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Taking into account all the limitations found in
scheduling problems and the high level of organisa-
tional complexity that a hospital currently has, we
consider this approach a possible solution to SSP
problems, of which the organisation of surgeries on
time and the necessary cost control, crucial to opti-
mise all management processes, always prevail. SA
presents extremely satisfactory results, nullifying the
number of surgeries with a penalty, i.e., surgeries with
a scheduled date higher than the deadline. Further-
more, this algorithm provides a nearly optimal solu-
tion, reaching a stabilisation point after 100 iterations,
something that does not happen in the HC and that
may justify the fact that it does not allocate a greater
number of surgeries.
In the first phase of this research, new perspec-
tives were obtained on how an approach based on AI
heuristics can translate into a solution capable of au-
tomating and improving this process. However, some
scenarios could be further explored as future work:
1. Testing this study in other healthcare specialities
will be the first step to understanding if the suc-
cess of implementing this logic is well achieved
in different environments.
2. A deeper study on the nullification of the schedul-
ing penalty may be equated. While the hospital
would like to pay the least amount of costs related
to surgeries scheduled after the deadline, it may
be pertinent to identify whether a proposal with
a higher penalty will better serve the hospital’s
scheduling interests.
3. A new constraint can be added, always consider-
ing that there are urgent cases to be executed. De-
spite in this study only surgeries from the waiting
list were considered, it is possible to complement
the scheduling constraints with an additional rule,
always leaving space in an OR for urgent cases;
4. Improving the efficiency of the optimization
method by exploring new models and their con-
figurations (only local search algorithms were ad-
dressed, but there is some scope for development
for population-based, blind search or even multi-
objective search algorithms).
REFERENCES
Agnetis, A., Coppi, A., Corsini, M., Dellino, G., Meloni,
C., and Pranzo, M. (2014). A decomposition approach
for the combined master surgical schedule and surgi-
cal case assignment problems. Health Care Manage-
ment Science, 17(1):49–59.
Azevedo, A. and Santos, M. (2009). Business Intelligence
- State of the Art, Trends, and Open Issues. Funchal -
Madeira, Portugal.
Balan, S. (2022). Metaheuristics in optimization: Algorith-
mic perspective. Informs.
Brent, R. P. (1989). Efficient implementation of the first-
fit strategy for dynamic storage allocation. ACM
Transactions on Programming Languages and Sys-
tems, 11(3):388–403.
Briganti, G. and Le Moine, O. (2020). Artificial Intelli-
gence in Medicine: Today and Tomorrow. Frontiers
in Medicine, 7. Publisher: Frontiers.
Cortez, P. (2014). Modern Optimization with R. Use R!
Springer International Publishing : Imprint: Springer,
Cham, 1st ed. 2014 edition.
Delen, D. (2019). Prescriptive Analytics: The Final Fron-
tier for Evidence-Based Management and Optimal
Decision Making. Pearson FT Press, Hoboken, 1st
edition edition.
Gandomi, A. H., Yang, X.-S., Talatahari, S., and Alavi,
A. H. (2013). Metaheuristic Algorithms in Modeling
and Optimization. In Metaheuristic Applications in
Structures and Infrastructures, pages 1–24. Elsevier.
G
¨
org
¨
ul
¨
u, Z.-K. and Pickl, S. (2013). Adaptive Business In-
telligence: The Integration of Data Mining and Sys-
tems Engineering into an Advanced Decision Sup-
port as an Integral Part of the Business Strategy. In
Rausch, P., Sheta, A. F., and Ayesh, A., editors, Busi-
ness Intelligence and Performance Management: The-
ory, Systems and Industrial Applications, Advanced
Information and Knowledge Processing, pages 43–58.
Springer, London.
Jayaratne, M., Nallaperuma, D., De Silva, D., Alahakoon,
D., Devitt, B., Webster, K. E., and Chilamkurti,
N. (2019). A data integration platform for patient-
centered e-healthcare and clinical decision support.
Future Generation Computer Systems, 92:996–1008.
Luke, S. (2012). Essentials of Metaheuristics. Lulu, 2nd
ed. 2012 edition.
Michalewicz, Z., Schmidt, M., Michalewicz, M., and
Chiriac, C. (2006). Adaptive Business Intelligence.
Springer-Verlag, Berlin Heidelberg.
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chat-
terjee, S. (2007). A Design Science Research Method-
ology for Information Systems Research. Journal of
Management Information Systems, 24(3):45–77.
van Hartskamp, M., Consoli, S., Verhaegh, W., Petkovic,
M., and van de Stolpe, A. (2019). Artificial Intel-
ligence in Clinical Health Care Applications: View-
point. Interactive Journal of Medical Research,
8(2):e12100.
Visintin, F., Cappanera, P., Banditori, C., and Danese, P.
(2017). Development and implementation of an oper-
ating room scheduling tool: an action research study.
Production Planning & Control, 28(9):758–775.
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