Multimedia Tools and Applications

, Volume 74, Issue 10, pp 3329–3343 | Cite as

Developing a job shop scheduling system through integration of graphic user interface and genetic algorithm

  • Jun Woo Kim


Job shop scheduling problem is one of the well known hardest combinatorial optimization problems that has a wide range of industrial application domains. Due to the NP-hardness of job shop scheduling problem, meta heuristic search methods such as genetic algorithm have been widely applied to find the good schedules, however, solving the precedence constrained sequencing problems such as JSP is still challenging for genetic algorithms. Moreover, the genetic algorithms for the precedence constrained sequencing problems have been often problem dependent or constraint specific, and the user experiences are not considered in developing them. To address these issues, this paper aims to develop a graphic user interface based job shop scheduling system that searches the good schedules by using the candidate order based genetic algorithm. The candidate order based genetic algorithm enable our scheduling system to handle a wide range of precedence constrained sequencing problems conveniently, and the users can construct various sequencing problems via simple graphic user interfaces. For illustration, our system is applied to classical JSP and its variant, and the experiment results reveal the promising applicability of the system.


Job shop scheduling problem Precedence constrained sequencing problem Candidate order based genetic algorithm Graphic user interface Scheduling system 



This work was supported by the Dong-A University research fund.


  1. 1.
    Abdelmaguid R (2010) Representations in genetic algorithm for the job shop scheduling problem: a computational study. J Softw Eng Appl 3:1155–1162CrossRefGoogle Scholar
  2. 2.
    Ahmed ZH (2010) Genetic algorithm for the traveling salesman problem using sequential constructive crossover operator. Int J Biom Bioinforma 3:96–105Google Scholar
  3. 3.
    Altiparmak F, Gen M, Lin L, Karaoglan I (2009) A steady state genetic algorithm for multi-product supply chain network design. Comput Ind Eng 56:521–537CrossRefGoogle Scholar
  4. 4.
    Bierwirth C (1995) A generalized permutation approach to job shop scheduling with genetic algorithms. Oper-Res-Spektrum 17:87–92CrossRefMATHGoogle Scholar
  5. 5.
    Brucker P, Schile R (1990) Job-shop scheduling with multi-purpose machines. Computing 45:369–375CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Cheng R, Gen M, Tsujimura Y (1996) A tutorial survey of job-shop scheduling problems using genetic algorithms – I. Representation. Comput Ind Eng 4:983–997CrossRefGoogle Scholar
  7. 7.
    Fang HL, Ross P, Corne D (1993) A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems, In: The 5th International Conference on Genetic Algorithms, 375-382Google Scholar
  8. 8.
    Holland JH (1975) Adaptation in Natural and Artificial Systems, University of Michigan PressGoogle Scholar
  9. 9.
    Kammer M, Akker MVD, Hoogeveen H (2011) Identifying and exploiting commonalities for the job-shop scheduling problem. Comput Oper Res 38:1556–1561CrossRefMathSciNetGoogle Scholar
  10. 10.
    Kim JW (2012) A simple job shop scheduling game for industrial engineering students. J Futur Game Technol 2:165–171Google Scholar
  11. 11.
    Kim JW (2013) Candidate order based genetic operators for sequencing problems. International Conference on Intelligence Fusion, In, pp 12–13Google Scholar
  12. 12.
    Kowalczyk R (1997) Constrained consistent genetic algorithms. IEEE International Conference on Evloutionary Computation, In, pp 343–348Google Scholar
  13. 13.
    Kozen D (1992) The design and analysis of algorithms, SpringerGoogle Scholar
  14. 14.
    Lee HP, Carter JN (2006) A modified Giffler and Thompson genetic algorithm on the job shop scheduling problem. MATEMATIKA 22:91–107Google Scholar
  15. 15.
    Lenstra JK, Kan AR (1978) Complexity of scheduling under precedence constraints. Oper Res 26:22–35CrossRefMATHGoogle Scholar
  16. 16.
    Muth JF, Thompson GL (1963) Industrial scheduling. Prentice Hall, New JerseyGoogle Scholar
  17. 17.
    Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35:3202–3212CrossRefMATHGoogle Scholar
  18. 18.
    Poon PW, Carter JN (1995) Genetic algorithm crossover operators for ordering applications. Comput Oper Res 22:135–147CrossRefMATHGoogle Scholar
  19. 19.
    Ruiz-Vanoye JA, Diza-Parra O, Zavala-Diaz JC (2011) Compexity indicators applied to the job shop scheduling problem to discriminate the best algorithm. Int J Comb Optim Probl Inform 2:25–31Google Scholar
  20. 20.
    Starkweather T, MacDaniel S, Mathias KE, Whitely LD, Whitley C (1991) A comparison of genetic sequencing operators, In: The 4th International Conference on Genetic Algorithms, 69-76Google Scholar
  21. 21.
    Werner F (2011) Genetic algorithms for shop scheduling problems: a survey. Accessed 24 June 2013
  22. 22.
    Yamada T, Nakano R (1992) A genetic algorithm applicable to large-scale job-shop problems. Parallel Probl Solving from Nat 2:281–290Google Scholar
  23. 23.
    Zhang Z, Gao L, Shi Y (2011) An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst Appl 38:3562–3573Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Industrial and Management Systems EngineeringDong-A UniversityBusanSouth Korea

Personalised recommendations