Active Sampling for Dynamic Job Shop Scheduling using Genetic Programming

Abstract

Dynamic job shop scheduling is an important but difficult problem in manufacturing systems which becomes complex particularly in uncertain environments with varying shop scenarios. Genetic programming based hyper-heuristics (GPHH) have been a successful approach for dynamic job shop scheduling (DJSS) problems by enabling the automated design of dispatching rules for DJSS problems. GPHH is a computationally intensive and time consuming approach. Furthermore, when complex shop scenarios are considered, it requires a large number of training instances. When faced with multiple shop scenarios and a large number of problem instances, identifying good training instances to evolve dispatching rules which perform well over diverse scenarios is of vital importance though challenging. Essentially this requires the tackling of exploration versus exploitation trade-off. To address this challenge, we propose a new framework for GPHH which incorporates active sampling of good training instances during evolutionary process. We propose a sampling algorithm based on the ϵ-greedy method to evolve a set of dispatching rules. Through our experiments, we demonstrate the ability of our framework to efficiently identify useful training instances toward evolving dispatching rules which outperform the existing training methods.