- Su, B., Xie, N., & Yang, Y. (2021). Hybrid genetic algorithm based on bin packing strategy for the unrelated parallel workgroup scheduling problem. Journal of Intelligent Manufacturing,32(4), 957-969. https://doi.org/10.1007/s10845-020-01597-8
- Pinedo, M., & Hadavi, K. (1992). Scheduling: theory, algorithms and systems development. In Operations Research Proceedings 1991: Papers of the 20th Annual Meeting/Vorträge der 20. Jahrestagung (pp. 35-42). Springer Berlin Heidelberg. DOI:1007/978-3-642-46773-8_5
- Chen, J. C., Chen, Y. Y., Chen, T. L., & Lin, Y. H. (2022). Multi-project scheduling with multi-skilled workforce assignment considering uncertainty and learning effect for large-scale equipment manufacturer. Computers & Industrial Engineering, 169, 108240. https://doi.org/10.1016/j.cie.2022.108240
- Almeida, B. F., Correia, I., & Saldanha‐da‐Gama, F. (2019). Modeling frameworks for the multi‐skill resource‐constrained project scheduling problem: a theoretical and empirical comparison. International Transactions in Operational Research, 26(3), 946-967, https://doi.org/10.1111/itor.12568
- Qin, S., Liu, S., & Kuang, H. (2016). Piecewise linear model for multiskilled workforce scheduling problems considering learning effect and project quality. Mathematical problems in Engineering, 2016(1), 3728934. https://doi.org/10.1155/2016/3728934
- Qian Li, Mengqin Jiang, Sha Tao, Jin Hao and Heap-Yih Chong (2023). The Integrated Problem of Construction Project Scheduling and Multiskilled Staff Assignment with Learning Effec. Journal of Construction Engineering and Management 149(8) DOI:10.1061/JCEMD4.COENG-13150
- Arık, O. A. (2019). Project scheduling and staff allocation problem with time-dependent learning effect: a mixed integer non-linear programming approach. Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering, 20(3), 204-215. https://doi.org/10.18038/estubtda.624291
- Haroune, M., Dhib, C., Neron, E., Soukhal, A., Mohamed Babou, H., & Nanne, M. F. (2022). Multi-project scheduling problem under shared multi-skill resource constraints. TOP, 1-42.
DOI:1007/s11750-022-00633-5
- Mika, M., Waligóra, G., & Weglarz, J. (2006). Modelling setup times in project scheduling. Perspectives in modern project scheduling, 131-163. DOI:1007/978-0-387-33768-5_6.
- Kolisch, R. (2013). Project scheduling under resource constraints: efficient heuristics for several problem classes. Springer Science & Business Media.
- Li, L., Zhang, H., & Bai, S. (2024). A multi-surrogate genetic programming hyper-heuristic algorithm for the manufacturing project scheduling problem with setup times under dynamic and interference environments. Expert Systems with Applications, 250, 123854. https://doi.org/10.1016/j.eswa.2024.123854.
- Heimerl, C., & Kolisch, R. (2010). Work assignment to and qualification of multi-skilled human resources under knowledge depreciation and company skill level targets. International Journal of Production Research, 48(13), 3759-3781. DOI: 1080/00207540902852785
- Attia, E. A., Duquenne, P., & Le-Lann, J. M. (2014). Considering skills evolutions in multi-skilled workforce allocation with flexible working hours. International Journal of Production Research, 52 (n°15), pp. 4548-4573. https://doi.org/10.1080/00207543.2013.877613
- Kia, R., Shahnazari-Shahrezaei, P., & Zabihi, S. (2016, December). Solving a multi-objective mathematical model for a multi-skilled project scheduling problem by CPLEX solver. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1220-1224). IEEE. DOI:1109/IEEM.2016.7798072
- Hosseinian, A. H., & Baradaran, V. (2020). P-GWO and MOFA: two new algorithms for the MSRCPSP with the deterioration effect and financial constraints (case study of a gas treating company). Applied Intelligence, 50, 2151-2176. https://doi.org/10.1007/s10489-020-01663-x
- Barghi, B., & Sikari, S. S,. 2022. Meta-heuristic solution with considering setup time for multi-skilled project scheduling problem. Vol. 3. Operations Research Forum: Springer. 24(10):1-17.. DOI:1007/s43069-021-00117-5
- Li, Q., Jiang, M., & Tao, Sh., Hao, J., & Chong, H.Y. (2023). The Integrated Problem of Construction Project Scheduling and Multiskilled Staff Assignment with Learning Effect. Journal of Construction Engineering and Management. 149. vol. 149, no. 8: 04023064. https://doi.org/10.1061/jcemd4.coeng-13150
- Hematian, M., Seyyed Esfahani, M.M., Mahdavi, I., Mahdavi-Amiri, N., & Rezaeian, J. .(2020). A multi-objective optimization model for multiple project scheduling and multi-skill human resource assignment problem based on learning and forgetting effect and activities' quality level. Journal of Industrial Engineering and Management Studies. Vol. 7, No. 2, 2020, pp. 98-118. https://doi.org/10.22116/jiems.2020.210566.1319
- Haroune, M., Dhib, C., Neron, E., Soukhal, A., Mohamed Babou, H., & Nanne, M. F. (2023). Multi-project scheduling problem under shared multi-skill resource constraints. Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, 31(1), pages 194-235, April http://dx.doi.org/10.1007/s11750-022-00633-5
- Biskup, D. (1999). Single-machine scheduling with learning considerations. European Journal of Operational Research, 115(1), 173-178. https://ideas.repec.org/a/eee/ejores/v115y1999i1p173-178.html
- Salehi Mir,M.S, Rezaeian,J. (2016). A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines. Applied soft computing 488-504. https://doi.org/10.1016/j.asoc.2015.12.035Get rights and content
- Yang, D. L. & Kuo, W. H. (2010). Some scheduling problems with deteriorating jobs and learning effects. Computers & Industrial Engineering, 58(1), 25-28. https://doi.org/10.1016/j.cie.2009.06.016.
- Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete applied mathematics, 5(1), 11-24. DOI:1016/0166-218X(83)90012-4
- Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2),182-197. https://doi.org/10.1109/4235.996017
- Taguchi, G. (1986). Introduction to Quality Engineering: Designing Quality into Products and Processes. Asian Productivity Organization, Tokyo.
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