Collaborative scheduling of operating room in hospital network: Multi-objective learning variable neighborhood search

In this study, the operating room scheduling of hospital networks with virtual alliance has been studied, which at the same time, there is a kind of cooperation and competition among the agents. The main feature in networks with the virtual alliance is the possibility of different objective functions among the agents, which has priority for agents compared to the network’s overall objective. Here, by considering the conditions of emergency arrival, the time of inter-hospital transportation, and the elective patients and non-elective patients in the scheduling, an attempt has been made to bring the problem closer to real-world situations. To solve this problem, first, a mixed-integer mathematical programming model is proposed. Because of its NP-hardness, then, a multi-objective learning variable neighborhood search algorithm is designed to minimize total completion of surgeries, the cost of allocating the patient to the hospital and the surgeon, and the cost of overtime operating rooms throughout the network. Finally, the performance of the proposed algorithm is evaluated in comparison with the NSGA-II and memetic-based algorithm, which due to considering the learning mechanism along with the use of various neighborhood structures in the proposed algorithm, its results are promising. It is expected that by using the proposed algorithms in a cooperative structure, the hospitals are able to achieve optimal/near-optimal solutions in a reasonable time, in which, in addition to more economic activity, patients also benefit due to better use of resources.