Intelligent neural network with greedy alignment for job-shop scheduling

Abstract EN

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.