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Incorporating Highly Explorative Methods to Improve the Performance of Variable Neighborhood Search

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Book cover Transactions on Computational Science XXI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 8160))

Abstract

Variable Neighborhood Search (VNS) is one of the most recently introduced metaheuristics. Although VNS is successfully applied on various problem domains, there is still some room for it to get improved. While VNS has an efficient exploitation strategy, it suffers from its inefficient solution space exploration. To overcome this limitation, VNS can be joined with explorative methods such as Evolutionary Algorithms (EAs) which are global population-based search methods. Due to its effective search space exploration, Differential Evolution (DE) is a popular EA which is a great candidate to be joined with VNS. In this article, two different DEs are proposed to be combined with VNS. The first DE uses explorative evolutionary operators and the second one is a Multi-Population Differential Evolution (MP-DE). Incorporating a number of sub-populations improves the population diversity and increases the chance of reaching to unexplored regions. Both proposed hybrid methods are evaluated on the classical Job Shop Scheduling Problems. The experimental results reveal that the combination of VNS with more explorative method is more reliable to find acceptable solutions. Furthermore, the proposed methods offer competitive solutions compared to the state-of-the-art hybrid EAs proposed to solve JSSPs.

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References

  1. Mladenovic, N., Hansen, P.: Variable neighborhood search. Computers and Operations Research 24, 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  2. Felipe, A., Ortuno, M.T., Tirado, G.: The double traveling salesman problem with multiple stacks: a variable neighborhood search approach. Computers and Operations Research 36(11), 2983–2993 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Fleszar, K., Osman, I.H., Hindi, K.S.: A variable neighbourhood search algorithm for the open vehicle routing problem. European Journal of Operational Research 195, 803–809 (2009)

    Article  MATH  Google Scholar 

  4. Fleszar, K., Hindi, K.S.: An effective vns for the capacitated p-median problem. European Journal of Operational Research 191(3), 612–622 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Brimberg, J., Mladenovic, N., Urosevic, D., Ngai, E.: Variable neighborhood search for the heaviest k-subgraph. Computers and Operations Research 36(11), 2885–2891 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Crainic, T., Gendreau, M., Hansen, P., Mladenovic, N.: Cooperative parallel variable neighborhood search for the p-median. Journal of Heuristics 10, 289–310 (2004)

    Google Scholar 

  7. Mansini, R., Tocchella, B.: The traveling purchaser problem with budget constraint. Computers and Operations Research 36(7), 2263–2274 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Wang, X., Tang, L.: A population-based variable neighborhood search for the single machine total weighted tardiness problem. Computers and Operations Research Archive 36(6), 2105–2110 (2009)

    Article  MATH  Google Scholar 

  9. Raeesi N., M.R., Kobti, Z.: Incorporating a genetic algorithm to improve the performance of variable neighborhood search. In: The Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 144–149 (2012)

    Google Scholar 

  10. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)

    Google Scholar 

  11. Moscato, P.: Memetic algorithms: A short introduction. New Ideas in Optimization 14, 219–234 (1999)

    Google Scholar 

  12. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  13. Vasile, M., Minisci, E.A., Locatelli, M.: An inflationary differential evolution algorithm for space trajectory optimization. IEEE Transactions on Evolutionary Computation 15(2), 267–281 (2011)

    Article  Google Scholar 

  14. Sharma, M., Pandit, M., Srivastava, L.: Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation. International Journal of Electrical Power and Energy Systems 33(3), 753–766 (2011)

    Article  Google Scholar 

  15. Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Transactions on Evolutionary Computation 12(1), 107–125 (2008)

    Article  Google Scholar 

  16. Onwubolu, G., Davendra, D.: Scheduling flow shops using differential evolution algorithm. European Journal of Operational Research 171(2), 674–692 (2006)

    Article  MATH  Google Scholar 

  17. Qian, B., Wang, L., Hu, R., Wang, W.L., Huang, D.X., Wang, X.: A hybrid differential evolution method for permutation flow-shop scheduling. The International Journal of Advanced Manufacturing Technology 38(7), 757–777 (2008)

    Article  Google Scholar 

  18. Zhang, R., Wu, C.: A hybrid differential evolution and tree search algorithm for the job shop scheduling problem. Mathematical Problems in Engineering 2011, Article ID 390593 (2011)

    Google Scholar 

  19. Pan, Q.K., Tasgetiren, M.F., Liang, Y.C.: A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Computers & Industrial Engineering 55(4), 795–816 (2008)

    Article  Google Scholar 

  20. Wang, L., Pan, Q.K., Suganthan, P.N., Wang, W.H., Wang, Y.M.: A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Computers & Operations Research 37(3), 509–520 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  21. Price, K., Storn, R., Lampinen, J.: Differential evolution: a practical approach to global optimization (Natural Computing Series). Springer, New York (2005)

    Google Scholar 

  22. Wisittipanich, W., Kachitvichyanukul, V.: Two enhanced differential evolution algorithms for job shop scheduling problems. International Journal of Production Research 50(10), 2757–2773 (2012)

    Article  Google Scholar 

  23. Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Parallel differential evolution. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2023–2029 (2004)

