Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2018, Article ID 5914360, 13 pages
https://doi.org/10.1155/2018/5914360
Research Article

A New Imperialist Competitive Algorithm for Multiobjective Low Carbon Parallel Machines Scheduling

School of Automation, Wuhan University of Technology, Wuhan 430070, China

Correspondence should be addressed to Qingyong Zhang; nc.ude.tuhw@gnahzyq

Received 22 October 2017; Revised 6 March 2018; Accepted 11 March 2018; Published 16 April 2018

Academic Editor: Erik Cuevas

Copyright © 2018 Zixiao Pan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. C. E. Cheng and C. C. S. Sin, “A state-of-the-art review of parallel-machine scheduling research,” European Journal of Operational Research, vol. 47, no. 3, pp. 271–292, 1990. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Li, H. Yang, S. Zhang, and G. Liu, “Unrelated parallel machine scheduling problem with energy and tardiness cost,” The International Journal of Advanced Manufacturing Technology, vol. 84, no. 1-4, pp. 213–226, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Liang, H.-D. Yang, G.-S. Liu, and J.-H. Guo, “An ant optimization model for unrelated parallel machine scheduling with energy consumption and total tardiness,” Mathematical Problems in Engineering, vol. 2015, Article ID 907034, 8 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Li, X. Zhang, J. Y.-T. Leung, and S.-L. Yang, “Parallel machine scheduling problems in green manufacturing industry,” Journal of Manufacturing Systems, vol. 38, pp. 98–106, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Che, Y. Zeng, and K. Lyu, “An efficient greedy insertion heuristic for energy-conscious single machine scheduling problem under time-of-use electricity tariffs,” Journal of Cleaner Production, vol. 129, pp. 565–577, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. Y.-C. Wang, M.-J. Wang, and S.-C. Lin, “Selection of cutting conditions for power constrained parallel machine scheduling,” Robotics and Computer-Integrated Manufacturing, vol. 43, pp. 105–110, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. S.-W. Lin, K.-C. Ying, W.-J. Wu, and Y.-I. Chiang, “Multi-objective unrelated parallel machine scheduling: A Tabu-enhanced iterated Pareto greedy algorithm,” International Journal of Production Research, vol. 54, no. 4, pp. 1110–1121, 2016. View at Publisher · View at Google Scholar · View at Scopus
  8. K. C. Ying, “A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems,” International Journal of Production Research, vol. 53, pp. 1065–1076, 2015. View at Google Scholar
  9. X. Li, F. Yalaoui, L. Amodeo, and H. Chehade, “Metaheuristics and exact methods to solve a multiobjective parallel machines scheduling problem,” Journal of Intelligent Manufacturing, vol. 23, no. 4, pp. 1179–1194, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II,” in Parallel Problem Solving from Nature PPSN VI, vol. 1917 of Lecture Notes in Computer Science, pp. 849–858, Springer, Berlin, Germany, 2000. View at Publisher · View at Google Scholar
  11. S. H. Pakzad-Moghaddam, “A Lévy flight embedded particle swarm optimization for multi-objective parallel-machine scheduling with learning and adapting considerations,” Computers & Industrial Engineering, vol. 91, pp. 109–128, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Afzalirad and J. Rezaeian, “A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches,” Applied Soft Computing, vol. 50, pp. 109–123, 2017. View at Publisher · View at Google Scholar · View at Scopus
  13. M. H. F. Zarandi and V. Kayvanfar, “A bi-objective identical parallel machine scheduling problem with controllable processing times: a just-in-time approach,” The International Journal of Advanced Manufacturing Technology, vol. 77, no. 1-4, pp. 545–563, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Atashpaz-Gargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 4661–4667, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. Ardalan, S. Karimi, O. Poursabzi, and B. Naderi, “A novel imperialist competitive algorithm for generalized traveling salesman problems,” Applied Soft Computing, vol. 26, pp. 546–555, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Kaveh and S. Talatahari, “Optimum design of skeletal structures using imperialist competitive algorithm,” Computers & Structures, vol. 88, no. 21-22, pp. 1220–1229, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Hosseini, A. A. Khaled, and S. Vadlamani, “Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem,” Neural Computing and Applications, vol. 25, no. 7-8, pp. 1871–1885, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Lian, C. Zhang, L. Gao, and X. Shao, “Single row facility layout problem using an imperialist competitive algorithm,” in Proceedings of the in Proceedings of the 41st International Conference on Computers and Industrial Engineering, pp. 578–586, 2011.
