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Operating rooms (ORs) account for a significant proportion of the costs of maintaining a hospital. Surgery scheduling is optimised to reduce the costs in ORs. However, an important issue that directly affects surgery scheduling is often neglected, i.e. surgeon collaboration. In reality, a surgery requires two surgeons: the main surgeon and an assistant. The surgeon of one surgery might be assigned as the assistant of another surgery. Thus, this paper proposes a two-stage robust model that considers such collaborations. In Stage 1, it determines surgery allocations and surgeon assignments, while in Stage 2, the start time of each surgery is decided. The properties of the solutions are detected, based on which we develop a simple algorithm to solve the two-stage model. In addition, the column-and-constraint generation algorithm is applied to solve the model with a large number of surgeries. The numerical experimentation shows the good performances of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

Whether triage targets can be achieved has been an imperative assessment of service qualities for an emergency department in healthcare management. In this research, we focus on triage targets and try to fully meet the target of fast emergency response for critical patients subject to triage requirements for other category patients by optimising the medical staff allocation in the emergency department. Main challenges stem from multiple stochastic constraints and the time-consuming simulation. To solve the stochastically constrained discrete optimisation via simulation problem, we develop a discrete-event simulation model and propose a simulated-annealing-based algorithm called ConSA that adopts a special searching mechanism and an efficient simulation budget allocation rule to find a high-quality configuration of medical staff. A case study based on the data from a public hospital in Hong Kong is carried out. Numerical experiments demonstrate that our algorithm leads to a 38.28% improvement in the main performance compared to the current staff allocation and dominates other algorithms in terms of computational efficiency and output accuracy. It indicates that our method is a good decision tool for hospital managers. [ABSTRACT FROM AUTHOR]

Simulation models of emergency departments (EDs) are often built based on incomplete data, for example, missing arrival times or service-time durations. The difficulty in collecting reliable and complete data can subsequently lead to invalid simulation results. To tackle this problem, we propose a simulation and optimisation method to characterise the unavailable durations of service times. Since many services in an ED are sequential and dependent on each other, this paper considers these multiple process steps cooperatively. We first use lognormal distributions to characterise the key service durations. Then we propose a new meta-heuristic approach, which combines an Improved Adaptive Genetic Algorithm (AGA) and Simulated Annealing (SA), IAGASA, to search for the optimal set of service-time distribution parameters. To address the difficulties of applying IAGASA when noise is involved in the performance measures and improve the simulation efficiency, we jointly apply IAGASA and Optimal Computing Budget Allocation (OCBA) technology. OCBA minimises the total simulation cost for achieving a desired level of probability of correctly selecting the best set of distribution parameters, which improves the search efficiency significantly. The experimental results indicate that our proposed method can find accurate estimates of service-time distribution parameters within a relatively short time. [ABSTRACT FROM PUBLISHER]