    Google Scholar 

  24. Tasgetiren, M.F., Suganthan, P.N.: A multi-populated differential evolution algorithm for solving constrained optimization problem. In: IEEE Congress on Evolutionary Computation (CEC), pp. 340–354 (2006)

    Google Scholar 

  25. Yu, W.J., Zhang, J.: Multi-population differential evolution with adaptive parameter control for global optimization. In: Genetic and Evolutionary Computation Conference (GECCO), Dublin, Ireland, pp. 1093–1098 (2011)

    Google Scholar 

  26. Mendes, R., Mohais, A.S.: DynDE: A differential evolution for dynamic optimization problems. In: IEEE Congress on Evolutionary Computation (CEC), vol. 2, pp. 2808–2815 (2005)

    Google Scholar 

  27. Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research 1, 117–129 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  28. Baker, K.R.: Introduction to Sequencing and Scheduling. Wiley (1974)

    Google Scholar 

  29. Croce, F., Tadei, R., Volta, G.: A genetic algorithm for the job shop problem. Computers in Operations Research 22, 15–24 (1995)

    Article  MATH  Google Scholar 

  30. Hasan, S., Sarker, R., Essam, D., Cornforth, D.: Memetic algorithms for solving job-shop scheduling problems. Memetic Computing 1, 69–83 (2008)

    Article  MATH  Google Scholar 

  31. Becerra, R.L., Coello, C.A.C.: A cultural algorithm for solving the job-shop scheduling problem. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation. STUDFUZZ, vol. 167, pp. 37–55. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  32. Gao, J., Sun, L., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers and Operations Research 35(9), 2892–2907 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  33. Caumond, A., Lacomme, P., Tcherneva, N.: A memetic algorithm for the job-shop with time-lags. Computers and Operations Research 35(7), 2331–2356 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  34. Chiang, T.C., Cheng, H.C., Fu, L.C.: NNMA: An effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems. Expert Systems with Applications 38(5), 5986–5999 (2011)

    Article  Google Scholar 

  35. Bierwirth, C.: A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spectrum. Special Issue on Applied Local Search 17(2-3), 87–92 (1995)

    Article  MATH  Google Scholar 

  36. Tasgetiren, M.F., Sevkli, M., Liang, Y.C., Gencyilmaz, G.: Particle swarm optimization algorithm for single-machine total weighted tardiness problem. In: Congress on Evolutionary Computation (CEC), Portland, Oregan, USA, pp. 1412–1419 (2004)

    Google Scholar 

  37. Blazewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: Conventional and new solution techniques. European Journal of Operational Research 93(1), 1–33 (1996)

    Article  MATH  Google Scholar 

  38. Van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job shop scheduling by simulated annealing. Operations Research 40(1), 113–125 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  39. Matsuo, H., Suh, C., Sullivan, R.: A controlled search simulated annealing method for the general job shop scheduling problem. In: Working Paper 03-04-88. University of Texas at Austin (1988)

    Google Scholar 

  40. Dell’Amico, M., Trubian, M.: Applying tabu search to the job-shop scheduling problem. Annals of Operations Research 41(3), 231–252 (1993)

    Article  MATH  Google Scholar 

  41. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop scheduling problem. Management Science 42(6), 797–813 (1996)

    Article  MATH  Google Scholar 

  42. Balas, E., Vazacopoulos, A.: Guided local search with shifting bottleneck for job shop scheduling. Management Science 44(2), 262–275 (1998)

    Article  MATH  Google Scholar 

  43. Raeesi, M.R.N., Kobt, Z.: A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic. Memetic Computing - Special Issue on Optimization on Complex Systems 4(3), 231–245 (2012)

    Article  Google Scholar 

  44. Lawrence, S.: Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques. Master’s thesis, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, Pennsylvania (1984)

    Google Scholar 

  45. Zobolas, G.I., Tarantilis, C.D., Ioannou, G.: A hybrid evolutionary algorithm for the job shop scheduling problem. Journal of the Operational Research Society 60, 221–235 (2009)

    Article  MATH  Google Scholar 

  46. González, M.A., Vela, C.R., Varela, R.: A new hybrid genetic algorithm for job shop scheduling problem. Computers and Operations Research 39(10), 2291–2299 (2012)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, University of Windsor, Windsor, ON, Canada

    Mohammad R. Raeesi N. & Ziad Kobti

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  1. Mohammad R. Raeesi N.
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  2. Ziad Kobti
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Editors and Affiliations

  1. University of Calgary, AB, Canada

    Marina L. Gavrilova

  2. CloudFabriQ Ltd., London, UK

    C. J. Kenneth Tan

  3. Technical University of Ostrava, Czech Republic

    Ajith Abraham

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Raeesi N., M.R., Kobti, Z. (2013). Incorporating Highly Explorative Methods to Improve the Performance of Variable Neighborhood Search. In: Gavrilova, M.L., Tan, C.J.K., Abraham, A. (eds) Transactions on Computational Science XXI. Lecture Notes in Computer Science, vol 8160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45318-2_14

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  • DOI: https://doi.org/10.1007/978-3-642-45318-2_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45317-5

  • Online ISBN: 978-3-642-45318-2

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