- Maghsoudlou, H. M., Afshar-Nadjafi, B., and Niaki, S. T. A., (2017). “Multi-Skilled Project Scheduling with Level-Dependent Rework Risk; Three Multi-Objective Mechanisms Based on Cuckoo Search”, Applied Soft Computing, Vol. 54, PP. 46-61. DOI: https://doi.org/10.1016/j.asoc.2017.01.024.
- Nguyen, H., and Zheng, R., (2013). “On Budgeted Influence Maximization in Social Networks”, IEEE Journal on Selected Areas in Communications, Vol. 31, No. 6, PP. 1084-1094.
- Bagherinejad, J., Jolai, F., and Rafiee Majd, Z., (2013). “Solving the MRCPSP/Max with the Objective of Minimizing Tardiness Costs and Maximizing Earliness Rewards of Activities with a Two-Stage Genetic Algorithm”, Journal of Industrial Engineering, Vol. 47, No. 1, PP. 1-13.
- Panahi, I., and Nahavandi, N., (2017). “An Efficient Imperialist Competitive Algorithm for Resource Constrained Project Scheduling Problem”, Journal of Industrial Engineering, Vol. 51, No. 2, PP. 161-174.
- Bellenguez O., Néron E. (2005). “Lower Bounds for the Multi-skill Project Scheduling Problem with Hierarchical Levels of Skills”, In: Burke E., Trick M. (eds) Practice and Theory of Automated Timetabling V. PATAT 2004. Lecture Notes in Computer Science, vol 3616. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/11593577_14.
- Wu, M., and Sun, S., (2006). “A Project Scheduling and Staff Assignment Model Considering Learning Effect”, The International Journal of Advanced Manufacturing Technology, Vol. 28, No. 11, PP. 1190-1195.
- Mehmanchi, E., and Shadrokh, S., (2013). “Solving a New Mixed Integer Non-Linear Programming Model of the Multi-Skilled Project Scheduling Problem Considering Learning and Forgetting Effect”, Proceedings of the 2013 IEEE IEEM, Bangkok, Thailand, DOI: 10.1109/IEEM.2013.6962442.
- Kazemipoor, H., Tavakkoli Moghaddam, R., Shahnazari Shahrezaei, P., and Azaron, A., (2013). “A Differential Evolution Algorithm to Solve Multi-Skilled Project Portfolio Scheduling Problems”, The International Journal of Advanced Manufacturing Technology, Vol. 64, No. 5-8, PP. 1099-1111.
- Tabrizi, B. H., Tavvakoli Moghaddam, R., and Ghaderi, S. F., (2014). “A Two-Phase Method for a Multi-Skilled Project Scheduling Problem with Discounted Cash Flows”, Scientia Iranica, Vol. 21, No. 3, PP. 1083-1095.
- Myszkowski, P. B., Skowronski, M., Olech, L. P., and Oslizlo, K., (2015). “Hybrid Ant Colony Optimization in Solving Multi Skill Resource-Constrained Project Scheduling Problem”, Soft Computing, Vol. 19, No. 12, PP. 3599-3619.
- Javanmard, S., Afshar-Nadjafi, B., and Niaki, S. T. A., (2017). “Preemptive Multi-Skilled Resource Investment Project Scheduling Problem; Mathematical Modelling and Solution Approaches”, Computers and Chemical Engineering, Vol. 96, PP. 55-68. DOI: https://doi.org/10.1016/j.compchemeng.2016.11.001.
- Maghsoudlou, H., Afshar-Nadjafi, B., and Niaki, S.T.A., (2016). “A Multi-Objective Invasive Weeds Optimization Algorithm for Solving Multi-Skill Multi-Mode Resource Constrained Project Scheduling Problem”, Computers and Chemical Engineering, Vol. 8, PP. 157-169. DOI: https://doi.org/10.1016/j.compchemeng.2016.02.018.
- Chen, R., Liang, C., Gu, D., and Leung, J., (2017), “A Multi-Objective Model for Multi-Project Scheduling and Multi-Skilled Staff Assignment for IT Product Development Considering Competency Evolution”, International Journal of Production Research, Vol. 55, No. 21, PP. 6207-6234.
- Hosseinian, A.H., Baradaran, V., and Bashiri, M., (2019), “Modeling of the time-dependent multi-skilled RCPSP considering learning effect: An evolutionary solution approach”, Journal of Modelling in Management, Vol. 14, No. 2, PP. 521-558.
- Hosseinian, A.H., and Baradaran, V., (2019), “An Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the Multi-Mode Multi-Skilled Resource-constrained Project Scheduling Problem”, Journal of Optimization in Industrial Engineering, Vol. 12, No. 2, PP. 155-178.
- Hosseinian, A.H., and Baradaran, V., (2019), “Detecting communities of workforces for the multi-skill resource-constrained project scheduling problem: A dandelion solution approach”, Journal of Industrial and Systems Engineering, Vol. 12, Special issue on Project Management and Control, 72-99.
- Hartmann, S., (2013). “Project Scheduling with Resource Capacities and Requests Varying with Time: A Case Study”, Flexible Services And Manufacturing Journal, Vol. 25, No. 1, PP. 74-93.
- Pargar F., Zandieh, M., Kauppila, O., and Kujala, J., (2018). “The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop: A Bi-Objective Approach”, Journal of Systems Science and Systems Engineering, Vol. 27, No. 3, PP. 265-291.
- Najafi, A. A., and Arjmand, M., (2016). “Three Developed Meta-Heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously”, Journal of Industrial Engineering, Vol. 50, No. 3, PP. 471-482.
- Amin Tahmasbi, H., Daghbandan, A., and Bagherpour, R., (2017). “Dual-Objective Preemptive Multi-Mode Resource-Constrained Project Scheduling Problem Optimization Model”, Journal of Industrial Engineering, Vol. 51, No. 1, PP. 29-44.
- Murata, T., and Ishibuchi, H., (1995). "MOGA: Multi-Objective Genetic Algorithms", Proceedings of 1995 IEEE International Conference on Evolutionary Computation, Perth, WA, Australia, PP. 289-294.
- Gadhvi, B., Savsani, V., and Patel, V., (2016). “Multi-Objective Optimization of Vehicle Passive Suspension System Using NSGA-II, SPEA2 And PESA-II”, Procedia Technology, Vol. 23, PP. 361-368. DOI: https://doi.org/10.1016/j.protcy.2016.03.038. 23. Rahmati, S. H. A., Hajipour, V., and Niaki, S. T. A., (2013). “A Soft-Computing Pareto-Based Meta-Heuristic Algorithm for a Multi-Objective Multi-Server Facility Location Problem”, Applied Soft Computing, Vol. 13, No. 4, PP. 1728-1740.
- Gao, J., Chen, R., and Deng, W., (2013), “An Efficient Tabu Search Algorithm for the Distributed Permutation Flowshop Scheduling Problem”, International Journal of Production Research, Vol. 51, No. 3, PP. 641-651
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