role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm

Flexible manufacturing system (fms) readily addresses the dynamic needs of the customers in terms of variety and quality. at present, there is a need to produce a wide range of quality products in limited time span. on-time delivery of customers’ orders is critical in make-to-order (mto) manufacturing systems. the completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. among those, batch size is a significant construct for effective scheduling of an fms, as it directly affects completion time. on the other hand, constant batch size makes mto less responsive to customers’ demands. in this paper, an fms scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. the effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. a mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. scheduling of an fms is not only a combinatorial optimization problem but also np-hard problem. suitable solutions of such problems through exact methods are difficult. hence, a meta-heuristic genetic algorithm is used to optimize scheduling of the fms.