The trafFc control system of the MetropolitanExpressway is collecting and processing traffic data obtained from vehicle detection sensors installed along the highway. This traffic data is then offered to the users of the highway as information on required travel time, congestion and the like.Most of the sensors installed along the Metropolitan Expressway are ultrasonic sensors. However, there have been several problems associated with previous types of ultrasonic vehicle detection sensors: 1.) in tunnels and on three lane sections of the highways, previous types of sensors detected vehicles erroneously due to the reception of unnecessary reflected signals which caused a decrease in the accuracy of detection, 2.) due to the fact that there are more than about 2,000 sensors installed, and 3.) speed measurement.This report is an outline of the intelligent vehicle detection sensors (hereafrer, I Type vehicle detection sensors) introduced to solve these issues.
Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, range estimation of the parameters would not be very useful. Considering such uncertainties, finding a good situation-dependent dispatching rule would be more suitable than solving the RCPSP under uncertainties. In this paper we apply the Pitts approach, one of the genetic algorithms, to the situation-dependent dispatching rule acquisition. We compare the obtained rule with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rule in the problems with unpredictable wafer errors.
The scheduling of semiconductor wafer testing processes may be seen as a resource constraint project scheduling problem (RCPSP), but it includes uncertainties caused by wafer error, human factors, etc. Because uncertainties are not simply quantitative, estimating the range of the parameters is not useful. Considering such uncertainties, finding a good situationdependent dispatching rule is more suitable than solving an RCPSP under uncertainties. In this paper we apply machine learning approaches to acquiring situation-dependent dispatching rule. We compare obtained rules and examine their effectiveness and usefulness in problems with unpredictable wafer testing errors.
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