Skip to main content
SpringerLink
Log in

Integrating Advanced Harmony Search with Fuzzy Logic for Solving Buffer Allocation Problems

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This paper introduces a new fuzzy advanced harmony search algorithm for solving single-objective buffer allocation problems (BAPs). The proposed algorithm represents the first attempt at solving BAPs using a fuzzy logic system, by tuning the advanced harmony search control parameters. The main steps of the proposed algorithm included parameter initialisation, harmony memory initialisation and evaluation, improvisation, harmony memory update, AHS parameter update, and termination criterion check. The aim of this approach is to achieve a better convergence rate and avoid the stacking of local optima. The performance of the proposed algorithm was compared with other methods used in solving BAPs. The proposed approach has shown a higher capability in finding optimal solutions compared to previous methods used for two benchmark problems. Improvement of up to 94.75% in the overall throughput is reported for the 3-stage problem, while for the 12-stage problem, a slight improvement (up to 7.58%) is also reported in the overall throughput. The results achieved indicate that the proposed algorithm is an efficient and promising tool in solving BAPs.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Demir, L.; Tunali, S.; Eliiyi, D.T.: The state of the art on buffer allocation problem: a comprehensive survey. J. Intell. Manuf. 25, 371–392 (2014)

    Article  Google Scholar 

  2. Martinez Delgado, V.: The buffer allocation problem for general finite buffer queuing networks: a survey. Doctoral dissertation, UPV Universitat Politècnica de València, Spain (2015)

  3. Kłos, S.; Patalas-Maliszewska, J.: An approach to buffer allocation, in parallel-serial manufacturing systems using the simulation method. In: World Conference on Information Systems and Technologies, pp. 225–235. Springer (2018)

  4. MacGregor Smith, J.; Cruz, F.: The buffer allocation problem for general finite buffer queueing networks. IIE Trans. 37, 343–365 (2005)

    Article  Google Scholar 

  5. Diamantidis, A.; Papadopoulos, C.: A dynamic programming algorithm for the buffer allocation problem in homogeneous asymptotically reliable serial production lines. Math. Probl. Eng. 2004, 209–223 (2004)

    Article  MathSciNet  Google Scholar 

  6. Manne, A.S.: Linear programming and sequential decisions. Manag. Sci. 6, 259–267 (1960)

    Article  MathSciNet  Google Scholar 

  7. Tanaka, S.; Fujikuma, S.: A dynamic-programming-based exact algorithm for general single-machine scheduling with machine idle time. J. Sched. 15, 347–361 (2012)

    Article  MathSciNet  Google Scholar 

  8. Spinellis, D.D.; Papadopoulos, C.T.: A simulated annealing approach for buffer allocation in reliable production lines. Ann. Oper. Res. 93, 373–384 (2000)

    Article  MathSciNet  Google Scholar 

  9. Lutz, C.M.; Davis, K.R.; Sun, M.: Determining buffer location and size in production lines using tabu search. Eur. J. Oper. Res. 106, 301–316 (1998)

    Article  Google Scholar 

  10. Narasimhamu, K.; Reddy, V.; Rao, C.: Optimization of buffer allocation in manufacturing system using particle swarm optimization. Int. Rev. Model. Simul. 8, 212–222 (2015)

    Google Scholar 

  11. Dolgui, A.; Eremeev, A.; Kolokolov, A.; Sigaev, V.: A genetic algorithm for the allocation of buffer storage capacities in a production line with unreliable machines. J. Math. Model. Algorithms 1, 89–104 (2002)

    Article  MathSciNet  Google Scholar 

  12. Vitanov, I.V.; Vitanov, V.I.; Harrison, D.K.: Buffer capacity allocation using ant colony optimisation algorithm. In: Winter Simulation Conference, Winter Simulation Conference, pp. 3158–3168 (2009)

  13. Horng, S.-C.; Lin, S.-S.: Merging artificial immune system and ordinal optimization for solving the optimal buffer resource allocation of production line. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 6–11. IEEE (2017)

