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 The pdf suggests a constrained problem with binary decision variables and a linear objective function. The code, however, employs different decision variables because when using simulation optimization we can decrease the number of decision variables and make the problem unconstrained, which makes the objective function nonlinear but it is still easy to calculate for each run of the simulation. In the code there are N decision variables, where x(i) denotes the day on which patient i is scheduled for surgery. The variables x(i) themselves are bound to be integer and between r(i) (the first day patient i can be scheduled for surgery) and H+1 where H is the number of days in the planning period, H+1 indicating that the patient is not scheduled for surgery during the planning horizon.
 Suggested budget: 1000, 10000

 M. Lamiri, F. Grimaud, and X. Xie. Optimization methods for a stochastic surgery
planning problem. International Journal of Production Economics, 120:400 – 410, 2009.