A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

Omega (United Kingdom)

Volumn 53, Issue , 2015, Pages 97-103

  Parisio, Alessandra ^a   Neil Jones, Colin ^b  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b EPFL   (Switzerland)

Author keywords

Decision making process; Decision support systems; Integer programming; Operational OR; Optimization; Resource management; Scheduling; Stochastic programming

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONSTRAINT PROGRAMMING; DECISION MAKING; INTEGER PROGRAMMING; SALES; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS;

DECISION-MAKING PROCESS; INTEGER PROGRAM- MING; OPERATIONAL/OR; OPTIMISATIONS; RESOURCE MANAGEMENT; RETAIL OUTLETS; SCHEDULING; SCHEDULING SYSTEMS; TWO-STAGE STOCHASTIC PROGRAMMING; UNCERTAIN DEMAND;

DECISION SUPPORT SYSTEMS;

EID: 84921713375     PISSN: 03050483     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.omega.2015.01.003     Document Type: Article

References (30)

1. Brucker P., Qu R., Burke E. Personnel scheduling. models and complexity. European Journal of Operational Research 2011, 210(3):467-473.
2. Restrepo M., Lozano L., Medaglia A. Constrained network-based column generation for the multi-activity shift scheduling problem. International Journal of Production Economics 2012, 140(1):466-472.
3. Hojati M., Patil A. An integer linear programming-based heuristic for scheduling heterogeneous, part-time service employees. European Journal of Operational Research 2011, 209(1):37-50.
4. Lequy Q., Desaulniers G., Solomon M. A two-stage heuristic for multi-activity and task assignment to work shifts. Computers & Industrial Engineering 2012, 63(4):831-841.
5. Adamuthe A, Bichkar R. Tabu search for solving personnel scheduling problem. In: International conference on communication, information & computing technology; 2012. p. 1-6.
6. Yan J, Wang Z. Ga based algorithm for staff scheduling considering learning-forgetting effect. In: IEEE international conference on industrial engineering and engineering management, vol. 1; 2011. p. 122-6.
7. Maenhout B., Vanhoucke M. An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems. Omega 2013, 41(2):485-499.
8. Smet P., Wauters T., Mihaylov M., Berghe G.V. The shift minimisation personnel task scheduling problem. a new hybrid approach and computational insights. Omega 2014, 46:64-73.
9. Örmeci E.L., Salman F.S., Yücel E. Staff rostering in call centers providing employee transportation. Omega 2014, 43:41-53.
10. Ladier A.-L., Alpan G., Penz B. Joint employee weekly timetabling and daily rostering. a decision-support tool for a logistics platform. European Journal of Operational Research 2014, 234(1):278-291.
11. Chapados N., Joliveau M., L'Ecuyer P., Rousseau L.-M. Retail store scheduling for profit. European Journal of Operational Research 2014, 239(3):609-624.
12. Shapiro A, Dentcheva D, Ruszczyński A. In: Lectures on stochastic programming: modeling and theory, MPS-SIAM series on optimization. Society for Industrial & Applied Mathematics: USA; 2009.
13. Kall P., Mayer J. Stochastic linear programming: models, theory, and computation 2005, Springer-Verlag, Berlin, Germany.
14. Robbins T., Harrison T. A stochastic programming model for scheduling call centers with global service level agreements. European Journal of Operational Research 2010, 207:1608-1619.
15. Kim K, Mehrotra S. A two-stage stochastic integer programming approach to integrated staffing and scheduling with application to nurse management. http://www.optimization-online.org/.
16. den Bergh J.V., Belien J., Bruecker P.D., Demeulemeester E., Boeck L.D. Personnel scheduling. a literature review. European Journal of Operational Research 2013, 226:367-385.
17. Bruecker PD, den Bergh JV, Beliën J, Demeulemeester E. Workforce planning incorporating skills: state of the art. European Journal of Operational Research 2014: 1-16.
18. Cortes C. Support-vector networks. Machine Learning 1995, 20:273-297.
19. Osuna E, Freund R, Girosi F. Support vector machines: training and applications. Tech. Rep. AIM-1602, CBCL-144, Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology, Available as March 1997. http://hdl.handle.net/1721.1/7290.
20. Zucchini W., MacDonald I. Hidden Markov models for time series 2009, MacDonald Chapman and Hall/CRC.
21. Jones C, Nolde K. Demand driven employee scheduling for retail outlets. Tech. Rep. EPFL-REPORT-187141, Automatic Control Lab, EPFL-STI-IGM-LA, Station 9, CH-1015 Lausanne, Switzerland; 2009.
22. Bemporad A. Model predictive control of hybrid systems. In: Advanced process control applications for industry workshop. Vancouver, British Columbia; 2007.
23. Richard A, How J. Mixed-integer programming for control. In: American control conference, vol. 4. Portland, Oregon (USA); 2005.
24. Yue L, Yafeng Y, Junjun G, Chongli T. Demand forecasting by using support vector machine. In: Third international conference on natural computation, vol. 3; 2007. p. 272-6.
25. Sun H, Jia R, Xue Y. Customer demand forecasting based on SVR using moving time window method. In: International conference on industrial engineering and engineering management; 2009. p. 104-7.
26. Heitsch H., Römisch W. Scenario reduction algorithms in stochastic programming. Journal of Computational Optimization and Applications 2003, 24(2-3):187-206.
27. Kaut M., Wallace S.W. Evaluation of scenario-generation methods for stochastic programming. Pacific Journal of Optimization 2007, 3(2):257-271.
28. Pelckmans K, Suykens J, VanGestel T, et al. LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines. ESAT-SCD-SISTA K.U. Leuven Kasteelpark Arenberg 10, B-3001 Leuven-Heverlee, Belgium; 1998.
29. http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html.
30. ILOG, CPLEX 12.0 Users manual, 2010.