A Variable Neighbourhood Search Algorithm for Job Shop Scheduling Problems

  • Mehmet Sevkli
  • M. Emin Aydin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3906)


Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for solving combinatorial optimization problems in which a systematic change of neighbourhood within a local search is carried out. In this paper, a variable neighbourhood search algorithm is proposed for Job Shop Scheduling (JSS) problem with makespan criterion. The results gained by VNS algorithm are presented and compared with the best known results in literature. It is concluded that the VNS implementation is better than many recently published works with respect to the quality of the solution.


Local Search Completion Time Neighbourhood Structure Greedy Randomize Adaptive Search Procedure Hybrid Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adams, J., Balas, E., Zawack, D.: The Shifting Bottleneck Procedure for Job Shop Scheduling. Management Science 34, 391–401 (1988)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Aiex, R.M., Binato, S., Resende, M.G.C.: Parallel GRASP with Path-Relinking for Job Shop Scheduling. Parallel Computing 29, 393–430 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Applegate, D., Cook, W.: A Computational Study of Job-Shop Scheduling. ORSA Journal on Computing 3(2), 149–156 (1991)CrossRefMATHGoogle Scholar
  4. 4.
    Aydin, M.E., Fogarty, T.C.: A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems. Journal of Heuristics 10, 269–292 (2004)CrossRefGoogle Scholar
  5. 5.
    Balas, E., Vazacopoulos, A.: Guided Local Search with Shifting Bottleneck for Job Shop Scheduling. Management Science 44, 262–275 (1998)CrossRefMATHGoogle Scholar
  6. 6.
    Beasley, J.E.: Obtaining Test Problems via Internet. Journal of Global Optimisation 8, 429-433,
  7. 7.
    Bierwith, C.: A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms. OR Spektrum 17, 87–92 (1995)CrossRefGoogle Scholar
  8. 8.
    Blum, C., Sampels, M.: An Ant Colony Optimization Algorithm for Shop Scheduling Problems. Journal of Mathematical Modelling and Algorithms 3, 285–308 (2004)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Bruce, K.B., Cardelli, L., Pierce, B.C.: Comparing Object Encodings. In: Ito, T., Abadi, M. (eds.) TACS 1997. LNCS, vol. 1281, pp. 415–438. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  10. 10.
    Carlier, J., Pison, E.: An Algorithm for Solving the Job-Shop Problem. Management Science 35, 164–176 (1989)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Cheng, R., Gen, M., Tsujimura, Y.: A Tutorial Survey of Job Shop Scheduling Problems Using genetic Algorithms-I. Representation. Journal of Computers and Industrial Engineering 30(4), 983–997 (1996)CrossRefGoogle Scholar
  12. 12.
    Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant System for Job-Shop Scheduling. Belgian Journal of Operations Research, Statistics and Computer Science (JORBEL) 34(1), 39–53 (1994)MATHGoogle Scholar
  13. 13.
    Dell’Amico, M., Trubian, M.: Applying Tabu-Search to the Job-Shop Scheduling Problem. Annals of Operations Research 4, 231–252 (1993)CrossRefMATHGoogle Scholar
  14. 14.
    Dorndorf, U., Pesch, E.: Evolution Based Learning in a Job Shop Scheduling Environment. Computers & Operations Research 22 (1995); 0:30 International Journal of Production Research. A hybrid PSO for the JSSP (June 26, 2005)Google Scholar
  15. 15.
    Dorndorf, U., Pesch, E., Phan-Huy, T.: Constraint Propagation and Problem Decomposition: A Preprocessing Procedure for the Job Shop Problem. Annals of Operations Research 115, 125–145 (2002)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Garey, M., Johnson, D., Sethy, R.: The Complexity of Flow Shop and Job Shop Scheduling. Mathematics of Operations Research 1, 117–129 (1976)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Groce, F.D., Tadei, R., Volta, G.: A Genetic Algorithm for the Job Shop Problem. Computers & Operations Research 22, 15–24 (1995)CrossRefMATHGoogle Scholar
  18. 18.
    Goncalves, J.F., Mendes, J.M., Resende, M.: A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operations Research 167(1), 77–95 (2004)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Huang, W., Yin, A.: An Improved Shifting Bottleneck Procedure for the Job Shop Scheduling Problem. Computers & Operations Research 31, 2093–2110 (2004)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Jain, A., Meeran, S.: Deterministic Job-Shop Scheduling: Past, Present and Future. European Journal of Operational Research 113, 390–434 (1999)CrossRefMATHGoogle Scholar
  21. 21.
    Kolonko, M.: Some New Results on Simulated Annealing Applied to the Job Shop Scheduling Problem. European Journal of Operational Research 113, 123–136 (1999)CrossRefMATHGoogle Scholar
  22. 22.
    Mladenovic, N., Hansen, P.: Variable Neighborhood Search. Computers and Operations Research 24, 1097–1100 (1997)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Nowicki, E., Smutnicki, C.: A Fast Taboo Search Algorithm for the Job Shop Problem. Management Science 42, 797–813 (1996)CrossRefMATHGoogle Scholar
  24. 24.
    Pezzella, F., Merelli, E.: A Tabu Search Method Guided by Shifting Bottleneck for the Job Shop Scheduling Problem. European Journal of Operational Research 120, 297–310 (2000)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Ribeiroa, C.C., Souza, M.C.: Variable neighborhood search for the degree-constrained minimum spanning tree problem. Discrete Applied Mathematics 118, 43–54 (2002)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Satake, T., Morikawa, K., Takahashi, K., Nakamura, N.: Neural Network Approach for Minimizing the Makespan of the General Job- Shop. International Journal of Production Economics 33, 67–74 (1994)CrossRefGoogle Scholar
  27. 27.
    Satake, T., Morikawa, K., Takahashi, K., Nakamura, N.: Simulated Annealing Approach for Minimizing the Makespan of the General Job- Shop. International Journal of Production Economics 60, 515–522 (1999)CrossRefGoogle Scholar
  28. 28.
    Urosevic, D., Brimberg, J., Mladenovic, N.: Variable neighborhood decomposition search for the edge weighted k-cardinality tree problem. Computers & Operations Research 31, 1205–1213 (2004)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mehmet Sevkli
    • 1
  • M. Emin Aydin
    • 2
  1. 1.Dept. of Industrial EngineeringFatih UniversityBuyukcekmeceTurkey
  2. 2.Department of Computing and Information SystemsUniversity of LutonLutonUK

Personalised recommendations