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Najafi, Amir Abbas, Arjmand, Masoud. (1395). Three Developed Meta-heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously. , 50(3), 471-482. doi: 10.22059/jieng.2016.63178
Amir Abbas Najafi; Masoud Arjmand. "Three Developed Meta-heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously". , 50, 3, 1395, 471-482. doi: 10.22059/jieng.2016.63178
Najafi, Amir Abbas, Arjmand, Masoud. (1395). 'Three Developed Meta-heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously', , 50(3), pp. 471-482. doi: 10.22059/jieng.2016.63178
Najafi, Amir Abbas, Arjmand, Masoud. Three Developed Meta-heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously. , 1395; 50(3): 471-482. doi: 10.22059/jieng.2016.63178

Three Developed Meta-heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously

Advances in Industrial Engineering
مقاله 12، دوره 50، شماره 3، اسفند 2016، صفحه 471-482    اصل مقاله (866.35 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2016.63178
نویسندگان
Amir Abbas Najafi* ؛ Masoud Arjmand
Faculty of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran
چکیده
In this paper, a bi-objective resource availability cost problem (RACP) is studied, in which the first objective function tries to minimize the completion time of the project, and the second one tries to minimize the total resource costs. Due to the problem complexity, three developed meta-heuristic algorithms, namely NSGA-II and NRGA and MOPSO, are applied to solve the model. To evaluate the algorithms, a set of tests’ problem are considered. In addition, a MADM approach called TOPSIS is employed to compare the algorithms' results. Finally, the sensitivity analysis in terms of problem’s performance is fulfilled.
کلیدواژه‌ها
MOPSO؛ Multi-objective optimization؛ NSGA-II؛ RACP؛ RCPSP
مراجع
  1. Blazewicz, J., Lenstra, J. K. and Rinnooy Kan A. H. G. (1983). “Scheduling subject to resource constraints”,  Discrete Appl Math, Vol. 1, No. 5, PP. 11–24.
  2. Paraskevopoulos, D. C., Tarantilis, C. D. and Ioannou, G. (2012). "Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm", Expert. Syst. Appl, Vol. 39, No. 4, PP. 3983-3994.
  3. Hartmann, S. and Kolisch, R. (2000). “Experimental evaluation of state-of-the-art heuristics for the resource constrained project scheduling problem”, Eur. J. Oper. Res.,Vol. 127, No. 2, PP. 394-407.
  4. Afshar-Nadjafi, B., Karimi, H., Rahimi, A. and Khalili, S. (2013). “Project scheduling with limited resources using an efficient differential evolution algorithm”, Journal of KingSaud University, In Press, Corrected Proof.
  5. Zamani, R. (2013). "A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem", Eur. J. Oper. Res., Vol. 229, No. 2,  PP. 552-559.
  6. Jairo, R., Torres, M. and Edgar, G. F. (2010). "Carolina Pirachicán-Mayorga, project scheduling with limited resources using a genetic algorithm", Int. J. project.Manage., Vol, 28, No. 6, PP. 619-628.
  7. Hartmann, S. and Briskorn, D. A. (2010). "Survey of variants and extensions of the resource constrained project scheduling problem", Eur. J. Oper. Res., Vol. 207, No. 1, PP. 1-14.
  8. Agarwal, R., Colak, S. and Erenguc, S. (2011). “A Neurogenetic approach for the resource constrained project scheduling problem”, Comput. Oper. Res., Vol. 38, No. 1, PP. 44- 50.
  9. Fang, C. and Wang, L. (2012). “An effective shuffled frog-leaping algorithm for resource constrained project scheduling problem”, Comput. Oper. Res., Vol. 39, No. 5, PP. 890-901.
  10. Koné, O. (2012). "New approaches for solving the resource constrained project scheduling problem", 4OR, Vol. 10, No. 1, PP. 105-106.
  11. Mohring, R. H. (1984). “Minimizing Costs of Resource Requirements in project Networks subject to a Fix Completion Time”, Op Research, Vol. 32, No. 1, PP. 89-120.
  12. Nubel, H. (1999). “A Branch and Bound procedure for Resource Investment problem subject to Temporal Constraints’’, Technical Report 574, University Karlsruhe, Germany.
  13. Neumann, K. and Zimmermann, J. (1997). “Resource Leveling for project with schedule dependent time windows”, Technical Report 508, Institut fur wirtschafttheorie and Operations Research, Universitat Karsruhe.
  14. Akpan, E. O. P. (1997). “Optimal Resource Determination for project scheduling”, Project Planning and Control, Vol. 8, No. 5, PP .462-468.
  15. Kimms, A. (2001). “Minimizing the Net Present Value of a project under Resource Constraints Using a lagrangian Relaxation Based Heuristic with tight upper bound”, Annual of operation Research, Vol. 102, No. 1, PP. 221-236.
  16. Najafi, A. A. and Niaki, S. T. A. (2006). “A genetic algorithm for resource investment problem with discount cash flow”, Applied Mathematical and Computation, Vol. 183, No. 2, PP. 1057-1070.
  17. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, Chichester.
  18. Shadrokh, S. and Kianfar, F. (2007). “A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty”, Eur J Oper Res, Vol. 181, No. 1, PP. 86–101.
  19. Coello-Coello, C. A., Van Veldhuizen, D. A. and Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Publishers.
  20. Kennedy, J. and Eberhart, R. C. (1995). “Particle swarm optimization”, Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ, Seoul, Korea, Vol. IV, PP. 1942-1948
  21. Hwang, C. L. and Yoon, K. (1981). Multiple attribute decision making, Springer.
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