Patient non-attendance is the major challenge that reduces practice efficiency, resource utilization, and clinic accessibility, and leads to increased cost and diminished quality of care, while the clinic scheduling system is known as a determining factor for clinic efficiency, resource utilization and the accessibility of patients to healthcare facilities. A suitable and optimized scheduling system is one of the most important components for efficient care delivery to address the major challenges in the healthcare industry. Hence, reducing the adverse effect of patient no-shows and short-notice appointment notifications through the appointment scheduling approach is a strategically important matter for any healthcare systems. In this research, three patient scheduling models are proposed to address the patient non-attendance problem in the outpatient clinics. The first model is a two-stage mixed stochastic programming model, which can be used to optimize the overbooking decisions: (1) How many appointment slots should be overbooking; (2) Which appointment slot should be overbooking. In addition, this model also considers the cooperation between providers and patients’ choice. The second model is a Markov Decision Process (MDP) model, which can be used to optimize the walk-in patient admission policy in clinics with single physician by answering the four vital questions: (1) When the walk-in patient admission decisions should be made; (2) At each decision point, how many walk-in patients should be admitted; (3) Which provider should serve the admitted walk-in patients; (4) When the admitted walk-in patient should be served. By using this MDP model, heuristic optimal walk-in patient admission rules have been found for the single physician systems. For systems with more physicians, a more advanced two-stage mixed stochastic programming model (the third model) is proposed in order to make the optimal real time walk-in patient admission decisions. At last, it worthwhile to mention that novel solution approach has also been developed in order to solve these models in the efficient and effective manner.