A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties

In this paper, a hybrid genetic algorithm is developed to solve the single machine scheduling problem with the objective to minimize the weighted sum of earliness and tardiness costs. First, dominance properties of (the conditions on) the optimal schedule are developed based on the switching of two adjacent jobs i and j. These dominance properties are only necessary conditions and not sufficient conditions for any given schedule to be optimal. Therefore, these dominance properties are further embedded in the genetic algorithm and we call it genetic algorithm with dominance properties (GADP). This GADP is a hybrid genetic algorithm. The initial populations of schedules in the genetic algorithm are generated using these dominance\ud properties. GA can further improve the performance of these initial solutions after the evolving procedures. The performances of hybrid genetic algorithm (GADP) have been compared with simple genetic algorithm (SGA) using benchmark\ud instances. It is shown that this hybrid genetic algorithm (GADP) performs very well when compared with DP or SGA alone