Noisy Optimization of Dispatching Policy for the Cranes at the Storage Yard in an Automated Container Terminal

• الحاوية / القاعدة: Applied Sciences
• المؤلفون الرئيسيون: Jeongmin Kim, Ellen J. Hong, Youngjee Yang, Kwang Ryel Ryu
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: MDPI AG 2021-07-01
• الموضوعات: seaport container terminal; storage yard; crane dispatching; evolutionary algorithm; noisy optimization
• الوصول للمادة أونلاين: https://www.mdpi.com/2076-3417/11/15/6922

In this paper, we claim that the operation schedule of automated stacking cranes (ASC) in the storage yard of automated container terminals can be built effectively and efficiently by using a crane dispatching policy, and propose a noisy optimization algorithm named N-RTS that can derive such a policy efficiently. To select a job for an ASC, our dispatching policy uses a multi-criteria scoring function to calculate the score of each candidate job using a weighted summation of the evaluations in those criteria. As the calculated score depends on the respective weights of these criteria, and thus a different weight vector gives rise to a different best candidate, a weight vector can be deemed as a policy. A good weight vector, or policy, can be found by a simulation-based search where a candidate policy is evaluated through a computationally expensive simulation of applying the policy to some operation scenarios. We may simplify the simulation to save time but at the cost of sacrificing the evaluation accuracy. N-RTS copes with this dilemma by maintaining a good balance between exploration and exploitation. Experimental results show that the policy derived by N-RTS outperforms other ASC scheduling methods. We also conducted additional experiments using some benchmark functions to validate the performance of N-RTS.