Multi-skilled workforce scheduling with training and welfare considerations

• Autorzy: Peña Diana A. , Osorio Andrés F. , Orejuela Juan P. , Idarraga Juan C.

Identyfikatory

DOI

10.2478/emj-2023-0018

• Języki publikacji: EN

Abstrakty

EN

Flexibility in workforce scheduling in services is necessary to reduce the impact of demand uncertainty, absenteeism, and desertion while maintaining high service levels. This paper studies the workforce scheduling problem, including multiple skill accumulation, training, and welfare, as well as flexibility for employees and the company. All these elements are modelled and included in a mixed-integer linear programming (MILP) model that maximises their accumulated skill level. A real case study based on the scheduling of lab assistants to laboratory practices at a university in Colombia is used to generate numerical experiments. Different experiments were conducted, and the results show that the level of skill achieved is highly sensitive to the number of assistants and the number of allocations. The experiments also showed that, while keeping the same number of lab assistants, it is possible to include flexibility and welfare constraints. Finally, the proposed model can generate schedules that achieve high levels of skills and meet the different constraints of the model, including balance, accumulation, demand and welfare.

Słowa kluczowe

EN

flexibility; multiskilling; workforce scheduling; training; optimisation

PL

elastyczność; wykwalifikowana siła robocza; planowanie pracowników; szkolenie; optymalizacja

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2023
• Tom: Vol. 15, no. 3
• Strony: 27--41
• Opis fizyczny: Bibliogr. 36 poz., tab., wykr.

Twórcy

autor

Peña Diana A.

• Universidad Icesi, Colombia

• https://orcid.org/0000-0001-6309-724X

autor

Osorio Andrés F.

• Universidad Icesi, Colombia

• https://orcid.org/0000-0003-2728-5432

autor

Orejuela Juan P.

• Escuela de Ingeniería Industrial, Universidad del Valle, Colombia

• https://orcid.org/0000-0003-2187-0630

autor

Idarraga Juan C.

