A simulation-optimization approach for capacity planning and appointment scheduling of blood donors based on mathematical programming representation of event dynamics

This paper presents a simulation-optimization approach based on mathematical programming representation of event dynamics for capacity planning and appointment scheduling in blood collection systems. The mathematical model is formulated as a multi-criteria mixed integer programming (MIP) which corresponds to the stochastic counterpart approximating problem which characterize the underlying event dynamics and transient performance measures of a blood collection system which has been simplified to a M/M/S(t) system. In this case the random variables analyzed are non-homogeneous arrivals of whole blood donors, physician service times and no-shows of scheduled donors. Numerical experiments are then performed to determine the appointment scheduling and capacity planning for different intraday (within-day) patterns of blood donors systems in order to simultaneously maximize donor service level and minimize the system overtime.