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Advances in Operations Research
Volume 2017, Article ID 7048042, 10 pages
https://doi.org/10.1155/2017/7048042
Research Article

Towards Merging Binary Integer Programming Techniques with Genetic Algorithms

School of Computing and Information Technology, Wollongong University, Wollongong, NSW 2522, Australia

Correspondence should be addressed to Reza Zamani; ua.ude.wou@azer

Received 10 June 2017; Revised 27 August 2017; Accepted 6 September 2017; Published 17 October 2017

Academic Editor: Demetrio Laganà

Copyright © 2017 Reza Zamani. 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. Y. Yan, “Genetic-binary combinatorial algorithm for 0-1 integer programming,” in Proceedings of the International Workshop on Autonomous Decentralized System, pp. 205–209, September 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. N. P. Theodorakatos, N. M. Manousakis, and G. N. Korres, “Optimal placement of PMUs in power systems using binary integer programming and genetic algorithm,” in Proceedings of the 9th Mediterranean Conference on Power Generation, Transmission Distribution and Energy Conversion, MedPower 2014, November 2014. View at Scopus
  3. J. A. Joines, C. T. Culbreth, and R. E. King, “Manufacturing cell design: an integer programming model employing genetic algorithms,” IIE Transactions, vol. 28, no. 1, pp. 69–85, 1996. View at Publisher · View at Google Scholar · View at Scopus
  4. T. Yokota, M. Gen, and Y.-X. Li, “Genetic algorithm for non-linear mixed integer programming problems and its applications,” Computers & Industrial Engineering, vol. 30, no. 4, pp. 905–917, 1996. View at Publisher · View at Google Scholar · View at Scopus
  5. Y.-C. Lin, K.-S. Hwang, and F.-S. Wang, “A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems,” Computers & Mathematics with Applications. An International Journal, vol. 47, no. 8-9, pp. 1295–1307, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  6. K. Deep, K. P. Singh, M. L. Kansal, and C. Mohan, “A real coded genetic algorithm for solving integer and mixed integer optimization problems,” Applied Mathematics and Computation, vol. 212, no. 2, pp. 505–518, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. C. F. M. Toledo, L. De Oliveira, R. De Freitas Pereira, P. M. França, and R. Morabito, “A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem,” Computers & Operations Research, vol. 48, pp. 40–52, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Poli, G. Oliveri, and A. Massa, “An integer genetic algorithm for optimal clustering in phased array antenna,” in Proceedings of the 2017 International Applied Computational Electromagnetics Society Symposium - Italy (ACES), pp. 1-2, Florence, March 2017. View at Publisher · View at Google Scholar
  9. R. Zamani, R. B. Brown, G. Beydoun, and W. J. Tibben, “The Architecture of an Effective Software Application for Managing Enterprise Projects,” The Journal of Modern Project Management, vol. 5, no. 1, 2017. View at Google Scholar
  10. R. Kolisch, “Efficient priority rules for the resource-constrained project scheduling problem,” Journal of Operations Management, vol. 14, no. 3, pp. 179–192, 1996. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Kelley, “The critical-path method: resource planning and scheduling,” in Industrial Scheduling, J. F. Muth and G. L. Thompson, Eds., pp. 347–365, Prentice-Hall, Upper Saddle River, NJ, USA, 1963. View at Google Scholar
  12. D. D. Bedworth and J. E. Bailey, Integrated Production Control Systems-Management, Analysis, Design, Wiley, New York, NY, USA, 1982.
  13. R. Kolisch, “Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation,” European Journal of Operational Research, vol. 90, no. 2, pp. 320–333, 1996. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Kolisch and R. Padman, “An integrated survey of deterministic project scheduling,” Omega , vol. 29, no. 3, pp. 249–272, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Kolisch and S. Hartmann, “Experimental investigation of heuristics for resource-constrained project scheduling: an update,” European Journal of Operational Research, vol. 174, no. 1, pp. 23–37, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. V. Valls, F. Ballestín, and S. Quintanilla, “Justification and RCPSP: A technique that pays,” European Journal of Operational Research, vol. 165, no. 2, pp. 375–386, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. J. D. Wiest, “Some Properties of Schedules for Large Projects with Limited Resources,” Operations Research, vol. 12, no. 3, pp. 395–418, 1964. View at Publisher · View at Google Scholar
  18. K. Y. Li and R. J. Willis, “An iterative scheduling technique for resource-constrained project scheduling,” European Journal of Operational Research, vol. 56, no. 3, pp. 370–379, 1992. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Tormos and A. Lova, “A competitive heuristic solution technique for resource-constrained project scheduling,” Annals of Operations Research, vol. 102, pp. 65–81, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. D. Debels, B. De Reyck, R. Leus, and M. Vanhoucke, “A hybrid scatter search/electromagnetism meta-heuristic for project scheduling,” European Journal of Operational Research, vol. 169, no. 2, pp. 638–653, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. V. Valls, F. Ballestin, and S. Quintanilla, “A population-based approach to the resource-constrained project scheduling problem,” Annals of Operations Research, vol. 131, pp. 305–324, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  22. A. A. B. Pritsker and L. J. Watters, A zero-one programming approach to scheduling with limited resources, RAND Corporation, Santa Monica, Calif, USA, 1968.
