A Genetic Algorithm Based Scheduling for a Flexible System

Abstract

Based on the rapid technical development in the last decade, the automation in many areas of production and distribution has developed significantly. This leads to complex situations where decisions have to be taken within a short time and among several alternatives - often without the human intervention. This paper considers flexible system as a mixed model flexible flow line, where ‘n’ independent jobs are required to be processed on ‘m’ different machines, where all the jobs have the same processing order on the machines. Here the objective is to find the ordering of the jobs on the machines that minimizes the make-span. Above objective can be achieved through evolutionary based heuristic of Genetic Algorithm (GA) on the flow line scheduling problem. The advantage of the GA approach here is the ease with which it can handle constraints and objectives;, making it easy to adapt the GA scheduler to the particular requirements of a very wide range of possible line scheduling problem. The proposed architecture has been developed to depict the application of genetic algorithms to optimize production schedules in a flexible flow line system representing a flexible system. Results show that the implementation of the genetic algorithm is very effective as compared to standard sequencing rules like shortest processing time, total processing time, etc. and at the same time easy to use. Finally this paper intends to discuss some of these interesting results with a focus on application of lead-time reduction in an interesting flexible system like RES (Reverse Enterprise System).