Towards Business Process Intelligence to Port2Port Governance Responsibility based on Learning Algorithms
6th IEEE International Conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1788634
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. © 2021 IEEE.
Business Process Intelligence (BPI); Learning algorithms; port governance responsibilities; Port2Port; Compliance control; Decision making; Deep learning; Gas emissions; Greenhouse gases; Reinforcement learning; Business Process Intelligence; Control process; Decision makers; Greenhouse gas emissions; Planning and execution; Port governance responsibility; Sharing information; Technological solution
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
6th IEEE International Conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021
Year:
2021
Document Type:
Article
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