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

Expert System-Based Exploratory Approach to Cost Modeling of Reinforced Concrete Office Building

Expert system is a conventional method that is in use in cost modeling, considering its advantage over traditional regression method. It is based on this fact, that this study aimed at deploying neural network in cost modeling of reinforced concrete office building. One hundred (100) samples were selected at random and divided into two; one part was used to develop network algorithm while the second part was used for model validation. Neural network was used to generate the model algorithm; the model is divided into 3 modules: the data optimization module, criteria selection with initializing and terminating modules. Regression analysis was carried out and model validated with Jackknife re-sampling technique. The colinearity analysis indicates high level of tolerance and -0.07403 lowest variation prediction quotients to 0.66639 highest variation quotients. Also the Regression coefficient (R-square) value for determining the model fitness is 0.034 with standard error of 0.048 this attest to the fitness of the model generated. The model is flexible in accommodating new data and variables, thus, it allows for regular updating

Image Type Water Meter Character Recognition Based on Embedded DSP

In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition. We collected water meter image through camera at a fixed angle, and the projection method is used to recognize those digital images. The experiment results show that the method can recognize the meter characters accurately and artificial meter reading is replaced by automatic digital recognition, which improves working efficiency