A genetic algorithm for scheduling job families on a single machine with arbitrary earliness/tardiness penalties and an unrestricted common due date

We propose and investigate a genetic algorithm for scheduling jobs about an unrestricted common due date on a single machine. The objective is to minimize total earliness and tardiness cost where early and tardy penalty rates are allowed to be arbitrary for each job. Jobs are classified into families and a family setup time is required between jobs from two different families. Results from a computational study are promising with close to optimal solutions obtained rather easily and quickly.