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A sample gradient-based algorithm for a multiple-OR and PACU surgery scheduling problem

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ABSTRACT In this article, we study a surgery scheduling problem in multiple Operating Rooms (ORs) constrained by the Post-Anesthesia Care Unit (PACU) capacity within the block-booking framework. With surgery sequences… Click to show full abstract

ABSTRACT In this article, we study a surgery scheduling problem in multiple Operating Rooms (ORs) constrained by the Post-Anesthesia Care Unit (PACU) capacity within the block-booking framework. With surgery sequences predetermined in each OR, a Discrete-Event Dynamic System (DEDS) is devised for the problem. A DEDS-based stochastic optimization model is formulated in order to minimize the cost incurred from patient waiting time, OR idle time, OR blocking time, OR overtime, and PACU overtime. A sample gradient-based algorithm is proposed for the sample average approximation of our formulation. Numerical experiments suggest that the proposed method identifies near-optimal solutions and outperforms previous methods. We also show that considerable cost savings (11.8% on average) are possible in hospitals where PACU beds are a constraint.

Keywords: pacu; surgery; scheduling problem; sample gradient; problem; surgery scheduling

Journal Title: IISE Transactions
Year Published: 2017

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