Abstract
Dynamic job shop scheduling (DJSS) has been shown as a realistic and complex combinatorial optimization problem, which is characterized by complexity, dynamics, and uncertainty. Though dispatching rules (DRs) have been seen as a suitable method for solving DJSS problem, it is hard to manually design a DR with good scheduling performance considering all the aspects, much less a general DR for the complex dynamic environment of the job shop. This paper presents a genetic programming hyper-heuristic (GPHH) based DR evaluation approach to automatically generate customized DRs, in which job shop configuration, objective, and other information are considered. After testing it on the single objective DJSS problems with six different scenarios, the experimental result indicates that the proposed method can effectively evolve better DRs for different DJSS problems than manually designed DRs. Besides, the role of four key parameters in GPHH, including the number of generations, the population size, and the maximal depth, have been deeply analyzed based on the corresponding experiments.
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