Parameter setting in a bio-inspired model for dynamic flexible job shop scheduling with sequence-dependent setups

This paper addresses the parameter modelling and optimisation issues in the application of a bio-inspired model for the scheduling of dynamic flexible job shop with sequence-dependent setups. The study sets the model parameters using two steps, termed mapping and tuning. Mapping establishes a set of coefficients to link the model parameters with the scheduling problem characteristics. Tuning determines good values of these coefficients and is then used to compute the model parameters. Such a tuning procedure is accomplished by extensive computational experiments and statistical analyses. A data set from semiconductor manufacturing was used to show the effectiveness of the parameter setting approach. The performance of the proposed multiagent model was compared with that of another scheduling method which is based on dispatching rules. It is concluded that the proposed parameter setting method is effective and worth considering when applying bio-inspired division of labour to dynamic manufacturing scheduling. [Received 5 October 2006; Revised 2 January 2007; Accepted 1 March 2007]