
@inproceedings{walsh_stochastic_2002,
	address = {Amsterdam, The Netherlands, The Netherlands},
	series = {{ECAI}'02},
	title = {Stochastic {Constraint} {Programming}},
	isbn = {978-1-58603-257-9},
	url = {http://dl.acm.org/citation.cfm?id=3000905.3000930},
	abstract = {To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.},
	urldate = {2018-02-25},
	booktitle = {Proceedings of the 15th {European} {Conference} on {Artificial} {Intelligence}},
	publisher = {IOS Press},
	author = {Walsh, Toby},
	year = {2002},
	pages = {111--115},
	file = {FS01-04-020.pdf:/home/francois/Zotero/storage/Y6SRJRY9/FS01-04-020.pdf:application/pdf}
}

@article{chapados_retail_2014,
	title = {Retail store scheduling for profit},
	volume = {239},
	issn = {0377-2217},
	url = {http://www.sciencedirect.com/science/article/pii/S0377221714004561},
	doi = {10.1016/j.ejor.2014.05.033},
	abstract = {In spite of its tremendous economic significance, the problem of sales staff schedule optimization for retail stores has received relatively scant attention. Current approaches typically attempt to minimize payroll costs by closely fitting a staffing curve derived from exogenous sales forecasts, oblivious to the ability of additional staff to (sometimes) positively impact sales. In contrast, this paper frames the retail scheduling problem in terms of operating profit maximization, explicitly recognizing the dual role of sales employees as sources of revenues as well as generators of operating costs. We introduce a flexible stochastic model of retail store sales, estimated from store-specific historical data, that can account for the impact of all known sales drivers, including the number of scheduled staff, and provide an accurate sales forecast at a high intra-day resolution. We also present solution techniques based on mixed-integer (MIP) and constraint programming (CP) to efficiently solve the complex mixed integer non-linear scheduling (MINLP) problem with a profit-maximization objective. The proposed approach allows solving full weekly schedules to optimality, or near-optimality with a very small gap. On a case-study with a medium-sized retail chain, this integrated forecasting–scheduling methodology yields significant projected net profit increases on the order of 2–3\% compared to baseline schedules.},
	number = {3},
	urldate = {2018-02-25},
	journal = {European Journal of Operational Research},
	author = {Chapados, Nicolas and Joliveau, Marc and L’Ecuyer, Pierre and Rousseau, Louis-Martin},
	month = dec,
	year = {2014},
	keywords = {Constraint programming, Mixed integer programming, Retail, Shift scheduling, Statistical forecasting},
	pages = {609--624},
	file = {1-s2.0-S0377221714004561-main.pdf:/home/francois/Zotero/storage/6NFQP2JJ/1-s2.0-S0377221714004561-main.pdf:application/pdf}
}

@article{parisio_two-stage_2015,
	title = {A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand},
	volume = {53},
	issn = {0305-0483},
	url = {http://www.sciencedirect.com/science/article/pii/S0305048315000055},
	doi = {10.1016/j.omega.2015.01.003},
	abstract = {This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland.},
	urldate = {2018-02-24},
	journal = {Omega},
	author = {Parisio, Alessandra and Neil Jones, Colin},
	month = jun,
	year = {2015},
	keywords = {Stochastic programming, Decision making/process, Decision support systems, Integer programming, Operational/OR, Optimization, Resource management, Scheduling},
	pages = {97--103},
	file = {parisio2015.pdf:/home/francois/Zotero/storage/K8B6NYRM/parisio2015.pdf:application/pdf}
}