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A Sample-gradient-based Algorithm for a Multiple-OR and PACU Surgery Scheduling Problem

Miao Bai (mib411***at***lehigh.edu)
R. H Storer (rhs2***at***lehigh.edu)
G. L. Tonkay (glt0***at***lehigh.edu)

Abstract: In this paper, we study a multiple-OR surgery scheduling problem constrained by shared 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 thus 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 are possible in hospitals where PACU beds are a constraint.

Keywords: surgery scheduling, PACU, stochastic optimization, sample average approximation, sample-based gradients

Category 1: Applications -- OR and Management Sciences

Category 2: Stochastic Programming

Category 3: Applications -- OR and Management Sciences (Scheduling )

Citation:

Download: [PDF]

Entry Submitted: 10/24/2015
Entry Accepted: 10/24/2015
Entry Last Modified: 08/17/2016

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