- author = "Deepak Karunakaran and Yi Mei and Gang Chen2 and Mengjie Zhang",
- title = "Sampling Heuristics for Multi-Objective Dynamic Job Shop Scheduling Using Island Based Parallel Genetic Programming",
- booktitle = "15th International Conference on Parallel Problem Solving from Nature",
- year = "2018",
- editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley",
- volume = "11102",
- series = "LNCS",
- pages = "347--359",
- address = "Coimbra, Portugal",
- month = "8-12 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Scheduling, Parallel algorithms",
- isbn13 = "978-3-319-99258-7",
-
URL = "
https://www.springer.com/gp/book/9783319992587",
-
DOI = "
doi:10.1007/978-3-319-99259-4_28",
- abstract = "Dynamic job shop scheduling is a complex problem in production systems. Automated design of dispatching rules for these systems, particularly using the genetic programming based hyper-heuristics (GPHH) has been a promising approach in recent years. However, GPHH is a computationally intensive and time consuming approach. Parallel evolutionary algorithms are one of the key approaches to tackle this drawback. Furthermore when scheduling is performed under uncertain manufacturing environments while considering multiple conflicting objectives, evolving good rules requires large and diverse training instances. Under limited time and computational budget training on all instances is not possible. Therefore, we need an efficient way to decide which training samples are more suitable for training. We propose a method to sample those problem instances which have the potential to promote the evolution of good rules. In particular, a sampling heuristic which successively rejects clusters of problem instances in favour of those problem instances which show potential in improving the Pareto front for a dynamic multi-objective scheduling problem is developed. We exploit the efficient island model-based approaches to simultaneously consider multiple training instances for GPHH.",
-
notes = "PPSN2018 http://ppsn2018.dei.uc.pt
This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018",
