- author = "Deepak Karunakaran and Yi Mei and Gang Chen2 and Mengjie Zhang",
- title = "Active Sampling for Dynamic Job Shop Scheduling using Genetic Programming",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "434--441",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, scheduling, active learning, dispatching rules",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8789923",
- size = "8 pages",
- 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 dur",
-
notes = "also known as \cite{8789923},
IEEE Catalog Number: CFP19ICE-ART",
