Intelligent Neural Network with Greedy Alignment for Job-Shop Scheduling
IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING,
2015, Volume 15, Issue 3, Pages 11-24
AbstractAbstract –Job-Shop Scheduling (JSS) processes have highly complex structure in
terms of many criteria. Because there is no limitation in the number of the process and
there are many alternative scheduling. In JSS, each order that is processed on different
machines has its own process and process order. It is very important to put these
processes into a sequence according to a certain order. In addition, some constraints
must be considered in order to obtain the appropriate tables.
In this paper, a 3-layers Feed Forward Backpropagation Neural Network (FFBNN) has
been used for two different purposes, the first one task is to obtain the priority and the
second one role is to determine the starting order of each operation within a job.
Precedence order of operations indicates the dependency of subtasks within a job,
Furthermore, the combined greedy procedure along with the back propagation algorithm
will align operations of each job until best solution is obtained. In particular, greedy
type algorithm will not always find the optimal solution. However, adding a predefined
alignment dataset along with the greedy procedure result in optimal solutions.
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