Çizelgeleme problemlerinin çözümünde hibrit biyocoğrafya tabanlı optimizasyon algoritmasının kullanımı

Biyocoğrafya Tabanlı Optimizasyon (BTO), habitat türlerinin göçünden esinlenerek oluşturulan evrimsel bir algoritmadır. 2008 yılında Simon tarafından geliştirilen bu yöntem, optimizasyon problemlerinin çözümünde başarılı bir şekilde kullanılmaktadır. Biyocoğrafya tabanlı optimizasyon, esnek ve çok yönlü bir algoritmadır fakat en zor kombinatoryal optimizasyon probleminden biri olan atölye çizelgeleme problemlerini çözmek için kullanıldığında yetersiz kaldığından dolayı Hibrit Biyocoğrafya Tabanlı Optimizasyon (HBTO) geliştirilmiştir. Yapılan araştırmalar sonucunda HBTO yönteminin BTO’ya göre daha etkili ve esnek olduğu keşfedilmiştir. HBTO farklı kombinatoriyel optimizasyon problemlerinde kullanılabilen bir algoritmadır. Bu araştırmada, hibrit biyocoğrafya tabanlı optimizasyon algoritmasının çizelgeleme problemlerinin çözümünde kullanımı incelenmiştir. HBTO’nun çizelgeleme problemlerinin çözümünde kullanımı ile ilgili literatür araştırması yapılmıştır.

___

  • "Survey of biogeography based optimization," IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (2018) 1-8.
  • H. Ma, D. Simon, P. Siarry, Z. Yang and M. Fei, Biogeography-based optimization: a 10-year review, in IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 5 (2017) 391-407.
  • D. Simon, "Biogeography-based optimization," in IEEE Transactions on Evolutionary Computation, 12, 6 (2008) 702-713.
  • A. K. Bansal, R. Kumar and R. A. Gupta, Economic analysis and power management of a small autonomous hybrid power system (SAHPS) using biogeography based optimization (BBO) algorithm, in IEEE Transactions on Smart Grid, 4, 1 (2013) 638-648.
  • X. Li, J. Wang, J. Zhou and M. Yin, A perturb biogeography based optimization with mutation for global numerical optimization, Applied Mathematics and Computation, 218, 2 (2011) 598-609.
  • H. Ma, D. Simon, Blended biogeography-based optimization for constrained optimization, Engineering Applications of Artificial Intelligence, 24, 3 (2011) 517-525.
  • D. Simon, M. Ergezer and D. Du, Population distributions in biogeography-based optimization algorithms with elitism, 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, (2009) 991-996.
  • X Wang, H Duan, “A hybrid biogeography-based optimization algorithm for job shop scheduling problem”, Computers & Industrial Engineering, 73 (2014) 96-114.
  • E.N. Lorenz, Deterministic nonperiodic flow, Journal of Atmospheric Sciences, 20, (1963)130-141.
  • O. Engin, M.K. Yılmaz, M.E. Baysal, A. Sarucan, Solving fuzzy job shop scheduling problems with availability constraints using a scatter search method, Journal of Multiple-Valued Logic & Soft Computing, 21, 3-4 (2013) 317-334.
  • M.Yin, and X. Li, A hybrid bio-geography based optimization for permutation flow shop scheduling, Scientific Research and Essays, 6, 10 (2011) 2078-2100. J. Lin, S. Zhang. An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem, Computers & Industrial Engineering, 97 (2016) 128-136.
  • J. Huang and X. Gu, Distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times using a novel biogeography-based optimization algorithm, Engineering Optimization, 54, 4 (2022) 593-613.
  • Y. Wang, X. Li, A hybrid chaotic biogeography based optimization for the sequence dependent setup times flowshop scheduling problem with weighted tardiness objective, in IEEE Access, 5 (2017) 26046-26062.
  • S. Liu, P. Wang, J. Zhang, An improved biogeography-based optimization algorithm for blocking flow shop scheduling problem. Chinese J. Electron., 27 (2018) 351-358.
  • F. Zhao, S. Qin, Y. Zhang, W. Ma, C. Zhang and H. Song, A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem, Expert Systems with Applications, 126 (2019) 321-339.
  • M. Huang, S. Shi, X. Liang, X. Jiao and Y. Fu, An improved biogeography-based optimization algorithm for flow shop scheduling problem, IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT) (2020) 59-63.
  • S.H.A. Rahmati, M. Zandieh, A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem, The International Journal of Advanced Manufacturing Technology, 58 (2012) 1115-1129.
  • Y. Yang, A modified biogeography-based optimization for the flexible job shop scheduling problem, Mathematical Problems in Engineering, Hindawi, (2015)1-10.
  • Y. An, X. Chen, Y. Li, Y. Han, J. Zhang and H. Shi, An improved non-dominated sorting biogeography-based optimization algorithm for the (hybrid) multi-objective flexible job-shop scheduling problem, Applied Soft Computing, 99 (2021) 106869.
  • Y. An, X. Chen, Y. Li, J. Zhang, J. Jiang, Flexible job-shop scheduling and heterogeneous repairman assignment with maintenance time window and employee timetable constraints. Expert Systems with Applications 186 (2021) 115693.
  • S.S. Kim, J. H. Byeon, H. Yu, H. Liu, Biogeography-based optimization for optimal job scheduling in cloud computing, Applied Mathematics and Computation, 247 (2014) 266-280.
  • H. Piroozfard, K. Y. Wong, A. D. Asl, An improved biogeography-based optimization for achieving optimal job shop scheduling solutions, Procedia Computer Science, 115 (2017) 30-38.
  • M. Harrabi, O.B. Driss, K. Ghedira, A hybrid evolutionary approach to job-shop scheduling with generic time lags. Journal of Scheduling 24 (2021) 329–346.
  • J. Lin, A hybrid biogeography-based optimization for the fuzzy flexible job-shop scheduling problem, Knowledge-Based Systems, 78 (2015) 59-74
  • O. Engin, A. Fığlalı, Akış tipi çizelgeleme problemlerinin genetik algoritma yardımı ile çözümünde uygun çaprazlama operatörünün belirlenmesi, Doğuş Üniversitesi Dergisi, 6 (2002) 27-35.
  • S. Külahlı, O. Engin, İ. Koç, A new hybrid scatter search method for solving the flexible job shop scheduling problems, Celal Bayar University Journal of Science, 17, 4 (2021) 347-359.
  • O. Engin, C. Kahraman, M. K. A Yilmaz, Scatter search method for multiobjective fuzzy permutation flow shop scheduling problem: a real world application. In computational intelligence in flow shop and job shop scheduling, (2009)169-189 Springer, Berlin, Heidelberg.