  14. Teodorovic, D.; Dell’Orco, M.: Bee colony optimization—a cooperative learning approach to complex transportation problems. Adv. OR AI Methods Transp. 51, 60 (2005)

    Google Scholar 

  15. Horng, S.-C.; Lin, S.-S.: Embedding advanced harmony search in ordinal optimization to maximize throughput rate of flow line. Arab. J. Sci. Eng. 43, 1015–1031 (2018)

    Article  Google Scholar 

  16. Wang, X.; Gao, X.-Z.; Zenger, K.: The overview of harmony search. In: Kacprzyk, J. (ed.) An Introduction to Harmony Search Optimization Method, pp. 5–11. Springer, Cham (2015)

    Google Scholar 

  17. Moh’d Alia, O.; Mandava, R.: The variants of the harmony search algorithm: an overview. Artif. Intell. Rev. 36, 49–68 (2011)

    Article  Google Scholar 

  18. Abdel-Raouf, O.; Metwally, M.A.-B.: A survey of harmony search algorithm. Int. J. Comput. Appl. 70, 17–26 (2013)

    Google Scholar 

  19. Yun, H.-Y.; Jeong, S.-J.; Kim, K.-S.: Advanced harmony search with ant colony optimization for solving the traveling salesman problem. J. Appl. Math. 2013, 1–8 (2013)

    Article  MathSciNet  Google Scholar 

  20. Peraza, C.; Valdez, F.; Garcia, M.; Melin, P.; Castillo, O.: A new fuzzy harmony search algorithm using fuzzy logic for dynamic parameter adaptation. Algorithms 9, 69 (2016)

    Article  MathSciNet  Google Scholar 

  21. Zhang, M.; Matta, A.; Pedrielli, G.: Discrete event optimization: workstation and buffer allocation problem in manufacturing flow lines. In: Proceedings of the 2016 Winter Simulation Conference, pp. 2879–2890. IEEE Press (2016)

  22. Shi, C.; Gershwin, S.B.: A segmentation approach for solving buffer allocation problems in large production systems. Int. J. Prod. Res. 54, 6121–6141 (2016)

    Article  Google Scholar 

  23. Demir, L.; Tunalı, S.; Eliiyi, D.T.; Løkketangen, A.: Two approaches for solving the buffer allocation problem in unreliable production lines. Comput. Oper. Res. 40, 2556–2563 (2013)

    Article  Google Scholar 

  24. Demir, L.; Tunali, S.; Løkketangen, A.: A tabu search approach for buffer allocation in production lines with unreliable machines. Eng. Optim. 43, 213–231 (2011)

    Article  Google Scholar 

  25. Li, L.; Qian, Y.; Yang, Y.M.; Du, K.: A fast algorithm for buffer allocation problem. Int. J. Prod. Res. 54, 3243–3255 (2016)

    Article  Google Scholar 

  26. Wang, C.-M.; Huang, Y.-F.: Self-adaptive harmony search algorithm for optimization. Expert Syst. Appl. 37, 2826–2837 (2010)

    Article  Google Scholar 

  27. Mashinchi, M.H.; Orgun, M.A.; Mashinchi, M.; Pedrycz, W.: A tabu–harmony search-based approach to fuzzy linear regression. IEEE Trans. Fuzzy Syst. 19, 432–448 (2011)

    Article  Google Scholar 

  28. Jaberipour, M.; Khorram, E.: Two improved harmony search algorithms for solving engineering optimization problems. Commun. Nonlinear Sci. Numer. Simul. 15, 3316–3331 (2010)

    Article  Google Scholar 

  29. Vasebi, A.; Fesanghary, M.; Bathaee, S.: Combined heat and power economic dispatch by harmony search algorithm. Int. J. Electr. Power Energy Syst. 29, 713–719 (2007)

    Article  Google Scholar 

  30. Wang, G.; Guo, L.: A novel hybrid bat algorithm with harmony search for global numerical optimization. J. Appl. Math. 2013, 1–21 (2013)

