TY - GEN
T1 - Genetic Algorithm-based Clustering Methodology for Maintenance Scheduling in Healthcare Facilities
AU - Ahmed, Reem
AU - Nasiri, Fuzhan
AU - Zayed, Tarek
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/12
Y1 - 2021/12
N2 - Maintenance scheduling is considered a fundamental process within facility management frameworks particularly in high-risk buildings like healthcare facilities. Moreover, a proper agglomeration of maintenance actions is crucial for spaces necessitating a smooth continuous operation like Intensive Care Units and Operation Rooms. Consequently, this paper proposes an optimization-based clustering framework that first selects the most suitable actions to be applied for each hospital asset according to the cost, improvement and downtime associated with each maintenance action. This is performed on a multi-objective Genetic Algorithm basis. The output is further fed into a hierarchical clustering algorithm that combines the relevant closely located actions together. This aids in efficiently allocating available resources as well as minimizing disruptions in critical locations within healthcare facilities. The preliminary results of applying the proposed framework on a case study hospital surpassed the current practice by 29% by offering a highly improved performance of hospital assets within the lowest possible operational downtime.
AB - Maintenance scheduling is considered a fundamental process within facility management frameworks particularly in high-risk buildings like healthcare facilities. Moreover, a proper agglomeration of maintenance actions is crucial for spaces necessitating a smooth continuous operation like Intensive Care Units and Operation Rooms. Consequently, this paper proposes an optimization-based clustering framework that first selects the most suitable actions to be applied for each hospital asset according to the cost, improvement and downtime associated with each maintenance action. This is performed on a multi-objective Genetic Algorithm basis. The output is further fed into a hierarchical clustering algorithm that combines the relevant closely located actions together. This aids in efficiently allocating available resources as well as minimizing disruptions in critical locations within healthcare facilities. The preliminary results of applying the proposed framework on a case study hospital surpassed the current practice by 29% by offering a highly improved performance of hospital assets within the lowest possible operational downtime.
KW - Genetic Algorithm
KW - Healthcare Facilities
KW - Hierarchical Clustering
KW - K-Means Clustering
KW - Maintenance Scheduling
UR - http://www.scopus.com/inward/record.url?scp=85125773675&partnerID=8YFLogxK
U2 - 10.1109/DASA53625.2021.9682280
DO - 10.1109/DASA53625.2021.9682280
M3 - Conference article published in proceeding or book
AN - SCOPUS:85125773675
T3 - 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
SP - 643
EP - 646
BT - 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
Y2 - 7 December 2021 through 8 December 2021
ER -