[1] Bineshian, M., Safari, S., Abbasi, R., Momeni, M., (1397), Optimization of organization portfolio; clustering approach and fuzzy multi-criteria decision making, Modern Researches in decision making, 3(2), 81-106.
[2] Hartmann, S., Briskorn, D. (2010), A survey of variants and extensions of the resource-constrained project scheduling problem, European Journal of Operational Research, 207(1), 1–14.
[3] Wiest, J.D. (1963), The scheduling of large projects with limited resources, PhD dissertation, Carnegie Institute of Technology, 10–15.
[4] Fahmy, A., Hassan, T.M., Bassioni, H. (2014), Improving RCPSP solutions quality with Stacking Justification – Application with particle swarm optimization, Expert Systems with Applications, 41(13), 5870–5881.
[5] Jiang, G., Shi, J. (2005), Exact algorithm for solving project scheduling problems under multiple resource constraints, Journal of Construction Engineering and Management, 131(9), 986–992.
[6] Zhang, H., Li, H., Tam, C.M. (2006), Particle swarm optimization for resource-constrained project scheduling, International Journal of Project Management, 24, 83-92.
[7] Damak, J., Jarboui, B., Siarry, P., Loukil, T., (2009), Differential evolution for solving multi-mode resource-constrained project scheduling problems, Computers and Operations Research., 36, 2653–2659.
[8] Wu, L., Wang, Y., Zhou, S. (2010), Improved differential evolution algorithm for resource-constrained project scheduling problem, Journal of Systems Engineering and Electronics, 21, 798-805.
[9] Chen, R.M., Wu, C.L., Wang, C.M., Lo, S.T. (2010), Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Systems with Applications, 37, 1899-1910.
[10] Liu, Y.C., Gao, H.M., Yang, S.M., Chuang, C.Y. (2014), Application of genetic algorithm and fuzzy Gant chart to project scheduling with resource constraints, Intelligent Computing Methodologies, 8589, 241-252.
[11] Yan, R., Li, W., Jiang, P., Zhou, Y., Wu, G. (2014), A modified differential evolution algorithm for resource constrained multi-project scheduling Problem, Journal of Computers, 9, 1922-1927.
[12] Fahimy, A., Hassan, T. M., Bassioni, H., (2014), Improving RCPSP solutions quality with stacking justification–Application with particle swarm optimization, Expert Systems with Applications, 41(13), 5870–5881.
[13] Kumar, N., Vidyarthi, D. P. (2015), A model for resource-constrained project scheduling using adaptive PSO, Soft Computing, 19, 1-16.
[14] Kadri, R.L., Boctor, F.F., (2017), An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case, European Journal of Operational Research, 265(2), 454-462.
[15] Ghafoori, S., Taghizadeh.Y, MR., (2017), Proposing a multi-objective mathematical model for RCPSP and solving It with firefly and simulated annealing algorithms, Modern Researches in Decision Making, 1(4), 117 142.
[16] Karimi, N., Zandieh, M., Karamooz, H.R. (2010), Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach, Expert Systems with Applications, 37(6), 4024–4032.
[1] Bineshian, M., Safari, S., Abbasi, R., Momeni, M., (1397), Optimization of organization portfolio; clustering approach and fuzzy multi-criteria decision making, Modern Researches in decision making, 3(2), 81-106.
[2] Hartmann, S., Briskorn, D. (2010), A survey of variants and extensions of the resource-constrained project scheduling problem, European Journal of Operational Research, 207(1), 1–14.
[3] Wiest, J.D. (1963), The scheduling of large projects with limited resources, PhD dissertation, Carnegie Institute of Technology, 10–15.
[4] Fahmy, A., Hassan, T.M., Bassioni, H. (2014), Improving RCPSP solutions quality with Stacking Justification – Application with particle swarm optimization, Expert Systems with Applications, 41(13), 5870–5881.
[5] Jiang, G., Shi, J. (2005), Exact algorithm for solving project scheduling problems under multiple resource constraints, Journal of Construction Engineering and Management, 131(9), 986–992.
[6] Zhang, H., Li, H., Tam, C.M. (2006), Particle swarm optimization for resource-constrained project scheduling, International Journal of Project Management, 24, 83-92.
[7] Damak, J., Jarboui, B., Siarry, P., Loukil, T., (2009), Differential evolution for solving multi-mode resource-constrained project scheduling problems, Computers and Operations Research., 36, 2653–2659.
[8] Wu, L., Wang, Y., Zhou, S. (2010), Improved differential evolution algorithm for resource-constrained project scheduling problem, Journal of Systems Engineering and Electronics, 21, 798-805.
[9] Chen, R.M., Wu, C.L., Wang, C.M., Lo, S.T. (2010), Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Systems with Applications, 37, 1899-1910.
[10] Liu, Y.C., Gao, H.M., Yang, S.M., Chuang, C.Y. (2014), Application of genetic algorithm and fuzzy Gant chart to project scheduling with resource constraints, Intelligent Computing Methodologies, 8589, 241-252.
[11] Yan, R., Li, W., Jiang, P., Zhou, Y., Wu, G. (2014), A modified differential evolution algorithm for resource constrained multi-project scheduling Problem, Journal of Computers, 9, 1922-1927.
[12] Fahimy, A., Hassan, T. M., Bassioni, H., (2014), Improving RCPSP solutions quality with stacking justification–Application with particle swarm optimization, Expert Systems with Applications, 41(13), 5870–5881.
[13] Kumar, N., Vidyarthi, D. P. (2015), A model for resource-constrained project scheduling using adaptive PSO, Soft Computing, 19, 1-16.
[14] Kadri, R.L., Boctor, F.F., (2017), An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case, European Journal of Operational Research, 265(2), 454-462.
[15] Ghafoori, S., Taghizadeh.Y, MR., (2017), Proposing a multi-objective mathematical model for RCPSP and solving It with firefly and simulated annealing algorithms, Modern Researches in Decision Making, 1(4), 117 142.
[16] Karimi, N., Zandieh, M., Karamooz, H.R. (2010), Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach, Expert Systems with Applications, 37(6), 4024–4032.
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