- author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
- title = "Can Stochastic Dispatching Rules Evolved by Genetic Programming Hyper-heuristics Help in Dynamic Flexible Job Shop Scheduling?",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "41--48",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790030",
- size = "8 pages",
- abstract = "Dynamic flexible job shop scheduling (DFJSS) considers making machine assignment and operation sequencing decisions simultaneously with dynamic events. Genetic programming hyper-heuristics (GPHH) have been successfully applied to evolving dispatching rules for DFJSS. However, existing studies mainly focus on evolving deterministic dispatching rules, which calculate priority values for the candidate machines or jobs and select the one with the best priority. Inspired by the effectiveness of training stochastic policies in reinforcement learning, and the fact that a dispatching rule in DFJSS is similar to a policy in reinforcement learning, we investigate the effectiveness of evolving stochastic dispatching rules for DFJSS in this paper. Instead of using the winner-takes-all mechanism, we define a range of probability distributions based on the priority values of the candidates to be used by the stochastic dispatching rules. These distributions introduce varying degrees of randomness.",
-
notes = "also known as \cite{8790030},
IEEE Catalog Number: CFP19ICE-ART",
