Citation
Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays. Journal of Scheduling, 19 (4): 377-389, 2016.
Selected
Abstract
We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of operating rooms for patients requiring a surgery. In addition, we propose a more elaborate model that includes a flexible planning horizon, a complex notion of patient delay, and new components of the objective function. We design a solution approach based on local search, which explores the search space using a composite neighbourhood. In addition, we develop an instance generator that uses real-world data and statistical distributions so as to synthesise realistic and challenging case studies, which are made available on the web along with our solutions and the validator. Finally, we perform an extensive experimental evaluation of our solution method including statistically principled parameter tuning and an analysis of some features of the model and their corresponding impact on the objective function.
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Bibtex
@ARTICLE{2016-377-389-SI, author = {Ceschia, Sara and Schaerf, Andrea},
title = {{Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays}},
journal = {Journal of Scheduling},
year = {2016},
volume = {{19}},
pages = {377--389},
number = {4},
note = {Selected},
abstract = {{We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of operating rooms for patients requiring a surgery. In addition, we propose a more elaborate model that includes a flexible planning horizon, a complex notion of patient delay, and new components of the objective function. We design a solution approach based on local search, which explores the search space using a composite neighbourhood. In addition, we develop an instance generator that uses real-world data and statistical distributions so as to synthesise realistic and challenging case studies, which are made available on the web along with our solutions and the validator. Finally, we perform an extensive experimental evaluation of our solution method including statistically principled parameter tuning and an analysis of some features of the model and their corresponding impact on the objective function.}},
doi = {{10.1007/s10951-014-0407-8}},
eissn = {{1099-1425}},
issn = {{1094-6136}},
orcid-numbers = {{Ceschia, Sara/0000-0003-1191-1929}},
owner = {Graham},
timestamp = {2017.01.18},
unique-id = {{ISI:000379627200002}} }