Online multiobjective single machine dynamic scheduling with sequence-dependent setups using simulation-based genetic algorithm with desirability function

This paper presents a simulation-based genetic algorithm with desirability function (SIMGAD) that could be used on-line for the dynamic scheduling of a single machine with sequence-dependent setups. The weights used to combine the criteria (dispatching rules) into a single rule using linear weighted aggregation is determined by genetic algorithm (GA). The GA evaluates the performance of each set of weights with discrete-event simulation that returns a fitness value after multiple performance measures (objectives) are each expressed as a desirability function and combined into a single objective function. An illustrative simulation example based on the scheduling of an ion implanter machine in wafer fabrication plant shows that SIMGAD works effectively in solving the multiobjective scheduling problem with capability of handling user preference in decision making to achieve the desired performances.