eng. 46, 17–41 (2006)

    Article  Google Scholar 

  14. 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 

  15. S. Motta, R., Afonso, S.M.B., Lyra, P.R.M.: A modified NBI and NC method for the solution of N-multiobjective optimization problems. Structural and Multidisciplinary Optimization, 8 January 2012. doi:10.1007/s00158-011-0729-5

  16. 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 

  17. 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 

  18. 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 

  19. 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 

  20. 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 

  21. Sindhya, K., Deb, K., Miettinen, K.: A local search based evolutionary multi-objective optimization approach for fast and accurate convergence. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 815–824. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Fiasché, M.: A quantum-inspired evolutionary algorithm for optimization numerical problems. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 686–693. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34487-9_83

    Chapter  Google Scholar 

  23. Fiasché, M., Taisch, M.: On the use of quantum-inspired optimization techniques for training spiking neural networks: a new method proposed. In: Bassis, S., Esposito, A., Morabito, F.C. (eds.) Recent Advances of Neural Networks Models and Applications. SIST, vol. 37, pp. 359–368. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18164-6_35

    Google Scholar 

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Authors and Affiliations

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

    Francesco G. Sisca, Maurizio Fiasché & Marco Taisch

Authors
  1. Francesco G. Sisca
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  2. Maurizio Fiasché
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  3. Marco Taisch
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Corresponding author

Correspondence to Francesco G. Sisca .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Sisca, F.G., Fiasché, M., Taisch, M. (2015). A Novel Hybrid Modelling for Aggregate Production Planning in a Reconfigurable Assembly Unit for Optoelectronics. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_65

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  • DOI: https://doi.org/10.1007/978-3-319-26535-3_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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