• Universidad Icesi, Colombia

• https://orcid.org/0000-0002-5848-7662

Bibliografia

• Abdoul Soukour, A., Devendeville, L., Lucet, C., & Moukrim, A. (2013). A Memetic Algorithm for staff scheduling problem in airport security service. Expert Systems with Applications, 40(18), 7504-7512. doi: 10.1016/j.eswa.2013.06.073
• Ağralı, S., Taşkın, Z. C., & Ünal, A. T. (2017). Employee scheduling in service industries with flexible employee availability and demand. Omega (United Kingdom), 66, 159-169. doi: 10.1016/j.omega.2016.03.001
• 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), 1017-1027. doi: 10.1007/s00170-012-4032-4
• Al-Yakoob, S. M., & Sherali, H. D. (2006). Mathematical programming models and algorithms for a class–faculty assignment problem. European Journal of Operational Research, 173(2), 488-507. doi: 10.1016/j.ejor.2005.01.052
• Al-Yakoob, S. M., & Sherali, H. D. (2007). Mixed-integer programming models for an employee scheduling problem with multiple shifts and work locations. Annals of Operations Research, 155(1), 119-142. doi: 10.1007/s10479-007-0210-4
• Al-Yakoob, S., & Sherali, H. (2007a). Multiple Shift Scheduling of Hierarchical Workforce with Multiple Work Centers. Informatica, Lith. Acad. Sci., 18, 325-342. doi: 10.15388/Informatica.2007.180
• Al-Yakoob, S., & Sherali, H. (2008). A column generation approach for an employee scheduling problem with multiple shifts and work locations. Journal of the Operational Research Society, 59, 34-43. doi: 10.1057/palgrave.jors.2602294
• Bentefouet, F., & Nembhard, D. A. (2013). Optimal flow-line conditions with worker variability. International Journal of Production Economics, 141(2), 675-684. doi: 10.1016/j.ijpe.2012.10.008
• Cuevas, R., Ferrer, J. C., Klapp, M., & Muñoz, J. C. (2016). A mixed integer programming approach to multi-skilled workforce scheduling. Journal of Scheduling, 19(1), 91-106. doi: 10.1007/s10951-015-0450-0
• Dantzig, G. (1954). Letter to the Editor - A Comment on Edie’s “Traffic Delays at Toll Booths.” Oper. Res., 2, 339-341.
• De Bruecker, P., Beliën, J., Van den Bergh, J., & Demeulemeester, E. (2018). A three-stage mixed integer programming approach for optimizing the skill mix and training schedules for aircraft maintenance. European Journal of Operational Research, 267(2), 439-452. doi: 10.1016/j.ejor.2017.11.047
• Edie, L. C. (1954). Traffic Delays at Toll Booths. Oper. Res., 2, 107-138.
• Goodale, J. C., & Thompson, G. M. (2004). A Comparison of Heuristics for Assigning Individual Employees to Labor Tour Schedules. Annals of Operations Research, 128(1), 47-63. doi: 10.1023/B:ANOR.0000019098.97205.cc
• Heimerl, C., & Kolisch, R. (2010). Scheduling and staffing multiple projects with a multi-skilled workforce. OR Spectrum, 32(2), 343-368. doi: 10.1007/s00291-009-0169-4
• Henao, C. A., Ferrer, J., Carlos, Muñoz, J. C., & Vera, J. (2016). Multiskilling with closed chains in a service industry: A robust optimization approach. International Journal of Production Economics, 179, 166-178. doi: 10.1016/j.ijpe.2016.06.013
• Henao, C. A., Munoz, J. C., & Ferrer, J. C. (2015). The impact of multi-skilling on personnel scheduling in the service sector: A retail industry case. Journal of the Operational Research Society, 66(12), 1949-1959. doi: 10.1057/jors.2015.9
• Houghton, E., & Portougal, V. (2005). A model for labour pooling in batch manufacturing. International Journal of Production Research, 43, 671-685. doi: 10.1080/00207540410001704023
• Huq, F., Cutright, K., & Martin, C. (2004). Employee scheduling and makespan minimization in a flow shop with multi-processor work stations: a case study. Omega, 32(2), 121-129. doi: 10.1016/j.omega.2003.09.014
• Jarray, F. (2009). A 4-day or a 3-day workweeks scheduling problem with a given workforce size. Asia-Pacific Journal of Operational Research (APJOR), 26, 685- 696. doi: 10.1142/S0217595909002419
• Kaplansky, E., & Meisels, A. (2007). Distributed personnel scheduling – negotiation among scheduling agents. Annals OR, 155, 227-255. doi: 10.1007/s10479-007-0206-0
• Karam, A., Attia, E.-A., & Duquenne, P. (2017). A MILP model for an integrated project scheduling and multi-skilled workforce allocation with flexible working hours. IFAC-PapersOnLine, 50(1), 13964- 13969. doi: 10.1016/j.ifacol.2017.08.2221
• Kim, S., & Nembhard, D. A. (2013). Rule mining for scheduling cross training with a heterogeneous workforce. International Journal of Production Research, 51(8), 2281-2300. doi: 10.1080/00207543.2012.716169
• Mac-Vicar, M., Ferrer, J. C., Muñoz, J. C., & Henao, C. A. (2017). Real-time recovering strategies on personnel scheduling in the retail industry. Computers & Industrial Engineering, 113, 589-601. doi: 10.1016/j.cie.2017.09.045
• Montoya-Torres, C. A. M.-C. and J. R. (2016). Sustainability in Production Systems: A Review of Optimization Methods Studying Social Responsibility Issues in Workforce Scheduling. Studies in Computational Intelligence, 640, 115-123. doi: 10.1007/978-3-319-30337-6
• Ni, H., & Abeledo, H. (2007). A branch-and-price approach for large-scale employee tour scheduling problems. Annals of Operations Research, 155(1), 167-176. doi: 10.1007/s10479-007-0212-2
• Orejuela, J. P., Peña, D., & Bustamante, N. (2014). Modeling the labor scheduling problem considering wellbeing for the clinic’s employees. Ingeniería y Competitividad, 16(1), 11-21.
• Örmeci, E. L., Salman, F. S., & Yücel, E. (2014). Staff rostering in call centers providing employee transportation. Omega, 43, 41-53. doi: 10.1016/j.omega.2013.06.003
• Petrovic, S., & Vanden Berghe, G. (2012). A comparison of two approaches to nurse rostering problems. Annals OR, 194, 365-384. doi: 10.1007/s10479-010-0808-9
• Rocha, M., Oliveira, J., & Carravilla, M. (2014). A constructive heuristic for staff scheduling in the glass industry. Annals of Operations Research, 217, 463-478. doi: 10.1007/s10479-013-1525-y
• Rocha, M., Oliveira, J. F., & Carravilla, M. A. (2013). Cyclic staff scheduling: optimization models for some real-life problems. Journal of Scheduling, 16(2), 231-242. doi: 10.1007/s10951-012-0299-4
• Rojas, L., Tapia, S., & Jensen, M. (2008). Asignación de supervisores forestales: resolución mediante un algoritmo tabu search. Ingeniare. Revista Chilena de Ingeniería, 16. doi: 10.4067/S0718-33052008000300003
• Rong, A., & Grunow, M. (2009). Shift designs for freight handling personnel at air cargo terminals. Transportation Research Part E: Logistics and Transportation Review, 45(5), 725-739. doi: 10.1016/j.tre.2009.01.005
• Ulusam Seçkiner, S., Gökçen, H., & Kurt, M. (2007). An integer programming model for hierarchical workforce scheduling problem. European Journal of Operational Research, 183(2), 694-699. doi: 10.1016/j.ejor.2006.10.030
• van der Veen, E., Hans, E. W., Veltman, B., Berrevoets, L. M., & Berden, H. J. J. M. (2015). A case study of cost-efficient staffing under annualized hours. Health Care Management Science, 18(3), 279-288. doi: 10.1007/s10729-014-9292-0
• Yan, S., Yang, T.-H., & Chen, H.-H. (2004). Airline short-term maintenance manpower supply planning. Transportation Research Part A: Policy and Practice, 38(9), 615-642. doi: 10.1016/j.tra.2004.03.005
• Zabihi, S., Rashidi Kahag, M., Maghsoudlou, H., & Afshar-Nadjafi, B. (2019). Multi-objective teaching-learning-based meta-heuristic algorithms to solve multi-skilled project scheduling problem. Computers & Industrial Engineering, 136, 195-211. doi: 10.1016/j.cie.2019.07.032