15 © 2007, Global Institute of Flexible Systems Management Introduction The rapid advancement of technology is resulting in shortened life cycles and putting great pressure on organizations to achieve shorter and shorter manufacturing lead times. Hence, one of the important focuses of manufacturing enterprises is to find ways and means of reducing the manufacturing lead times to be able to respond quickly for changing market demands. Towards this end, scheduling has been always been considered as one of the most important issues for lead time reduction in a mixed model flexible system. Due to this complexity of scheduling problems an efficient and effective advanced scheduling approach is in great demand. Considering a mixed model flow shop scheduling problem, where ‘n’ independent jobs required to be processed on ‘m’ different machines, as a case representing a flexible system. Flow shop scheduling has been proved to be NP-hard in strong sense [Garey et. al, 1979]. Till now, mathematical programming, constructive heuristics and meta-heuristics have been proposed for a generic flow shop scheduling [Reeves, 1995; Ogbu, 1990; Wang et. al, 2003, Chan et. al 2005], where it is usually assumed that the buffer size between every two successive machines is infinite. However, in real practice, the A Genetic Algorithm Based Scheduling for a Flexible System S. Wadhwa Department of Mechanical Engineering Indian Institute of Technology Delhi, New Delhi, India J. Madaan Department of Mechanical Engineering Indian Institute of Technology Delhi, New Delhi, India R. Raina Department of Mechanical Engineering Indian Institute of Technology Delhi, New Delhi, India 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). Keywords: flexible system, flexible flow line, genetic algorithm, reverse enterprise system, simulator petrochemical processing industries, supply chains system with returns (reverse logistics) and electronic chip manufacturing system can be considered as example of system where buffer is non-existent. In such case, a job is called blocked if its operation on a certain machine has finished but the machine after that is still busy. In these situations GA approach is the easiest because it can handle variety of constraints and objectives of flexible systems; all such cases can be handled as weighted components of the fitness function, making it easy to adapt the GA scheduler to the particular requirements of a very wide range of possible overall objectives of generic flow shop scheduling. A number of knowledge-based system (KBS) techniques for reconfiguring flexible systems have been well proposed in the literature. Wadhwa & Brown (1989) had proposed potential of basic Petri net concepts for graphic representation, specification purposes, and control of flow in a flexible system. Since these KBS technique rely heavily on past experience. The required information sometimes may not be available or difficult to be obtained. Hence genetic algorithm (GA) is well a suited tool to tackle such complex reconfiguration problems, because GA uses information- randomized (genes) exchange to exploit and tackle complex giftjourn@l Global Journal of Flexible Systems Management 2007, Vol. 8, No. 3, pp 15-24 Model