### Multi-criteria sequence-dependent jop shop schedulling using genetic algorithms

Abstract

Real world job shops have to contend with jobs due on different days, material ready times that vary, reentrant workflows and sequence-dependent
setup times. The problem is even more complex because businesses often judge solution goodness according to multiple competing criteria. Producing an
optimal solution would be time consuming to the point of rendering the result meaningless. Commonly used heuristics such as shortest processing time (
SPT) and earliest due date (EDD) can be used to calculate a feasible schedule quickly, but usually do not produce schedules that are close to optimal
in these job shop environments. We demonstrate that genetic algorithms (GA) can be used to produce solutions in times comparable to common heuristics
but closer to optimal. Changing criteria or their relative weights does not affect the running time, nor does it require programming changes.
Therefore, a GA can be easily applied and modified for a variety of production optimization criteria in a job shop environment that includes sequence-
dependent setup times.