The designers and manufacturers of Flexible Manufacturing Systems (FMS) strive to ensure maximum flexibility in terms of design, planning, scheduling and control in the system. In practice, it is hard to accommodate frequent variations in the part designs of incoming jobs during the implementation of such manufacturing systems. The variation in job design can be efficiently overcome by scheduling a variety of incoming parts into the system. In this paper, an appropriate heuristic-based scheduling mechanism is designed and developed to generate a nearer to optimum schedule, using Genetic Algorithm (GA). GA is used in solving optimization problems in view of its characteristic of high efficiency and being fit for practical application. A GA based on machine code is developed and presented for minimizing the makespan in an identical machine scheduling problem. The GA developed is efficient and advantageous for scheduling scale identical parallel machine manufacturing systems for minimizing the makespan, which is demonstrated through numerical solutions. The quality of its solution is better suited over heuristic and Simulated Annealing (SA) algorithms.

The automation of job shop can pave the way for producing a variety of jobs in a produce-to-order environment. The automation of a flexible job shop is achieved with the integration of computer control machines, automatic material handling and inspection systems and automatic tool changes having large magazines capable of holding a variety of tools. The technological breakthrough in manufacturing with the application of computers and precise controls has led to the development of such systems. This paper deals with a Flexible Manufacturing System (FMS), that can achieve both flexibility and productivity. It is this type of system which has emerged in manufacturing to compete in the increasingly competitive global market.

Determining an efficient schedule for the general job shop problem has been the subject of research for more than 50 years. For n jobs and m machines in the general case, there will be (n!)^m feasible sequences (Taglia and Santochi, 1993). The evolution of Computer Integrated Manufacturing (CIM) has complicated the issue because of its complex nature of working. Scheduling problems are known to be complex even for simple formulations and are Nondeterministic Polynomials (NP) - hard in many cases. The NP is the class of decision problems that can be solved by NP algorithms. The automatic generation of scheduling plans for job shops is traditionally addressed using optimization and approximation approaches (Li and She, 1995). The algorithms used for solving optimization problems are mainly categorized into two techniques: mathematical programming techniques and enumerative programming techniques. The techniques using approximation techniques are: implicit enumeration or Branch and Bound (BB) technique; decomposition or lagrangian relaxation; priority rule-based heuristics; local search algorithms (iterative search, Simulated Annealing (SA), threshold annealing and Tabu-search); evolutionary programs, like Genetic Algorithms (GA); and artificial intelligence techniques (knowledge-based and expert systems).

Operations Management Journal, Genetic Algorithm, Optimization Technique, Flexible Manufacturing Systems, FMS, Simulated Annealing algorithms, Computer Integrated Manufacturing, CIM, Branch and Bound technique, Nondeterministic Polynomials, Giffler and Thompson algorithm.