Scheduling of Bi-Objective Projects with Multi-Skilled Working Groups, Learning Effect and Work-Group Dependent Setup and Processing Times

Document Type : Research Paper

Authors

1 Ph.D. Student in Industrial Management, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

3 Associate Professor, Department of Industrial Management and Technology, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Today, in project-oriented organizations, projects are carried out in parallel and in the form of teamwork. Therefore, human resource management in project-oriented organizations plays a key role in achieving projects. In fact, the appropriate allocation of employees with different skills to work teams to carry out projects, in addition to preserving the financial resources of the organization, also reduces the time of project processing. Therefore, in this research, a multi-objective mixed integer non-linear mathematical programming (MINLP) model is presented. The mathematical model of this research includes the multiple objectives of simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flowtime of projects. Then, to validate the proposed mathematical model, several sample problems have been designed and solved using Lingo software and the constrained method. Then, a multi-objective genetic algorithm with non-dominant sorting (NSGA-II) is used to solve problems in large scales, and the efficiency of the proposed algorithm is measured by comparing with the sum weighted method. 

Keywords

Main Subjects

References

  • 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.
Journal of Industrial Engineering Research in Production Systems
Volume 13, Issue 26
September 2025
Pages 19-35

Files

Share

How to cite

Statistics