Abstract: This paper presents a subjective job scheduler based
on a 3-layer Backpropagation Neural Network (BPNN) and a greedy
alignment procedure in order formulates a real-life situation. The
BPNN estimates critical values of jobs based on the given subjective
criteria. The scheduler is formulated in such a way that, at each time
period, the most critical job is selected from the job queue and is
transferred into a single machine before the next periodic job arrives.
If the selected job is one of the oldest jobs in the queue and its
deadline is less than that of the arrival time of the current job, then
there is an update of the deadline of the job is assigned in order to
prevent the critical job from its elimination. The proposed
satisfiability criteria indicates that the satisfaction of the scheduler
with respect to performance of the BPNN, validity of the jobs and the
feasibility of the scheduler.
Abstract: A subjectively influenced router for vehicles in a fourjunction
traffic system is presented. The router is based on a 3-layer
Backpropagation Neural Network (BPNN) and a greedy routing
procedure. The BPNN detects priorities of vehicles based on the
subjective criteria. The subjective criteria and the routing procedure
depend on the routing plan towards vehicles depending on the user.
The routing procedure selects vehicles from their junctions based on
their priorities and route them concurrently to the traffic system. That
is, when the router is provided with a desired vehicles selection
criteria and routing procedure, it routes vehicles with a reasonable
junction clearing time. The cost evaluation of the router determines
its efficiency. In the case of a routing conflict, the router will route
the vehicles in a consecutive order and quarantine faulty vehicles.
The simulations presented indicate that the presented approach is an
effective strategy of structuring a subjective vehicle router.