Harmony Search Algorithm with Various Evolutionary Elements for Fuzzy Aggregate Production Planning

  • Pasura Aungkulanon
  • Busaba Phruksaphanrat
  • Pongchanun Luangpaiboon
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)


This chapter presents an application of a fuzzy programming approach for multiple objectives to the aggregate production planning (APP). The APP parameter levels have been applied via a case study from the SMEs company in Thailand. The proposed model attempts to minimise total production cost and minimise the subcontracting units. Conventional harmony search algorithm (HSA) with its hybridisations of the novel global best harmony search (NGHSA) and the variable neighbourhood search of the HSA (VHSA) including the hunting search (HuS) element on the pitch adjustment (HuSHSA). Based on the experimental results, it can be concluded that each algorithm is suitable for different types of situations. However, for all situations VHSA and NGHSA can obtain good solutions. Furthermore, the proposed VHSA is more effective than other approaches in terms of superiority of solution and required CPU time.


Fuzzy aggregate production planning Particle swarm optimisation Hunting search algorithm and variable neighbourhood search algorithm 



This work was supported by the National Research University Project of Thailand Office of Higher Education Commission.


  1. 1.
    Techawiboonwong A (2003) Aggreate production planning and master production scheduling: problems and methods for labor-intensive Thai industries, PhD dissertation, SIIT Thammasart UniversityGoogle Scholar
  2. 2.
    Belmokaddem M, Mekidiche M, Sahed A (2009) Application of a fuzzy goal programming approach with different importance and priorities to aggregate production planning. J Appl Quant Method 4(3):317–331Google Scholar
  3. 3.
    Fahimnia B, Luong L, Marian R (2005) Modeling and optimisation of aggregate production planning – a genetic algorithm approach. Int J Math Comput Sci 1: 1–6Google Scholar
  4. 4.
    Baykoç Ö, Sakalli Ü (2009) An aggregate production planning model for brass casting industry in fuzzy environment. Eng Technol 52:117–121Google Scholar
  5. 5.
    Chen Y (2010) A production planning problem solved by the particle swarm optimisation. Proceeding of the international multiconference of engineers and computer scientist 2010 Vol III, IMECS 2010, Hong Kong, 17–19 March 2010Google Scholar
  6. 6.
    Leung S, Wu Y (2004) A robust optimisation model for stochastic aggregate production planning. Product Plan Control 15(5):502–514CrossRefGoogle Scholar
  7. 7.
    Aliev R, Fazlollahi B, Guirimov B, Aliev R (2007) Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Inform Sci 177: 4241–4255CrossRefMATHGoogle Scholar
  8. 8.
    Aungkulanon P, Phruksaphanrat B, Luangpaiboon P (2011) Various hybridisations of harmony search algorithm for fuzzy programming approach to aggregate production planning, Lecture notes in engineering and computer science. Proceedings of the international multiconference of engineers and computer scientist 2011, vol. I, IMECS 2011, Hong Kong, 16–18 March 2011, pp.73–79Google Scholar
  9. 9.
    Zimmerman HJ (1992) Fuzzy set theory and its application. Kluwer Academic, pp 336–339Google Scholar
  10. 10.
    Zimmerman HJ (1985) Applications of fuzzy sets theory to mathematical programming. Inform Sci 35:29–58CrossRefGoogle Scholar
  11. 11.
    Zimmerman HJ (1976) Description and optimisation of fuzzy systems. Int J Gen Syst 2:209–215CrossRefGoogle Scholar
  12. 12.
    Zimmerman HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–56CrossRefGoogle Scholar
  13. 13.
    Lee KS, Geem ZW (2004) A new meta-heuristic algorithm for continues engineering optimisation: harmony search theory and practice. Comput Math Appl Mech Eng 194:3902–3933CrossRefGoogle Scholar
  14. 14.
    Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math Appl 60: 2087–2098CrossRefMATHGoogle Scholar
  15. 15.
    Mladenovic N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24: 1097–1100CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Zou D, Gao L, Wu J, Li S, Li Y (2010) A novel global harmony search algorithm for reliability problems. Comput Ind Eng 58:307–316CrossRefGoogle Scholar
  17. 17.
    Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimisation problems. Appl Math Comput 188:1567–1579CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Pasura Aungkulanon
    • 1
  • Busaba Phruksaphanrat
    • 1
  • Pongchanun Luangpaiboon
    • 1
  1. 1.The Industrial Statistics and Operational Research Unit (ISO-RU), Department of Industrial Engineering, Faculty of EngineeringThammasat UniversityPathumtaniThailand

Personalised recommendations