Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling

Abstract

Dynamic flexible job shop scheduling has attracted widespread interest from scholars and industries due to its practical value. Genetic programming hyper-heuristic has achieved great success in automatically evolving effective scheduling heuristics to make real-time decisions (i.e., operation ordering and machine assignment) for dynamic flexible job shop scheduling. The design of the training set and fitness evaluation play key roles in improving the generalisation of the evolved scheduling heuristics. The commonly used strategies for improving the generalisation of learned scheduling heuristics include using multiple instances for evaluation at each generation or using a single instance but changing the instance at each new generation of the training process of genetic programming. However, using multiple instances is time-consuming, while changing a single instance at each new generation, potentially promising individuals that happen to underperform in one particular generation might be lost. To address this issue, this paper develops a genetic programming method with a multi-case fitness evaluation strategy, which is named GPMF to evolve the scheduling heuristics with better generalisation ability for the dynamic flexible job shop scheduling problem. The proposed multi-case fitness evaluation strategy divides one instance into multiple cases and uses the average value of the multi-case objectives as the fitness. Experimental results show that the proposed GPMF algorithm is significantly better than the baseline method in all the tested scenarios.