Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10115-006-0057-z
Title: Artificial intelligence methodologies for agile refining: An overview
Authors: Srinivasan, R. 
Keywords: Decision support
Enterprise-wide optimization
Fault diagnosis
Pattern recognition
Petroleum refining
Process supervision
Supply chain management
Issue Date: Jul-2007
Source: Srinivasan, R. (2007-07). Artificial intelligence methodologies for agile refining: An overview. Knowledge and Information Systems 12 (2) : 129-145. ScholarBank@NUS Repository. https://doi.org/10.1007/s10115-006-0057-z
Abstract: Agile manufacturing is the capability to prosper in a competitive environment of continuous and unpredictable changes by reacting quickly and effectively to the changing markets and other exogenous factors. Agility of petroleum refineries is determined by two factors - ability to control the process and ability to efficiently manage the supply chain. In this paper, we outline some challenges faced by refineries that seek to be lean, nimble, and proactive. These problems, which arise in supply chain management and operations management are seldom amenable to traditional, monolithic solutions. As discussed here using several examples, methodologies drawn from artificial intelligence - software agents, pattern recognition, expert systems - have a role to play in this path toward agility. © Springer-Verlag London Limited 2007.
Source Title: Knowledge and Information Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/68135
ISSN: 02191377
DOI: 10.1007/s10115-006-0057-z
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