  19. B. Mohammadi-ivatloo, A. Rabiee, A. Soroudi, and M. Ehsan, “Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch,” Energy, vol. 44, no. 1, pp. 228–240, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Bagher, M. Zandieh, and H. Farsijani, “Balancing of stochastic U-type assembly lines: An imperialist competitive algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 54, no. 1-4, pp. 271–285, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Wei, J. Yan, Y. Li, C. Xia, and J. Zheng, “Imperialist competitive algorithm for assembly sequence planning,” The International Journal of Advanced Manufacturing Technology, vol. 67, no. 9-12, pp. 2207–2216, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Wang, Z. Guan, D. Li, C. Zhang, and L. Chen, “Two-sided assembly line balancing with operator number and task constraints: a hybrid imperialist competitive algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 74, no. 5-8, pp. 791–805, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. M. A. Ahmadi, M. Ebadi, A. Shokrollahi, and S. M. J. Majidi, “Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir,” Applied Soft Computing, vol. 13, no. 2, pp. 1085–1098, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. K. Devika, A. Jafarian, and V. Nourbakhsh, “Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques,” European Journal of Operational Research, vol. 235, no. 3, pp. 594–615, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. J. Behnamian and M. Zandieh, “A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties,” Expert Systems with Applications, vol. 38, no. 12, pp. 14490–14498, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. S. M. Goldansaz, F. Jolai, and A. H. Zahedi Anaraki, “A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop,” Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, vol. 37, no. 23, pp. 9603–9616, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  27. D. Lei, M. Li, and L. Wang, “A two-phase meta-heuristic for multiobjective flexible job shop scheduling problem with total energy consumption threshold,” IEEE Transactions on Cybernetics, pp. 1–13, 2018. View at Publisher · View at Google Scholar
  28. B. Naderi and M. Yazdani, “A model and imperialist competitive algorithm for hybrid flow shops with sublots and setup times,” Journal of Manufacturing Systems, vol. 33, no. 4, pp. 647–653, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Mortazavi, A. A. Khamseh, and B. Naderi, “A novel chaotic imperialist competitive algorithm for production and air transportation scheduling problems,” Neural Computing and Applications, vol. 26, no. 7, pp. 1709–1723, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Forouharfard and M. Zandieh, “An imperialist competitive algorithm to schedule of receiving and shipping trucks in cross-docking systems,” The International Journal of Advanced Manufacturing Technology, vol. 51, no. 9-12, pp. 1179–1193, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. P. Zhang, Y. Lv, and J. Zhang, “An improved imperialist competitive algorithm based photolithography machines scheduling,” International Journal of Production Research, pp. 1–13, 2017. View at Publisher · View at Google Scholar · View at Scopus
  32. N. Karimi, M. Zandieh, and A. A. Najafi, “Group scheduling in flexible flow shops: A hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism,” International Journal of Production Research, vol. 49, no. 16, pp. 4965–4977, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Karimi, Z. Ardalan, B. Naderi, and M. Mohammadi, “Scheduling flexible job-shops with transportation times: mathematical models and a hybrid imperialist competitive algorithm,” Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, vol. 41, pp. 667–682, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. H. Seidgar, M. Zandieh, H. Fazlollahtabar, and I. Mahdavi, “Simulated imperialist competitive algorithm in two-stage assembly flow shop with machine breakdowns and preventive maintenance,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 230, no. 5, pp. 934–953, 2016. View at Publisher · View at Google Scholar · View at Scopus
  35. S. Hosseini and A. Al Khaled, “A survey on the Imperialist Competitive Algorithm metaheuristic: Implementation in engineering domain and directions for future research,” Applied Soft Computing, vol. 24, pp. 1078–1094, 2014. View at Publisher · View at Google Scholar · View at Scopus
  36. L. Jin, C. Zhang, X. Shao, X. Yang, and G. Tian, “A multi-objective memetic algorithm for integrated process planning and scheduling,” The International Journal of Advanced Manufacturing Technology, vol. 85, no. 5-8, pp. 1513–1528, 2016. View at Publisher · View at Google Scholar · View at Scopus
  37. U. Güvenç and F. Katırcıoğlu, “Escape velocity: a new operator for gravitational search algorithm,” Neural Computing and Applications, pp. 1–16, 2017. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Zhang, G. Sun, Z. Wang, and Y. Yao, “A hybrid genetic algorithm and gravitational search algorithm for global optimization,” Neural Network World, vol. 25, no. 1, pp. 53–73, 2015. View at Publisher · View at Google Scholar · View at Scopus