  23. N. Christofides, R. Alvarez-Valdes, and J. M. Tamarit, “Project scheduling with resource constraints: a branch and bound approach,” European Journal of Operational Research, vol. 29, no. 3, pp. 262–273, 1987. View at Publisher · View at Google Scholar · View at MathSciNet
  24. O. Koné, C. Artigues, P. Lopez, and M. Mongeau, “Event-based MILP models for resource-constrained project scheduling problems,” Computers & Operations Research, vol. 38, no. 1, pp. 3–13, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  25. P. Baptiste and S. Demassey, “Tight LP bounds for resource constrained project scheduling,” OR Spectrum, vol. 26, no. 2, pp. 251–262, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. P. Brucker, S. Knust, A. Schoo, and O. Thiele, “A branch and bound algorithm for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 107, no. 2, pp. 272–288, 1998. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Mingozzi, V. Maniezzo, S. Ricciardelli, and L. Bianco, “An exact algorithm for the resource-constrained project scheduling problem based on a new mathematical formulation,” Management Science, vol. 44, no. 5, pp. 714–729, 1998. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Brucker and S. Knust, “Linear programming and constraint propagation-based lower bound for the RCPSP,” European Journal of Operational Research, vol. 127, no. 2, pp. 355–362, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Demassey, C. Artigues, and P. Michelon, “Constraint-propagation-based cutting planes: an application to the resource-constrained project scheduling problem,” INFORMS Journal on Computing, vol. 17, no. 1, pp. 52–65, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. R. A.-V. Olaguíbel and J. T. Goerlich, “The project scheduling polyhedron: Dimension, facets and lifting theorems,” European Journal of Operational Research, vol. 67, no. 2, pp. 204–220, 1993. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Artigues, P. Michelon, and S. Reusser, “Insertion techniques for static and dynamic resource-constrained project scheduling,” European Journal of Operational Research, vol. 149, no. 2, pp. 249–267, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. M. Sabzehparvar and S. M. Seyed-Hosseini, “A mathematical model for the multi-mode resource-constrained project scheduling problem with mode dependent time lags,” The Journal of Supercomputing, vol. 44, no. 3, pp. 257–273, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. C. Artigues, P. Brucker, S. Knust, O. Koné, P. Lopez, and M. Mongeau, “A note on “Event-based MILP models for resource-constrained project scheduling problems",” Computers & Operations Research, vol. 40, no. 4, pp. 1060–1063, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  34. T. Baar, P. Brucker, and S. Knust, “Tabu search algorithms and lower bounds for the resource-constrained project scheduling problem,” in Meta-heuristics: advances and trends in local search paradigms for optimization (Sophia-Antipolis, 1997), pp. 1–18, Kluwer Acad. Publ., Boston, Mass, USA, 1999. View at Google Scholar · View at MathSciNet
  35. S. Hartmann and R. Kolisch, “Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 127, no. 2, pp. 394–407, 2000. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Klein, “Bidirectional planning: Improving priority rule-based heuristics for scheduling resource-constrained projects,” European Journal of Operational Research, vol. 127, no. 3, pp. 619–638, 2000. View at Publisher · View at Google Scholar · View at Scopus
  37. D. Merkle, M. Middendorf, and H. Schmeck, “Ant colony optimization for resource-constrained project scheduling,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 333–346, 2002. View at Publisher · View at Google Scholar · View at Scopus
  38. K. Bouleimen and H. Lecocq, “A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version,” European Journal of Operational Research, vol. 149, no. 2, pp. 268–281, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  39. M. Palpant, C. Artigues, and P. Michelon, “LSSPER: solving the resource-constrained project scheduling problem with large neighbourhood search,” Annals of Operations Research, vol. 131, no. 1, pp. 237–257, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  40. K. Fleszar and K. S. Hindi, “Solving the resource-constrained project scheduling problem by a variable neighbourhood search,” European Journal of Operational Research, vol. 155, no. 2, pp. 402–413, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  41. I. Pesek and J. Žerovnik, “Best insertion algorithm for resource-constrained project scheduling problem,” in 11th International Conference on Operational Research, Pula, Croatia, 2006.
