TY  - JOUR
AU  - Dekhici, Latifa
AU  - Guerraiche, Khaled
AU  - Jlassi, Jihen
AU  - Djari, Mohammed Adel
PY  - 2026
DA  - 2026/08/26
TI  - Energy-Aware Operating Theatres Scheduling Using Metaheuristics
JO  - Intelligent Computing for Engineering
T2  - Intelligent Computing for Engineering
JF  - Intelligent Computing for Engineering
VL  - 1
IS  - 1
SP  - 17
EP  - 29
DO  - 10.62762/ICE.2026.457366
UR  - https://www.icck.org/article/abs/ICE.2026.457366
KW  - operating room scheduling
KW  - energy efficiency
KW  - multi-objective optimisation
KW  - HVAC
KW  - Healthcare
KW  - metaheuristics
KW  - NSGA-II
KW  - interior search algorithm
AB  - Operating rooms (ORs) account for a significant fraction of a hospital's energy footprint, yet traditional surgical scheduling models ignore the dynamic energy consumption of heating, ventilation, and air conditioning (HVAC) systems, particularly the thermal inertia and peak restart loads. Furthermore, the interaction with the Post‑Anesthesia Care Unit (PACU) creates blocking effects that invalidate isolated OR schedules. This paper introduces a bi‑objective mixed‑integer linear programming (MILP) model and a metaheuristic framework for the Green Ambulatory Surgery Scheduling Problem with PACU constraints (G‑ASSP‑P). The objectives minimise both total completion time (makespan) and total energy consumption, explicitly modelling HVAC transition penalties. To solve large, realistic instances, a discrete adaptation of the Interior Search Algorithm (ISA) is adopted and compared against the Non‑dominated Sorting Genetic Algorithm II (NSGA‑II). Computational experiments on three instance scales (20, 50, 70 surgeries) with 4–12 ORs and 8–25 PACU beds demonstrate that NSGA‑II consistently outperforms ISA in terms of hypervolume and solution diversity. The trade‑off analysis reveals that substantial energy savings (exceeding 30%) can be achieved with only a marginal increase in makespan, offering hospital managers a practical tool to balance efficiency and sustainability.
SN  - 5 Articles Required
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Dekhici2026EnergyAwar,
  author = {Latifa Dekhici and Khaled Guerraiche and Jihen Jlassi and Mohammed Adel Djari},
  title = {Energy-Aware Operating Theatres Scheduling Using Metaheuristics},
  journal = {Intelligent Computing for Engineering},
  year = {2026},
  volume = {1},
  number = {1},
  pages = {17-29},
  doi = {10.62762/ICE.2026.457366},
  url = {https://www.icck.org/article/abs/ICE.2026.457366},
  abstract = {Operating rooms (ORs) account for a significant fraction of a hospital's energy footprint, yet traditional surgical scheduling models ignore the dynamic energy consumption of heating, ventilation, and air conditioning (HVAC) systems, particularly the thermal inertia and peak restart loads. Furthermore, the interaction with the Post‑Anesthesia Care Unit (PACU) creates blocking effects that invalidate isolated OR schedules. This paper introduces a bi‑objective mixed‑integer linear programming (MILP) model and a metaheuristic framework for the Green Ambulatory Surgery Scheduling Problem with PACU constraints (G‑ASSP‑P). The objectives minimise both total completion time (makespan) and total energy consumption, explicitly modelling HVAC transition penalties. To solve large, realistic instances, a discrete adaptation of the Interior Search Algorithm (ISA) is adopted and compared against the Non‑dominated Sorting Genetic Algorithm II (NSGA‑II). Computational experiments on three instance scales (20, 50, 70 surgeries) with 4–12 ORs and 8–25 PACU beds demonstrate that NSGA‑II consistently outperforms ISA in terms of hypervolume and solution diversity. The trade‑off analysis reveals that substantial energy savings (exceeding 30\%) can be achieved with only a marginal increase in makespan, offering hospital managers a practical tool to balance efficiency and sustainability.},
  keywords = {operating room scheduling, energy efficiency, multi-objective optimisation, HVAC, Healthcare, metaheuristics, NSGA-II, interior search algorithm},
  issn = {5 Articles Required},
  publisher = {Institute of Central Computation and Knowledge}
}