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How to cite this paper
APA: Eshraghi, A. (2016). A new approach for solving resource constrained project scheduling problems using differential evolution algorithm. International Journal of Industrial Engineering Computations, 7(2), 205-216.
Chicago/Turabian: Eshraghi, A. 2016. "A new approach for solving resource constrained project scheduling problems using differential evolution algorithm." International Journal of Industrial Engineering Computations 7, no. 2 (2016): 205-216.
AMA: Eshraghi, A. A new approach for solving resource constrained project scheduling problems using differential evolution algorithm. International Journal of Industrial Engineering Computations. 2016;7(2):205-216.
References
Akbari, R., Zeighami, V., & Ziarati, K. (2011). Artificial bee colony for resource constrained project scheduling problem. International Journal of Industrial Engineering Computations, 2(1), 45-60.
Akpan, E. O. (1997). Optimum resource determination for project scheduling. Production Planning & Control, 8(5), 462-468.
Amiri, M., & Barbin, J. P. (2015). New approach for solving software project scheduling problem using differential evolution algorithm.
Arjmand, M., & Najafi, A. A. (2015). Solving a multi-mode bi-objective resource investment problem using meta-heuristic algorithms. 1, 41-58.
Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete Applied Mathematics, 5(1), 11-24.
Bilolikar, V. S., Fr, C. R., & Jain, M. K. (2014). An adaptive crossover genetic algorithm for multi-mode resource constrained project scheduling with discounted cash flows. 1-9. Proceeding.
Biju, A. C., Victoire, T., & Mohanasundaram, K. (2015). An Improved Differential Evolution Solution for Software Project Scheduling Problem. The Scientific World Journal, Article ID 232193.
Hsu, C. C., & Kim, D. S. (2005). A new heuristic for the multi-mode resource investment problem. Journal of the Operational Research Society, 56(4), 406-413.
Demeulemeester, E., & Herroelen, W. (1992). A branch-and-bound procedure for the multiple resource-constrained project scheduling problem. Management science, 38(12), 1803-1818.
Deng, C., Dong, X., Yang, Y., Tan, Y., & Tan, X. (2015). Differential Evolution with Novel Local Search Operation for Large Scale Optimization Problems. In Advances in Swarm and Computational Intelligence (pp. 317-325). Springer International Publishing.
Demeulemeester, E. (1995). Minimizing resource availability costs in time-limited project networks. Management Science, 41(10), 1590-1598.
Drexl, A., & Kimms, A. (2001). Optimization guided lower and upper bounds for the resource investment problem. Journal of the Operational Research Society, 340-351.
Hartmann, S., & Kolisch, R. (2000). Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 127(2), 394-407.
Jia, Q., & Seo, Y. (2013). Solving resource-constrained project scheduling problems: conceptual validation of FLP formulation and efficient permutation-based ABC computation. Computers & Operations Research, 40(8), 2037-2050.
Klimek, M. (2011, January). A genetic algorithm for the project scheduling with the resource constraints. In Annales UMCS, Informatica (Vol. 10, No. 1, pp. 117-130).
Kolisch, R. (1996). Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90(2), 320-333.
Kolisch, R., & Hartmann, S. (1999). Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis (pp. 147-178). Springer US.
Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European journal of operational research, 174(1), 23-37.
Kolisch, R., & Padman, R. (2001). An integrated survey of deterministic project scheduling. Omega, 29(3), 249-272.
Koulinas, G., Kotsikas, L., & Anagnostopoulos, K. (2014). A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Information Sciences, 277, 680-693.
Leung, Y. W., & Wang, Y. (2001). An orthogonal genetic algorithm with quantization for global numerical optimization. Evolutionary Computation, IEEE Transactions on, 5(1), 41-53.
Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. Evolutionary Computation, IEEE Transactions on, 6(4), 333-346.
M?hring, R. H. (1984). Minimizing costs of resource requirements in project networks subject to a fixed completion time. Operations Research, 32(1), 89-120.#
Najafi, A. A., & Niaki, S. T. A. (2006). A genetic algorithm for resource investment problem with discounted cash flows. Applied Mathematics and Computation, 183(2), 1057-1070.
