Optimization of job shop scheduling problems using modified clonal selection algorithm

Creative Commons License

Atay Y., Kodaz H.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.22, no.6, pp.1528-1539, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 6
  • Publication Date: 2014
  • Doi Number: 10.3906/elk-1212-26
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1528-1539
  • Keywords: Artificial immune system, clonal selection algorithm, job shop scheduling, optimization, GENETIC ALGORITHM
  • Gazi University Affiliated: No


Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best makespan values are reached in 7 out of 10 test problems (FT06, LA01, LA02, LA03, LA04, LA05, and ABZ6). Reasonable makespan values are reached for the remaining 3 problems (FT10, LA16, and ABZ5). The obtained results demonstrate that the developed system can be applied successfully to job shop scheduling problems.