Towards Business Process Intelligence to Port2Port Governance Responsibility based on Learning Algorithms

A. Halabi-Echeverry, Juan C. Aldana-Bernal, D. Villate-Daza, S. Islam
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Abstract

This paper provides an approach to Port2Port Business Process Intelligence (BPIs) helping decision makers in tackling constant changes in governance responsibilities. This consideration leads to the need for Port2Port technological solutions among ports and development of capabilities on sharing information, planning and execution in a collaborative way. It is offered three Port2Port BPIs: 1) Control process for greenhouse gas emissions coming from ships, 2) The process for monitoring ballast Waters, 3) Sanitation Performance Compliance under COVID19 situation. The identification and selection of learning tasks have been integrated into the conceptualisation scheme, suggesting the exploitation of Deep reinforcement Learning (RL) to capture important aspects of the real problem facing the learning agents interacting with its environment to achieve the proposed goals.
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从业务流程智能到基于学习算法的Port2Port治理责任
本文提供了一种Port2Port业务流程智能(bpi)的方法,帮助决策者处理治理职责中的不断变化。这种考虑导致港口之间需要Port2Port技术解决方案,并以协作的方式开发共享信息、规划和执行的能力。提供三个Port2Port bpi: 1)船舶温室气体排放控制流程,2)压载水监测流程,3)2019冠状病毒病疫情下的卫生绩效合规。学习任务的识别和选择已经集成到概念化方案中,这表明利用深度强化学习(RL)来捕捉学习代理与环境交互以实现提议目标所面临的实际问题的重要方面。
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