Investigation of Machines Sequencing Flexibility in Traditional and Flexible Manufacturing Systems (FMS) Using Genetic Algorithms (GA)

Abstract

Machine Sequencing Flexibility (MSF), a measure of alternate machine sequences in which the operations of a job can be performed, is very important especially in the context of today’s competitive manufacturing environment where responsiveness to the market determines the survival of the industries. In a scheduling scenario where N numbers of jobs are to be scheduled on M number of machines and each job has to visit each machine, the problem becomes an Open Shop problem if MSF is 100%.

In this research, a scheduling methodology, based on GA, is proposed which integrates planning (machine sequencing) with shop floor scheduling (job sequencing) assuming 100% MSF. The methodology works for both Traditional Manufacturing (TM) and FMS. It takes the machine sequences for N number of jobs as chromosomes (process plan). The chromosomes of the given number of population size are evaluated for shop floor scheduling using a heuristic to sequence the jobs for the machine loading. A solution space of the chromosomes is searched through the use of GA for the best/optimal chromosome (machines sequence) for minimization of the Makespan. Various benchmark problems have been solved through the proposed scheduling methodology in TM and FMS environments and the results have been compared with other research results. These comparisons indicate that the presented GA-based scheduling methodology has performed extremely well.