4 research outputs found
A survey of AI in operations management from 2005 to 2009
Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.
Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.
Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an
increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.
Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified.
Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.
Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research
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Water chemistry of Oregon Cascade wilderness lakes : a comparison to 1985 data
The Western Lake Survey (WLS) of 1985 documented the status of lake water chemistry across the western US and inferred population representations of various subregions and geomorphic units via statistically analyses. Results from this 1985 study indicated that lakes of the Oregon Cascades had the second most pristine and dilute lake water chemistry in the nation. Accompanying this finding were indications of the extreme vulnerability of these lakes to acidification due to unique High Cascade geology and hydrology. This current study replicates the methods and analyses of the WLS for six Oregon Cascade wilderness lakes and presents analytical water chemistry results and modeling scenarios, along with quality assurance documentation and comparisons to 1985 data. Results indicate variability in cation and anion composition since the 1985 study. Acid neutralizing capacity, Na⁺, and K⁺ exhibited p-value test (95% confidence) determined significant increases since 1985 for each lake sampled. pH remained relatively stable; however decreased outside of data uncertainty ranges for one lake
(Helen Lake, pH drop of 0.4 pH units). Changes both inside and outside data uncertainty ranges for other parameters analyzed were documented; however, major anthropogenic influences on water chemistry was not apparent. Data serves as a valuable addition to the ongoing 'database of results' for Oregon Cascade lake water chemistry. A deliverable of this research is a long term monitoring plan with additional parameters to further document and understand Oregon Cascade wilderness lake water chemistry in the face of increasing atmospheric deposition of anthropogenic chemicals and a changing climate