The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling
通过计算机仿真，测试了七种场景下动态作业车间调度问题的表现，发现基于布朗运动的调度策略在随机场景下优于最短期望加工时间规则，表明确定性仿真结果不一定适用于实际随机环境。
The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic job-shop scheduling problems (including a two-station version of Conway et al.'s 1967 nine-station symmetric shop) are tested under seven different scenarios, one deterministic and six stochastic, using computer simulation. The deterministic scenario, where the processing times are exponential and observed by the scheduler, has been considered in many simulation studies, including Conway et al.'s. The six stochastic scenarios include the case where the processing times are exponential and only the mean is known by the scheduler, and five different cases where the machines are subject to unpredictable failures. Two policies were tested, the shortest expected processing time (SEPT) rule, and a rule derived from a Brownian analysis of the corresponding queueing network scheduling problem. Although the SEPT rule performed well in the deterministic scenario, it was easily outperformed by the Brownian policies in the six stochastic scenarios for all three problems. Thus, the results from simulation studies of dynamic, deterministic job-shop scheduling problems may not carry over to the more realistic setting where there is unpredictable variability.