Patient appointment scheduling (AS) in outpatient clinics is a widely studied subject and plays an important role in facilitating the efficient use of clinical resources and patients' timely access to quality care. This paper considers two AS systems: open access (OA) and overbooking (OB). Clinics make strategic decisions on selecting an AS system and then make tactical decisions on the efficient or optimal use of the system based on the selection. This study proposes some guidelines for the strategic choice of an AS system. For this purpose, we conduct a discrete-event simulation to compare the two AS systems under various environments. We employ four performance measures for the comparison: overtime work, the proportion of unmet demand, in-clinic waiting times, and the use of appointment time slots. For the analysis, we devise an integrated measure representing a linear combination of the four measures. We divide the analysis into two phases. In the first phase, well-performed OA and OB policies are separately identified, and in the second phase, the two scheduling systems with the identified policies are compared. We find overbooking is more robust to various clinic environments and performs better than open access in general. Along with that result, we additionally suggest some rules for determining best open access and overbooking policies.
This study identifies the optimal management policy of a given energy storage system (ESS) installed in a grid-connected wind farm in terms of maximizing the monetary benefits and provides guidelines for defining the economic value of the ESS under optimal management policy and selecting the optimal size of the ESS based on economic value. Considering stochastic models for wind power and electricity price, we develop a finite-horizon periodic-review Markov decision process (MDP) model to seek the optimal management policy. We also use a simple optimization model to find the optimal storage capacity and charging/discharging capacity of the ESS. By applying our analytic approach to a real-world grid-connected wind farm located in South Korea, we verify the usefulness of this study. Our numerical study shows that the economic value of the ESS is highly dependent on management policy, wind electricity variability, and electricity price variability. Thus, the optimal size of ESS should be carefully determined based on the locational characteristics and management policy even with limited investments. Furthermore, this study provides a meaningful policy implication regarding how much of a subsidy the government should provide for installing ESS in a wind farm.
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