Nübel, H. (1999). A branch and bound procedure for the resource investment problem subject to temporal constraints. Inst. für Wirtschaftstheorie und Operations-Research.
Nübel, H. (2001). The resource renting problem subject to temporal constraints. OR-Spektrum, 23(3), 359-381.
Rahmani, N., Zeighami, V., & Akbari, R. (2015). A study on the performance of differential search algorithm for single mode resource constrained project scheduling problem. Decision Science Letters, 4(4), 537-550.
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. (2008). Opposition versus randomness in soft computing techniques. Applied Soft Computing, 8(2), 906-918.
Storn, R., & Price, K. V. (1996, May). Minimizing the Real Functions of the ICEC & apos; 96 Contest by Differential Evolution. In International Conference on Evolutionary Computation (pp. 842-844).
Shadrokh, S., & Kianfar, F. (2007). A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty. European Journal of Operational Research, 181(1), 86-101.
Wang, H., Rahnamayan, S., & Wu, Z. (2013). Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. Journal of Parallel and Distributed Computing, 73(1), 62-73.
Wang, Y., Cai, Z., & Zhang, Q. (2012). Enhancing the search ability of differential evolution through orthogonal crossover. Information Sciences, 185(1), 153-177.
Xiao, L., Tian, J., & Liu, Z. (2014, June). An Activity-List based Nested Partitions algorithm for Resource-Constrained Project Scheduling. In Intelligent Control and Automation (WCICA), 2014 11th World Congress on (pp. 3450-3454). IEEE.
Yang, B., Geunes, J., & O’brien, W. J. (2001). Resource-constrained project scheduling: Past work and new directions. Department of Industrial and Systems Engineering, University of Florida, Tech. Rep.
Zhang, Q., & Leung, Y. W. (1999). An orthogonal genetic algorithm for multimedia multicast routing. Evolutionary Computation, IEEE Transactions on, 3(1), 53-62.
Ziarati, K., Akbari, R., & Zeighami, V. (2011). On the performance of bee algorithms for resource-constrained project scheduling problem. Applied Soft Computing, 11(4), 3720-3733.
Zimmermann, J., & Engelhardt, H. (1998). Lower bounds and exact algorithms for resource leveling problems. Report WIOR-517, University Karlsruhe.
Akpan, E. O. (1997). Optimum resource determination for project scheduling. Production Planning & Control, 8(5), 462-468.
Amiri, M., & Barbin, J. P. (2015). New approach for solving software project scheduling problem using differential evolution algorithm.
Arjmand, M., & Najafi, A. A. (2015). Solving a multi-mode bi-objective resource investment problem using meta-heuristic algorithms. 1, 41-58.
Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete Applied Mathematics, 5(1), 11-24.
Bilolikar, V. S., Fr, C. R., & Jain, M. K. (2014). An adaptive crossover genetic algorithm for multi-mode resource constrained project scheduling with discounted cash flows. 1-9. Proceeding.
Biju, A. C., Victoire, T., & Mohanasundaram, K. (2015). An Improved Differential Evolution Solution for Software Project Scheduling Problem. The Scientific World Journal, Article ID 232193.
Hsu, C. C., & Kim, D. S. (2005). A new heuristic for the multi-mode resource investment problem. Journal of the Operational Research Society, 56(4), 406-413.
Demeulemeester, E., & Herroelen, W. (1992). A branch-and-bound procedure for the multiple resource-constrained project scheduling problem. Management science, 38(12), 1803-1818.
Deng, C., Dong, X., Yang, Y., Tan, Y., & Tan, X. (2015). Differential Evolution with Novel Local Search Operation for Large Scale Optimization Problems. In Advances in Swarm and Computational Intelligence (pp. 317-325). Springer International Publishing.
Demeulemeester, E. (1995). Minimizing resource availability costs in time-limited project networks. Management Science, 41(10), 1590-1598.
