Document Type : Original Article
Assistant Professor of Industrial Management, Department of Management, Economics and Accounting, Payame Noor University, Tehran, Iran.
Assistant Professor of Business Management, Department of Management, Economics and Accounting, Payame Noor University, Tehran, Iran.
In this research, it has been tried to optimize the efficiency of employees by considering the concept of human factor engineering in scheduling. Due to the importance of human parameters such as learning and forgetting in employees' skills, especially during job rotation, these factors have been studied and modeled in the issue of staff job rotation scheduling. For this purpose, a nonlinear integer programming model is proposed for scheduling problem of employees with two types of skills. The objective function of the model is to maximize the employee performance. Different examples are solved by considering different parameters to analyze the effects of staff costs, learning and forgetting on staff scheduling efficiency. To solve this problem, GAMZ software is used. The results showed that the proposed model has the ability to provide employee scheduling plans with the aim of maximizing employees. The computational results also indicated that learning and forgetting rate play an important role in determining the optimal scheduling plan and the use or non-use of semi-skilled workers and the movement of employees between machines. The proposed model and the results of this research help employers in using a variety of scheduling schemes and system optimization with dual constraints.
- Ağralı, S., Taşkın, Z.C., & Ünal, A.T. (2017). Employee scheduling in service industries with flexible employee availability and demand. Omega, 66, 159-169.
- Akbari, M. (2017). Mathematical Modeling of Human Factors in Dual Resourced Constraint System. Modern Researches in decision making, 2(2), 23-49.
- Akbari, M. (2017). Part-Time Workforces Scheduling with Variable Productivity. Management Research in Iran, 21(3), 25-47.
- Akbari, M., Dorri, B., & Zandie, M. (2012). Scheduling Working Shifts for Multi-skilled Workforces with Genetic algorithm Approach. Journal of industrial management perspective, 2(3), 87-102. (In Persian)
- Akbari, M., Zandieh, M., & Dorri, B. (2013). Scheduling part-time and mixed-skilled workers to maximize employee satisfaction. The International Journal of Advanced Manufacturing Technology, 64 (5-8), 1017-1027
- Álvarez, E., Ferrer, J-C., Muñoz, J.C., & Henao, C.A. (2020) Efficient shift scheduling with multiple breaks for full-time employees: A retail industry case. Computers & Industrial Engineering, 150,
- Aryanezhad, M., Kheirkhah, A., Deljoo, V., & Mirzapour Al-e-hashem, S. (2009). Designing safe job rotation schedules based upon workers’ skills. The International Journal of Advanced Manufacturing Technology, 41(1–2), 193–199.
- Ayough, A., Zandieh, M., Farsijani, H., & Dorri Nokarani B. (2014). Job Rotation Scheduling in a New Arranged Lean Cell, a Genetic Algorithm Approach. Journal of industrial management perspective, 4(3), 33-59. (In Persian)
- Azizi, N., & Liang, M. (2013). An integrated approach to worker assignment, workforce flexibility acquisition, and task rotation. Journal of the Operational Research Society, 64(2), 260–275.
- Bürgy, , Michon-Lacaze, H., & Desaulniers, G. (2019). Employee scheduling with short demand perturbations and extensible shifts. Omega, 89, 177-192.
- Givi, Z.S., Jaber, M.Y., & Neumann, W.P. (2015). Production planning in DRC systems considering worker performance. Comput. Eng., 87(1), 317–327.
- Guimarães, L. M., Anzanello, M. J., & Renner, J. S. (2012). A learning curve-based method to implement multifunctional work teams in the
- Brazilian footwear sector. Applied Ergonomics, 43(3), 541–547.
- Hopp, W. J., Tekin, E., & Van Oyen, M. P. (2004). Benefits of skill chaining in serial production lines with cross-trained workers. Management Science, 50(1), 83–98.
- Jaber, M. Y., & Kher, H. (2005). Workforce cross-training with learning in production and reworks. Paper presented at the 18th International Conference on Production Research-ICPR 18, Salerno, Italy.
- Jaber, M. Y., & Neumann, W. P. (2010). Modelling worker fatigue and recovery in dual-resource constrained systems. Computers and Industrial Engineering, 59(1), 75–84.
