A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

• Published in: Evolutionary computation Vol. 27; no. 3; pp. 467 - 496
• Main Authors: Nguyen, Su, Mei, Yi, Xue, Bing, Zhang, Mengjie
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.09.2019; MIT Press Journals, The
• Subjects: Algorithms; Automation; Computational Biology - methods; Computer Heuristics; Computer Simulation; Dispatching rules; Genetic algorithms; Genetic programming; Heuristic job shop scheduling; Humans; hyper-heuristics; job shop; Job shop scheduling; Job shops; Machine learning; Optimization; Personnel Staffing and Scheduling - statistics & numerical data; production scheduling; Programming; Software; System effectiveness; job shop; production scheduling; Genetic programming; hyper-heuristics
• ISSN: 1063-6560; 1530-9304; 1530-9304
• DOI: 10.1162/evco_a_00230

Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.