Drexl, A., & Kimms, A. (2001). Optimization guided lower and upper bounds for the resource investment problem. Journal of the Operational Research Society, 340-351.
Hartmann, S., & Kolisch, R. (2000). Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 127(2), 394-407.
Jia, Q., & Seo, Y. (2013). Solving resource-constrained project scheduling problems: conceptual validation of FLP formulation and efficient permutation-based ABC computation. Computers & Operations Research, 40(8), 2037-2050.
Klimek, M. (2011, January). A genetic algorithm for the project scheduling with the resource constraints. In Annales UMCS, Informatica (Vol. 10, No. 1, pp. 117-130).
Kolisch, R. (1996). Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90(2), 320-333.
Kolisch, R., & Hartmann, S. (1999). Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis (pp. 147-178). Springer US.
Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European journal of operational research, 174(1), 23-37.
Kolisch, R., & Padman, R. (2001). An integrated survey of deterministic project scheduling. Omega, 29(3), 249-272.
Koulinas, G., Kotsikas, L., & Anagnostopoulos, K. (2014). A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Information Sciences, 277, 680-693.
Leung, Y. W., & Wang, Y. (2001). An orthogonal genetic algorithm with quantization for global numerical optimization. Evolutionary Computation, IEEE Transactions on, 5(1), 41-53.
Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. Evolutionary Computation, IEEE Transactions on, 6(4), 333-346.
M?hring, R. H. (1984). Minimizing costs of resource requirements in project networks subject to a fixed completion time. Operations Research, 32(1), 89-120.#
Najafi, A. A., & Niaki, S. T. A. (2006). A genetic algorithm for resource investment problem with discounted cash flows. Applied Mathematics and Computation, 183(2), 1057-1070.
Nübel, H. (1999). A branch and bound procedure for the resource investment problem subject to temporal constraints. Inst. für Wirtschaftstheorie und Operations-Research.
Nübel, H. (2001). The resource renting problem subject to temporal constraints. OR-Spektrum, 23(3), 359-381.
Rahmani, N., Zeighami, V., & Akbari, R. (2015). A study on the performance of differential search algorithm for single mode resource constrained project scheduling problem. Decision Science Letters, 4(4), 537-550.
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. (2008). Opposition versus randomness in soft computing techniques. Applied Soft Computing, 8(2), 906-918.
Storn, R., & Price, K. V. (1996, May). Minimizing the Real Functions of the ICEC & apos; 96 Contest by Differential Evolution. In International Conference on Evolutionary Computation (pp. 842-844).
Shadrokh, S., & Kianfar, F. (2007). A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty. European Journal of Operational Research, 181(1), 86-101.
Wang, H., Rahnamayan, S., & Wu, Z. (2013). Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. Journal of Parallel and Distributed Computing, 73(1), 62-73.
Wang, Y., Cai, Z., & Zhang, Q. (2012). Enhancing the search ability of differential evolution through orthogonal crossover. Information Sciences, 185(1), 153-177.
Xiao, L., Tian, J., & Liu, Z. (2014, June). An Activity-List based Nested Partitions algorithm for Resource-Constrained Project Scheduling. In Intelligent Control and Automation (WCICA), 2014 11th World Congress on (pp. 3450-3454). IEEE.
Yang, B., Geunes, J., & O’brien, W. J. (2001). Resource-constrained project scheduling: Past work and new directions. Department of Industrial and Systems Engineering, University of Florida, Tech. Rep.
Zhang, Q., & Leung, Y. W. (1999). An orthogonal genetic algorithm for multimedia multicast routing. Evolutionary Computation, IEEE Transactions on, 3(1), 53-62.
Ziarati, K., Akbari, R., & Zeighami, V. (2011). On the performance of bee algorithms for resource-constrained project scheduling problem. Applied Soft Computing, 11(4), 3720-3733.
Zimmermann, J., & Engelhardt, H. (1998). Lower bounds and exact algorithms for resource leveling problems. Report WIOR-517, University Karlsruhe.