    MathSciNet  MATH  Google Scholar 

  31. Yıldız, A.R.: Hybrid Taguchi-harmony search algorithm for solving engineering optimization problems. Int. J. Ind. Eng. 15, 286–293 (2008)

    Google Scholar 

  32. Zhao, S.-Z.; Suganthan, P.N.; Pan, Q.-K.; Tasgetiren, M.F.: Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst. Appl. 38, 3735–3742 (2011)

    Article  Google Scholar 

  33. Schwarz, J.A.: Analysis of buffer allocations in time-dependent and stochastic flow lines. Doctoral dissertation, Universität Mannheim, Germany (2015)

  34. Cruz, F.R.B.d.; Kendall, G.; While, L.; Duarte, A.R.; Brito, N.L.C.: Throughput maximization of queueing networks with simultaneous minimization of service rates and buffers. Math. Probl. Eng. 2012, 1–19 (2012)

    Article  MathSciNet  Google Scholar 

  35. Weiss, S.; Schwarz, J.A.; Stolletz, R.: The buffer allocation problem in production lines: formulations, solution methods, and instances. IISE Trans. 51, 456–485 (2019)

    Article  Google Scholar 

  36. Ghasemzadeh, H.; Jafari, R.: A greedy buffer allocation algorithm for power-aware communication in body sensor networks. In: 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES + ISSS), pp. 195–204. IEEE (2010)

  37. Ahn, D.S.; Lee, M.J.: Optimal buffer allocation in ATM switches by effective cell loss. J. High Speed Netw. 6, 247–262 (1997)

    Google Scholar 

  38. Stratton, R.; Knight, A.: Utilising buffer management to manage patient flow. In: 16th International Annual EurOMA Conference (2009)

  39. Yuan, Q.: Modeling and optimization of resource allocation in supply chain management problems. Doctoral dissertation, University of Tennessee, USA (2013)

  40. Muñoz, M.A.; Sun, Y.; Kirley, M.; Halgamuge, S.K.: Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Inf. Sci. 317, 224–245 (2015)

    Article  Google Scholar 

  41. Pourjavad, E.; Shahin, A.: The application of Mamdani fuzzy inference system in evaluating green supply chain management performance. Int. J. Fuzzy Syst. 20, 901–912 (2018)

    Article  Google Scholar 

  42. Pourjavad, E.; Mayorga, R.V.: A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system. J. Intell. Manuf. 30, 1085–1097 (2019)

    Article  Google Scholar 

  43. Zadeh, L.A.: From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions. IEEE Trans. Circ. Syst. I Fund. Theory Appl. 46, 105–119 (1999)

    Article  MathSciNet  Google Scholar 

  44. Pichitlamken, J.; Nelson, B.L.; Hong, L.J.: A sequential procedure for neighborhood selection-of-the-best in optimization via simulation. Eur. J. Oper. Res. 173, 283–298 (2006)

    Article  Google Scholar 

  45. Kim, S.; Henderson, S.G.: The mathematics of continuous-variable simulation optimization. In: 2008 Winter Simulation Conference, pp. 122–132. IEEE (2008)

Download references

Author information

Authors and Affiliations

  1. Yarmouk University, Irbid, 21163, Jordan

    Mahmoud Z. Mistarihi, Ghazi M. Magableh & Haythem Bany Salameh

  2. Jordan University of Science and Technology, Irbid, 22110, Jordan

    Rasha A. Okour

  3. Al Ain University, Al Ain, UAE

    Haythem Bany Salameh

Authors
  1. Mahmoud Z. Mistarihi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rasha A. Okour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghazi M. Magableh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haythem Bany Salameh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud Z. Mistarihi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mistarihi, M.Z., Okour, R.A., Magableh, G.M. et al. Integrating Advanced Harmony Search with Fuzzy Logic for Solving Buffer Allocation Problems. Arab J Sci Eng 45, 3233–3244 (2020). https://doi.org/10.1007/s13369-020-04348-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-04348-2

Keywords

Search

Navigation