We use the genetic algorithm (GA) technique to handle two problems in manufacturing systems: (i) the formation of manufacturing cells in cellular manufacturing and (ii) batch scheduling. In the formation of machine cells, we use multi-objective functions as criteria to form the cells. These criteria are to minimise the inter-cell movement, to minimise the variation of workload within the cells and to maximise the similarity within the cells. Unlike traditional methods, which merely rearrange the part-incidence matrix, this algorithm incorporates other parameters, such as the processing times of each part and the number of parts required. The batch scheduling problem described in this paper is the problem of scheduling a single machine with jobs of different due dates and arrival times. We have developed an algorithm which is not only able to find the optimal or near-optimal job sequence, but is also able to determine the number of jobs to be processed in each batch. The effectiveness of two types of crossover and mutation operators, the position-based and order-based operators, are evaluated. Two different objective functions are used to minimise the completion times and the total tardiness, respectively.
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