Improving Memory for Solving Dynamic Job Shop Scheduling

When faced with a changing world, humans are apt to look not just to the future, but to the past. Drawing on knowledge from similar situations we have encountered helps us to decide what to do next. The more experience we’ve had with a particular situation, the better we can expect to perform. When solving dynamic problems using search, it may be enough to solve the problem completely from scratch when we encounter it again. An appropriate strategy for store past information is memory. In the researches shown that using standard memory with evolutionary algorithms for solving dynamic optimization problem is capable. Standard memory is containing infirmity point memory determinate capacity. In this paper presented a new memory namely Classifier-based memory, which solves standard memory problems. This memory combined with GA for solving dynamic scheduling. The dynamic job shop scheduling problem is one of the most complex forms of machine scheduling. Classifier-based memory is introduced to extend the use of memory to dynamic problems where solutions may become obsolete as the environment changes. Classifier-based memory creates an abstraction layer between feasible solutions and memory entries so that old solutions stored in memory may be mapped to solutions that are feasible in the current environment. The technique presented in this paper improves the ability of memories to guide search quickly and efficiently to good solutions as the environment changes.