Multi-objective Solution Approaches for Employee Shift Scheduling Problems in Service Sectors (RESEARCH NOTE)

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Graduate School of Sciences, Eskişehir Technical University, Eskisehir, Turkey

2 Department of Industrial Engineering, Engineering Faculty, Eskişehir Technical University, Eskisehir, Turkey

Abstract

Today, workforce scheduling programs are being implemented in many production and service centers. These sectors can provide better quality products and/or services to their customers, taking into account employees’ desires and preferences in order to increase sector productivity. In this study, an employee shift scheduling problem in the service sector is discussed. In the problem, the aim is to minimize the total amount of workloads of the employees and to provide the preferences of the employees. Under this multi-objective structure, by taking into account the needs of employees, a multi-objective decision model has been developed. After then, a multi-criteria decision-making model has been developed to obtain the weights/priorities of the objective functions. By the help of these obtained weights, the problem is scalarized by the Weighted Sum Scalarization (WSS) and Conic Scalarization (CS) methods. When Pareto solutions are compared, it is seen that more Pareto solutions are obtained with CS method. Additionally, better schedules have been obtained in a very short time in terms of the quality of the solution according to the manually prepared schedule.

Keywords

Main Subjects


 
1. Cheraghalipour, A., Paydar, M.M. and Hajiaghaei-Keshteli, M.,
“An integrated approach for collection center selection in reverse
logistics”, International Journal of Engineering - Transactions
A: Basics, Vol. 30, No. 7, (2017), 1005–1016.  
2. Markowitz, H., “Portfolio selection”, The Journal of Finance,
Vol. 7, No. 1, (1952), 77–91.  
3. Saborido, R., Ruiz, A.B., Bermúdez, J.D., Vercher, E. and Luque,
M., “Evolutionary multi-objective optimization algorithms for
fuzzy portfolio selection”, Applied Soft Computing, Vol. 39,
(2016), 48–63.  
4. Bermúdez, J.D., Segura, J.V., and Vercher, E., “A multi-objective
genetic algorithm for cardinality constrained fuzzy portfolio 
selection”, Fuzzy Sets and Systems, Vol. 188, No. 1, (2012), 16–
26. 
5. Vercher, E. and Bermudez, J.D., “A Possibilistic Mean-Downside 
Risk-Skewness Model for Efficient Portfolio Selection”, IEEE
Transactions on Fuzzy Systems, Vol. 21, No. 3, (2013), 585–595.  
6. Kaviyani-Charati, M., Heidarzadeh Souraki, F. and HajiaghaeiKeshteli,
M.,
“A
Robust
Optimization
Methodology
for Multiobjective
Location-transportation
Problem
in Disaster Response
Phase under Uncertainty”, International Journal of Engineering
- Transactions B: Applications, Vol. 31, No. 11, (2018), 1953–
1961.  
7. Li, X., Qin, Z., and Kar, S., “Mean-variance-skewness model for
portfolio selection with fuzzy returns”, European Journal of
Operational Research, Vol. 202, No. 1, (2010), 239–247.  
8. Vercher, E. and Bermúdez, J.D., “Portfolio optimization using a
credibility mean-absolute semi-deviation model”, Expert Systems
with Applications, Vol. 42, No. 20, (2015), 7121–7131.  9. Wang, S. and Xia, Y., Portfolio Selection and Asset Pricing,
Springer Berlin Heidelberg, Vol. 514, (2002). 
10. Bermudez, J.D., Segura, J.V., and Vercher, E., “A fuzzy ranking
strategy for portfolio selection applied to the Spanish stock
market”, 2007 IEEE International Fuzzy Systems Conference,
(2007), 1–4.  
11. Harvey, C.R., Liechty, J.C., Liechty, M.W. and Müller, P.,
“Portfolio selection with higher moments”, Quantitative
Finance, Vol. 10, No. 5, (2010), 469–485.  
12. Cao, J.L., “Algorithm research based on multi period fuzzy
portfolio optimization model”, Cluster Computing, (2018), 1–8.  
13. Liu, S., Wang, S. Y., and Qiu, W., “Mean-variance-skewness
model for portfolio selection with transaction costs”,
International Journal of Systems Science, Vol. 34, No. 4,
(2003), 255–262.  
14. Gupta, P., Inuiguchi, M., Mehlawat, M.K. and Mittal, G.,
“Multiobjective credibilistic portfolio selection model with fuzzy
chance-constraints”, Information Sciences, Vol. 229, (2013), 1–
17.  
 
15. Mokhtarian Asl, M. and Sattarvand, J., “Integration of commodity
price uncertainty in long-term open pit mine production planning
by using an imperialist competitive algorithm”, Journal of the
Southern African Institute of Mining and Metallurgy, Vol. 118,
No. 2, (2018), 165–172.  
16. Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S. and Luo, J., “New
Frontiers in Applied Data Mining”, PAKDD: Pacific-Asia
Conference on Knowledge Discovery and Data Mining, PAKDD
2011 International Workshops, Shenzhen, China, (2012). 
17. Barbati, M., Greco, S., Kadziński, M. and Słowiński, R.,
“Optimization of multiple satisfaction levels in portfolio decision
analysis”, Omega, Vol. 78, (2018), 192–204.  
18. Liagkouras, K. and Metaxiotis, K., “A new efficiently encoded
multiobjective algorithm for the solution of the cardinality
constrained portfolio optimization problem”, Annals of
Operations Research, Vol. 267, No. 1–2, (2018), 281–319.  
19. Li, H. and Zhang, Q., “Multiobjective Optimization Problems
With Complicated Pareto Sets, MOEA/D and NSGA-II”, IEEE
Transactions on Evolutionary Computation, Vol. 13, No. 2,
(2009), 284–302.