Scheduling Working Shifts for Multi-skilled Workforces with Genetic algorithm Approach

Document Type : Original Article


1 Ph.D. Student, Shahid Beheshti University.

2 Professor, Shahid Beheshti University.

3 Associate Professor, Shahid Beheshti University.


Overall goal of our research is to incorporate human factors engineering into scheduling theory in order to exploit optimized human performance. Tour scheduling problem (in which full-time employees have variable performance) have been studied in this paper. Objective function of proposed staff scheduling model is minimizing staffing cost to provide efficient workforces. The unique characteristic of this study is consideration of ergonomic aspect (fatigue, learning and forgetting rate of employees) in staff scheduling problem. We used genetic algorithm to conquest difficulty of our model and to find desirable solution in a reasonable running time. In order to show effectiveness and efficiency of our algorithm we compared the results of genetic algorithm with LINGO and lower bound. The results showed that proposed model is capable to model human factors and find suitable shift schedules. Also this study showed that considered human parameters impact on workforce output and shift scheduling. Hence we recommend that managers study impacts of human factors on output of workforces and provide productive shift schedules using proposed model.


1.   Cai, X. and Li, K.N., (2000).A genetic algorithm for scheduling staff of mixed skills under multi-criteria. European Journal of Operational Research,  125(2), 359-369.
2.   Lodree Jr, E.J., Geiger, C.D., and Jiang, X., (2009).Taxonomy for integrating scheduling theory and human factors: Review and research opportunities. International Journal of Industrial Ergonomics,  39(1), 39-51.
3.   Topaloglu, S. and Ozkarahan, I., (2004).An implicit goal programming model for the tour scheduling problem considering the employee work preferences. Annals of Operations Research,  128(1-4), 135-158.
4.   Easton, F.F. and Rossin, D.F., (1991).Sufficient Working Subsets for the Tour Scheduling Problem. Management Science,  37(11), 1441-1451.
5.   Li, C., Robinson, E.P., and Mabert, V.A., (1991).An Evaluation of Tour Scheduling Heuristics with Differences in Employee Productivity and Cost. Decision Sciences,  22(4), 700-718.
6.   Brusco, M.J., Johns, T.R., and Reed, J.H., (1998).Cross-utilization of a two-skilled workforce. Int. J. Oper. & Prod. Manag.,  18(6), 555–564.
7.   Campbell, G.M., (1999).Cross-Utilization of Workers Whose Capabilities Differ. Management Science,  45(5), 722-732.
8.   Thompson, G.M. and Goodale, J.C., (2006).Variable employee productivity in workforce scheduling. European Journal of Operational Research,  170(2), 376-390.
9.   Guastello, S.J., (2006)  Human factors engineering and ergonomics: a systems approach: Lawrence Erlbaum Associates.
10. Niebel, B.W., (1962)  Motion and time study: an introduction to methods, time study, and wage payment: Richard D. Irwin.
11. Carnahan, B.J., Norman, B.A., and Redfern, M.S., (2001).Incorporating physical demand criteria into assembly line balancing. Iie Transactions,  33(10), 875-887.
12. Michalos, G., Makris, S., Rentzos, L., and Chryssolouris, G., (2010).Dynamic job rotation for workload balancing in human based assembly systems. CIRP Journal of Manufacturing Science and Technology,  2(3), 153-160.
13. Nembhard, D.A. and Uzumeri, M.V., (2000).An Individual-Based Description of Learning within an Organization. IEEE Transactions on Engineering Management,  47(3), 370.
14. Azizi, N., Zolfaghari, S., and Liang, M., (2010).Modeling job rotation in manufacturing systems: The study of employee’s boredom and skill variations. Int. J. ProductionEconomics,  123, 69–85.
15. YAN, J.H. and WANG, Z.M., (2011) GA Based Algorithm for Staff Scheduling Considering Learning-forgetting Effect, in Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on Changchun 122 - 126.
16. Easton, F.F., (2011).Cross-training performance in flexible labor scheduling environments. Iie Transactions,  43(8), 589-603.
17. F. Easton and Mansour, N., (1999).Distributed Genetic Algorithm for Deterministic and Stochastic Labor Scheduling Problems. Eur. J. Oper. Res., 118(3), 505–523.
18. J. Tanomaru. (1995). Staff Scheduling by a Genetic Algorithm with Heuristic Operators. in In Proceedings of the 1995 IEEE International Conference on Evolutionary Computation.  Perth, Australia.
19. F. Easton and Mansour, N. (1993). A Distributed Genetic Algorithm for Employee Scheduling Problems. in In S. Forest (ed.), Genetic Algorithms: Proceedings of the 5th International Conference.  San Mateo, CA: Morgan Kaufmann.
20. Diego-Mas, J.A., Asensio-Cuesta, S., Sanchez-Romero, M.A., and Artacho-Ramirez, M.A., (2009).A multi-criteria genetic algorithm for the generation of job rotation schedules. International Journal of Industrial Ergonomics,  39(1), 23-33.