Scheduling in dual-resources constrained manufacturing systems using genetic algorithms

Presents a scheduling approach, based on genetic algorithms (GA), developed to address the scheduling problem in manufacturing systems constrained by both machines and workers. The GA algorithm utilizes a new chromosome representation, which takes into account machine and worker assignments to jobs. A study was conducted, using the proposed scheduling method to compare the performance of six dispatching rules with respect to eight performance measures for two different shop characteristics: i) dual-resources (machines and workers) constrained shop, and ii) single-resource constrained shop (machines only). An example is used for illustration. The results indicate that the dispatching rule which works best for a single-resource constrained shop is not necessarily the best rule for a dual-resources constrained system. Furthermore, it is shown that the most suitable dispatching rule depends on the selected performance criteria and the characteristics of the manufacturing system