Intelligent Neural Network with Greedy Alignment for Job-Shop Scheduling

Abstract

Abstract –Job-Shop Scheduling (JSS) processes have highly complex structure interms of many criteria. Because there is no limitation in the number of the process andthere are many alternative scheduling. In JSS, each order that is processed on differentmachines has its own process and process order. It is very important to put theseprocesses into a sequence according to a certain order. In addition, some constraintsmust be considered in order to obtain the appropriate tables.In this paper, a 3-layers Feed Forward Backpropagation Neural Network (FFBNN) hasbeen used for two different purposes, the first one task is to obtain the priority and thesecond 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 algorithmwill align operations of each job until best solution is obtained. In particular, greedytype algorithm will not always find the optimal solution. However, adding a predefinedalignment dataset along with the greedy procedure result in optimal solutions.