Simplifying Dispatching Rules in Genetic Programming for Dynamic Job Shop Scheduling

Sai Panda, Yi Mei, Mengjie Zhang

Abstract: Evolving dispatching rules through Genetic Programming (GP) has been shown to be successful for solving Dynamic Job Shop Scheduling (DJSS). However, the GP-evolved rules are often very large and complex, and are hard to interpret. Simplification is a promising technique that can reduce the size of GP individuals without sacrificing effectiveness. However, GP with simplification has not been studied in the context of evolving DJSS rules. This paper proposes a new GP with simplification for DJSS, and analyses its performance in evolving both effective and simple/small rules. In addition to adopting the generic algebraic simplification operators, we also developed new problem-specific numerical and behavioural simplification operators for DJSS. The results show that the proposed approach can obtain better and simpler rules than the baseline GP and existing GP algorithms with simplification. Further analysis verified the effectiveness of the newly developed numerical and simplification operators.