Search

Simulation optimization (SO) techniques show a strong ability to solve large-scale problems. In this article, we concentrate on stochastically constrained SO. There are some challenges to tackle the problem: 1) the objective and constraints have no analytical forms and need to be evaluated via simulation; 2) we should make a tradeoff between exploiting around the best solution and exploring more unknown regions; and 3) both the objective value and feasibility determine the quality of a solution. Motivated by these issues, we propose an adaptive Gaussian process-based search (AGPS) to address stochastically constrained discrete SO problems. AGPS fast constructs the Gaussian process for each performance and then builds a new sampling distribution to adaptively balance exploration and exploitation considering the objective function and stochastic constraints. We show that AGPS converges to the set of globally optimal solutions with probability one. Numerical experiments demonstrate the superiority of our method compared with other advanced approaches. Note to Practitioners—Simulation is widely used to model complex and large-scale systems, such as healthcare, transportation, and supply chain logistics. When optimizing these systems, practitioners always assess overall performance by multiple indicators. Inspired by this issue, this article focuses on a general problem that aims to optimize the system’s primary performance while keeping the secondary performance within limits. We propose an adaptive Gaussian process-based method called AGPS to search for a high-quality solution. The bright spot of AGPS is that it can intelligently determine the quality of a solution considering all the stochastic performance and adaptively search the solution space. The merit lightens the burden of practitioners to design specific parameters for different performance indicators. Numerical experiments demonstrate that AGPS can apply to the large-scale discrete optimization problems with smooth function values and shows higher efficiency than existing methods. [ABSTRACT FROM AUTHOR]

Medical staff configuration is a critical problem in the management of an emergency department (ED) in Hong Kong (HK). Given the service requirements by HK government, it is imperative for the hospital managers to develop medical staff configuration in a cost-and-time-effective way. In this paper, the medical staff configuration problem in ED is modeled as minimizing the total labor cost while satisfying the service quality requirements. To solve this issue, we propose a highly efficient search method, called random boundary generation with feasibility detection (RBG-FD). The random boundary generation (RBG) is applied to efficiently identify good-quality solutions based on the objective value. The feasibility detection (FD) procedure is used to retain the probability of correct feasibility detection of each sampled solution at the desired level, which intrinsically allocates a reasonable number of simulation replications. To estimate the performance measures of the ED, a discrete-event simulation model is developed to reflect the patient flow. Using these techniques, the efficiency of identifying the optimal staff configuration can be significantly improved. A case study is performed in a public hospital in HK. The numerical results indicate significantly higher practicability and efficiency of the proposed method with different patient arrival rates and service constraints. Note to Practitioners This paper seeks to solve the problem of minimizing the medical staff cost constrained by certain service requirements [i.e., patients’ waiting times for treatment] at an emergency department in Hong Kong. In our formulation, these service requirements are characterized by some stochastic constraints. Most of the existing random search methods concentrate on the computing efforts in the neighborhood of the best-so-far solutions in order to obtain good-quality solutions. Due to the special structure of this problem and ease of computing the objective values, we proposed an efficient random search approach that iteratively identifies a solution with a better objective value than that of the current best solution. Experimental studies demonstrate the significantly higher efficiency of this method. In order to obtain the same solution quality, it is able to reduce the computational time by 90% compared with some existing approaches in the literature. [ABSTRACT FROM PUBLISHER]