DOI QR코드

DOI QR Code

Development of a Heuristic Algorithm Based on Simulated Annealing for Time-Resource Tradeoffs in Project Scheduling Problems

시간-자원 트레이드오프 프로젝트 스케줄링 문제 해결을 위한 시뮬레이티드 어닐링 기반 휴리스틱 알고리즘 개발

  • 김건아 (고려대학교 산업경영공학과) ;
  • 서윤호 (고려대학교 산업경영공학과)
  • Received : 2019.11.06
  • Accepted : 2019.12.02
  • Published : 2019.12.31

Abstract

Purpose This study develops a heuristic algorithm to solve the time-resource tradeoffs in project scheduling problems with a real basis. Design/methodology/approach Resource constrained project scheduling problem with time-resource tradeoff is well-known as one of the NP-hard problems. Previous researchers have proposed heuristic that minimize Makespan of project scheduling by deriving optimal combinations from finite combinations of time and resource. We studied to solve project scheduling problems by deriving optimal values from infinite combinations. Findings We developed heuristic algorithm named Push Algorithm that derives optimal combinations from infinite combinations of time and resources. Developed heuristic algorithm based on simulated annealing shows better improved results than genetic algorithm and further research suggestion was discussed as a project scheduling problem with multiple resources of real numbers.

References

  1. 김준우. "Job Shop 일정계획 문제 풀이를 위한 유전 알고리즘의 복호화 방법". 정보시스템연구, 25(4), 2016, pp105-119.
  2. 정성욱, & 김준우. "후보순위 기반 타부 서치를 이용한 제약 조건을 갖는 작업 순서결정 문제 풀이" 정보시스템연구, 25(1), 2016, pp159-182.
  3. Bell, C. E., & Park, K., "Solving resource - constrained project scheduling problems by a* search." Naval Research Logistics (NRL), 37(1), 1990, pp. 61-84. https://doi.org/10.1002/1520-6750(199002)37:1<61::AID-NAV3220370104>3.0.CO;2-S
  4. Blazewicz, J., Lenstra, J. K., & Kan, A. R., "Scheduling subject to resource constraints: classification and complexity." Discrete applied mathematics, 5(1), 1983, pp. 11-24. https://doi.org/10.1016/0166-218X(83)90012-4
  5. Christofides, N., Alvarez-Valdes, R., & Tamarit, J. M., "Project scheduling with resource constraints: A branch and bound approach." European Journal of Operational Research, 29(3), . 1987. Pp. 262-273. https://doi.org/10.1016/0377-2217(87)90240-2
  6. Ciesielski, V., & Scerri, P., "Real time genetic scheduling of aircraft landing times." In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1998, May, pp. 360-364
  7. De Reyck, B., Demeulemeester, E., & Herroelen, W., "Local search methods for the discrete time/resource trade-off problem in project networks." Naval Research Logistics (NRL), 45(6), 1998, pp. 553-578. https://doi.org/10.1002/(SICI)1520-6750(199809)45:6<553::AID-NAV2>3.0.CO;2-1
  8. Demeulemeester, E., & Herroelen, W., "The discrete time/resource trade-off problem in project networks: a branch-andbound approach". IIE transactions, 32(11), 2000, pp. 1059-1069. https://doi.org/10.1080/07408170008967461
  9. Dewi, D. S., & Septiana, T., "Workforce scheduling considering physical and mental workload: a case study of domestic freight forwarding." Procedia Manufacturing, 4, 2015, pp. 445-453. https://doi.org/10.1016/j.promfg.2015.11.061
  10. Hong, K. S., & Leung, J. T., "On-line scheduling of real-time tasks." In Proceedings. Real-Time Systems Symposium, IEEE, 1988, December, pp. 244-250.
  11. Icmeli, O., & Erenguc, S. S., "A tabu search procedure for the resource constrained project scheduling problem with discounted cash flows." Computers & operations research, 21(8), 1994, pp. 841-853. https://doi.org/10.1016/0305-0548(94)90014-0
  12. Ortner, M., Descombes, X., & Zerubia, J. "An adaptive simulated annealing cooling schedule for object detection in images". 2007, Research Report, INRIA, Vol. 6336
  13. Jia, Q., & Seo, Y., "An improved particle swarm optimization for the resource-constrained project scheduling problem." The International Journal of Advanced Manufacturing Technology, 67(9-12), 2013a, pp. 2627-2638. https://doi.org/10.1007/s00170-012-4679-x
  14. Jia, Q., & Seo, Y., "Solving resource-constrained project scheduling problems: conceptual validation of FLP formulation and efficient permutationbased ABC computation." Computers & Operations Research, 40(8), 2013b, pp. 2037-2050. https://doi.org/10.1016/j.cor.2013.02.012
  15. Kelley, J. E., "The critical-path method: resource planning and scheduling." Industrial scheduling. 1963.
  16. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P., "Optimization by simulated annealing." science, 220(4598), 1983, pp. 671-680. https://doi.org/10.1126/science.220.4598.671
  17. Kolisch, R., Sprecher, A., & Drexl, A. "Characterization and generation of a general class of resource-constrained project scheduling problems." Management science, 41(10), 1995, pp. 1693-1703. https://doi.org/10.1287/mnsc.41.10.1693
  18. Kolisch, R., & Hartmann, S., "Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis." In Project scheduling, Springer, Boston, MA, 1999, pp. 147-178.
  19. Ranjbar, M. R., & Kianfar, F., "Solving the discrete time/resource trade-off problem in project scheduling with genetic algorithms." Applied Mathematics and Computation, 191(2), 2007, pp. 451-456. https://doi.org/10.1016/j.amc.2007.02.109
  20. Ranjbar, M., De Reyck, B., & Kianfar, F., "A hybrid scatter search for the discrete time/resource trade-off problem in project scheduling." European Journal of Operational Research, 193(1), 2009, pp. 35-48. https://doi.org/10.1016/j.ejor.2007.10.042
  21. Sprecher, A., Kolisch, R., & Drexl, "A. Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem." European Journal of Operational Research, 80(1), 1995, pp. 94-102. https://doi.org/10.1016/0377-2217(93)E0294-8