A NEW HYBRID MODEL FOR SOLVING FLOW SHOP SCHEDULING AND JOB SHOP SCHEDULING PROBLEM BY DISTRIBUTED GRID COMPUTING AND ARTIFICIAL NEURAL NETWORK

Abstract

Optimization methods are often used in different kinds of optimization problems that allow the minimization or maximization of definite objective functions. Job shop scheduling problem (JSSP) and flow shop scheduling problem (FSSP) is an optimization problem in operation research and computer science. The main difference between JSSP and FSSP is that a fixed linear structure follows in a manufacturing process for FSSP, whereas the routing of each job can be individual in JSSP, which means that all sequencing need to be manufactured differently on a certain part of the same machines or we can say that in the same machines. The main aim of these types of scheduling problem is to find out the makespan as least as possible. To achieve the optimum solutions for this kind of scheduling problems it is required to apply such heuristic methods which we have applied in the presented paper. This research work proposes the use of artificial neural network (ANN) in grid computing to minimize the makespan in JSSP and FSSP.