- Jaber, M. Y., Givi, Z. S., & Neumann, W. P. (2013). Incorporating human fatigue and recovery into the learning–forgetting process. Applied Mathematical Modelling, 37(12–13), 7287–7299.
- Jaber, M. Y., Kher, H. V., & Davis, D. J. (2003). Countering forgetting through training and deployment. International Journal of Production Economics, 85(1), 33–46.
- Kim, S., & Nembhard, D. A. (2010). Cross-trained staffing levels with heterogeneous learning/forgetting. IEEE Transactions on Engineering Management, 57(4), 560–574.
- Lei, , & Tan, X. (2016). Local search with controlled deterioration for multi-objective scheduling in dual-resource constrained flexible job shop. 28th Chinese Control and Decision Conference, Wuhan, 430070 China, 28-30 May 2016, 4921-4926.
- Li, J., & Huang, Y. (2016). A Hybrid Genetic Algorithm for Dual-Resource Constrained Job Shop Scheduling Problem. Intelligent Computing Theories and Application: 12th International Conference, ICIC 2016, Lanzhou, China
- Mattia, S., Rossi, F., Servilio, M., Smriglio, S., (2017). Staffing and scheduling flexible call centers by two-stage robust optimization. Omega, 72, 25-37.
- Nelson, R.T. (1967). Labor and machine limited production systems. Management Science 13(9), 648–671.
- Nobil, A.H., Sharifnia, S.M.E., & Cárdenas-Barrón, L.E. (2021) Mixed integer linear programming problem for personnel multi-day shift scheduling: A case study in an Iran hospital. Alexandria Engineering Journal, ISSN 1110-0168.
- Othman, M., Gouw, G. J., & Bhuiyan, N. (2012). Workforce scheduling: A new model incorporating human factors. Journal of Industrial Engineering & Management, 5(2), 259-284.
- Pasquale,V.D., Miranda, S., Iannone, R., & Riemma, S. (2016). Integration of learning and forgetting processes with the SHERPA model. IFAC-PapersOnLine, 49(12), 197-202.
- Porto, F., Henao, C.A., López-Ospina, H., & González, E.R. (2019). Hybrid flexibility strategy on personnel scheduling: Retail case study. Computers & Industrial Engineering, 133, 220-230.
- Russell, H., Maitre, B., & Watson, D. (2016). Work-Related Musculoskeletal Disorders and Stress, Anxiety and Depression in Ireland: Evidence from the QNHS 2002-2013, Economic & Social Research Institute.
- Sadrabad N., Boshrouei Shargh, S., & Mirfakhredini, S.H. (2020) Providing a Mathematical Model for Solving the Problem of Timetabling of Periodical Services. Journal of industrial management perspective, 9(4), 139-163. (In Persian)
- Soriano, J., Jalao, E. R., & Martinez, I. A. (2020). Integrated employee scheduling with known employee demand, including breaks, overtime, and employee preferences. Journal of industrial engineering and management, 13(3), 451-463.
- Taskiran, G. K., & Zhang, X. (2017). Mathematical models and solution approach for cross-training staff scheduling at call centers. Computers & Operations Research, 87, 258-269.
- Thomas, B. G., & Nembhard, D. A. (2004). Preference based search approach for scheduling workers with learning and forgetting, MSOM Sponsored Session INFORMS Ann. Meeting, Oct. 2004.
- Treleven, M. D. (1989). A review of the dual resource constrained system research. IE Transactions, 21(3), 279–287.
- Xu, J., Xu, X., & Xie, S.Q. (2011). Recent developments in Dual Resource Constrained (DRC) system research. European Journal of Operational Research, 215(2, 1), 309-318.
- Yue, H. (2005). Worker flexibility in dual resource constrained (DRC) shops. University Library Groningen [Host].
- Zamiska, J. R., Jaber, M. Y., & Kher, H. V. (2007). Worker deployment in dual resource constrained systems with a task-type factor. European Journal of Operational Research, 177(3), 1507–1519.
- Zan, J., Hasenbein, J. J., Morton, D. P., & Mehrotra, V. (2018). Staffing call centers under arrival-rate uncertainty with Bayesian updates. Operations Research Letters, 46, 379-384.