The ranking and selection of simulation optimization is a very powerful tool in systems engineering and operations research. Due to the influence of randomness, the algorithms for ranking and selection need high and uncertain amounts of computing power. Recent advances in cloud computing provide an economical and flexible platform to execute these algorithms. Among all ranking and selection algorithms, the optimal computing budget allocation (OCBA) algorithm is one of the most efficient. However, because of the lack of sufficient samples that can be executed in parallel at each stage, some features of the cloud-computing platform, such as parallelism, scalability, flexibility, and symmetry, cannot be fully utilized. To solve these problems, this paper proposes a distributed asynchronous OCBA (DA-OCBA) algorithm. Under the framework of parallel asynchronous simulation, this algorithm takes advantage of every idle docker container to run better designs in advance that are selected by an asymptotic allocation rule. The experiment demonstrated that the efficiency of simulation optimization for DA-OCBA was clearly higher than that for the traditional OCBA on the cloud platform with symmetric architecture. As the number of containers grew, the speedup of DA-OCBA was linearly increasing for simulation optimization.
Robust ranking and selection with optimal computing budget allocation
