AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

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

Production and Applications of Nanoparticles Grown or Deposited on Surfaces

This thesis focuses on the development of two methods of nanoparticle synthesis, the applications of the nanoparticles and the characterisation, classification, and behaviour of the nanoparticle structures. Building on the previously developed Matrix Assembly Cluster Source (MACS) technology, gold nanoparticles were fabricated using a newly created MiniMACS system, altered by using butane as a matrix gas which deposits using readily available liquid nitrogen (at a temperature of 77 K). Direct sputtering of atoms from a target material was also developed in this system, displaying the versatility of the system by the ease of switching between the two methods. This Sputtered Atom Source (SAS) technique was calibrated and subsequently used to create nanoparticles by growth of gold clusters from atoms on a carbon surface then were classified according to the cluster structure. After analysis of over 600 gold clusters identified in aberration corrected Scanning Transmission Electron Microscopy (STEM) images, it was found that clusters smaller than 300 atoms tend to have a glassy/amorphous structure, while above this size decahedral and FCC (Face – Centred Cubic) motifs compete for the dominant structure, with FCC being slightly more favourable. Icosahedral was observed very rarely when carrying out this analysis.Dynamic analysis was also undertaken by studying a video of 500 frames of thesame two clusters showing a coalescence event. Before aggregation, the largercluster analysed structurally consisted of less than the 309 atoms that represent an “ideal” magic number cluster and crosses this threshold after the event has taken place. The dominant structure went from a prevailing decahedral shape before coalescence to an amorphous/glassy configuration, with the second most commonly occurring structure being FCC. The decahedral shape appeared much less frequently post aggregation.The chamber housing the MiniMACS was adapted to include two probes to measure resistance in-situ while depositing atoms using the SAS method. Fabrication of gold contacts on a suitable silicon substrate was successfully achieved, allowing for the observation of a resistance profile during creation of a percolating network of gold clusters. From here, exposure to the atom beam could be correctly terminated at a point just below the percolation threshold, allowing for the creation of atomic filament growth and potential with potential to image these in the TEM.Calibration of a larger scale MACS system was undertaken to create silver nanoparticles deposited on both copper and silver foils for use in catalysis. Lead nanoparticles were also deposited into carbon fibre paper to fabricate an electrode for use in water purification. This feat demonstrated that the MACS method is approaching rates needed for research and development in industry of the production of fuel cells, creating clusters at a rate of around 0.05 mg/cm2/hour. This fabrication also demonstrated the depth the clusters can penetrate a porous structure, with strong signal found in the top 50 μm, and then a diminished signal at around 150 – 200 μm,numbers which correlate to the carbon paper’s pore size.This project has gone beyond that of the work carried out in the literature, whereby this reports on the first instance in which clusters created with the MACS method, with clusters been fabricated at a higher temperature than before, and as butane with a matrix gas.Surface grown clusters were also discovered using readily available instruments that the MACS uses, allowing for quick switching between the two techniques. The non-mass selected nature of the clusters allowed the observation of single atoms, allowing investigations into the number of atoms surface grown clusters contain, classified by their structure.Coating carbon fibre paper with lead clusters accomplished both a proof-of-concept reactive oxygen species for water purification, as well as demonstrating a high cluster yield.Finally, instrumentation development, beam calibration and exposure recipes have lead way to large cluster coverage on the scale of percolating thing films for use of imaging filaments in a memristor device. This work has laid the foundation for further work to be developed

Predicting the amount of coke deposition on catalyst through image analysis and soft computing

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis

ZnO/Mg-Al Layered Double Hydroxides as a Photocatalytic Bleaching of Methylene Orange - A Black Box Modeling by Artificial Neural Network

The paper reports the development of ZnO-MgAl layered double hydroxides as an adsorbent-photo catalyst to remove the dye pollutants from aqueous solution and the experiments of a photocatalytic study were designed and modeled by response surface methodology (RSM) and artificial neural network (ANN). The co-precipitation and urea methods were used to synthesize the ZnO-MgAl layered double hydroxides and FT-IR, XRD and SEM analysis were done for characterization of the catalyst.The performance of the ANN model was determined and showed the efficiency of the model in comparison to the RSM method to predict the percentage of dye removal accurately with a determination coefficient (R2) of 0.968. The optimized conditions were obtained as follows: 600 oC, 120 min, 0.05 g and 20 ppm for the calcination temperature, irradiation time, catalyst amount and dye pollutant concentration, respectively.