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As we anticipate future challenges in healthcare due to insufficient resources, it becomes imperative to proactively address potential issues that directly impact human life. This doctoral study is dedicated to enhancing the efficiency of healthcare practices through rigorous scientific research. Our focus lies in developing cost-effective and practical solutions, particularly in healthcare planning, by harnessing the power of big data in today's world. This approach enables us to leverage modern technologies such as machine learning and deep learning within the healthcare domain.

Furthermore, we integrate classical operational research and mathematical modeling methods traditionally used in scheduling problems with these cutting-edge tools. By doing so, we aim to not only capitalize on today's vast datasets but also provide integrated solutions using operational research methods for more effective approaches. Moreover, our integrated approach can pave the way for future research by inspiring similar solution ideas in combinatorial problems. It's crucial to emphasize the value of our prescriptive analytics in this study, aimed to benefit practitioners. This multidisciplinary approach ensures that our research contributes to the advancement of healthcare practices by marrying contemporary technologies with proven operational research methodologies.