  42. A. Lim, H. Ma, B. Rodrigues, S. T. Tan, and F. Xiao, “New meta-heuristics for the resource-constrained project scheduling problem,” Flexible Services and Manufacturing Journal, vol. 25, no. 1-2, pp. 48–73, 2013. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Hartmann, “A self-adapting genetic algorithm for project scheduling under resource constraints,” Naval Research Logistics (NRL), vol. 49, no. 5, pp. 433–448, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  44. J. J. Mendes, J. F. Goncalves, and M. G. Resende, “A random key based genetic algorithm for the resource constrained project scheduling problem,” Computers & Operations Research, vol. 36, no. 1, pp. 92–109, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  45. R. Zamani, “An accelerating two-layer anchor search with application to the resource-constrained project scheduling problem,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 6, pp. 975–984, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. X. Pan and H. Chen, “A multi-agent social evolutionary algorithm for resource-constrained project scheduling,” in Proceedings of the 2010 International Conference on Computational Intelligence and Security, CIS 2010, pp. 209–213, chn, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. J. F. Gonçalves, M. G. C. Resende, and J. J. M. Mendes, “A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem,” Journal of Heuristics, vol. 17, no. 5, pp. 467–486, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. V. Valls, F. Ballestín, and S. Quintanilla, “A hybrid genetic algorithm for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 185, no. 2, pp. 495–508, 2008. View at Publisher · View at Google Scholar · View at Scopus
  49. R. Zamani, “A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 229, no. 2, pp. 552–559, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  50. N. Kumar and D. P. Vidyarthi, “A model for resource-constrained project scheduling using adaptive PSO,” Soft Computing, vol. 20, no. 4, pp. 1565–1580, 2016. View at Publisher · View at Google Scholar · View at Scopus
  51. A. Sprecher, “Network decomposition techniques for resource-constrained project scheduling,” Journal of the Operational Research Society, vol. 53, no. 4, pp. 405–414, 2002. View at Publisher · View at Google Scholar · View at Scopus
  52. R. Zamani, “An efficient time-windowing procedure for scheduling projects under multiple resource constraints,” OR Spectrum, vol. 26, no. 3, pp. 423–440, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  53. D. Debels and M. Vanhoucke, “A decomposition-based genetic algorithm for the resource-constrained project-scheduling problem,” Operations Research, vol. 55, no. 3, pp. 457–469, 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. R. Zamani, “A hybrid decomposition procedure for scheduling projects under multiple resource constraints,” Operational Research, vol. 11, no. 1, pp. 93–111, 2011. View at Publisher · View at Google Scholar · View at Scopus
  55. A. Agarwal, S. Colak, and S. Erenguc, “A neurogenetic approach for the resource-constrained project scheduling problem,” Computers & Operations Research, vol. 38, no. 1, pp. 44–50, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  56. P. P. Das and S. Acharyya, “Hybrid local search methods in solving resource constrained project scheduling problem,” Journal of Computers (Finland), vol. 8, no. 5, pp. 1157–1166, 2013. View at Publisher · View at Google Scholar · View at Scopus
  57. F. Berthaut, R. A. Pellerin, N. Hajji, and Perrier., A Path Relinking-Based Scatter Search for the Resource-Constrained Project Scheduling Problem, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, Canada, 2014.
  58. G. Koulinas, L. Kotsikas, and K. Anagnostopoulos, “A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem,” Information Sciences, vol. 277, pp. 680–693, 2014. View at Publisher · View at Google Scholar · View at Scopus
  59. P. Jedrzejowicz and E. Ratajczak-Ropel, “Reinforcement Learning strategies for A-Team solving the Resource-Constrained Project Scheduling Problem,” Neurocomputing, vol. 146, pp. 301–307, 2014. View at Publisher · View at Google Scholar · View at Scopus
  60. Ö. H. Bettemir and R. Sonmez, “Hybrid genetic algorithm with simulated annealing for resource-constrained project scheduling,” Journal of Management in Engineering, vol. 31, no. 5, Article ID 04014082, 2015. View at Publisher · View at Google Scholar · View at Scopus
  61. R. Zamani, “An evolutionary implicit enumeration procedure for solving the resource-constrained project scheduling problem,” International Transactions in Operational Research, vol. 24, no. 6, pp. 1525–1547, 2017. View at Google Scholar
  62. R. Kolisch and A. Sprecher, “PSPLIB—a project scheduling problem library,” European Journal of Operational Research, vol. 96, no. 1, pp. 205–216, 1997. View at Publisher · View at Google Scholar · View at Scopus
  63. M. Amirghasemi and R. Zamani, “An effective evolutionary hybrid for solving the permutation flowshop scheduling problem,” Evolutionary Computation, vol. 25, no. 1, pp. 87–111, 2017. View at Publisher · View at Google Scholar · View at Scopus
  64. M. Amirghasemi and R. Zamani, “An effective asexual genetic algorithm for solving the job shop scheduling problem,” Computers & Industrial Engineering, vol. 83, article no. 3956, pp. 123–138, 2015. View at Publisher · View at Google Scholar · View at Scopus