- author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
- title = "Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "1366--1373",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
DOI:10.1109/CEC.2019.8790112",
- abstract = "Dynamic flexible job shop scheduling (DFJSS) is one of the well-known combinational optimisation problems, which aims to handle machine assignment (routing) and operation sequencing (sequencing) simultaneously in dynamic environment. Genetic programming, as a hyper-heuristic method, has been successfully applied to evolve the routing and sequencing rules for DFJSS, and achieved promising results. In the actual production process, it is necessary to get a balance between several objectives instead of simply focusing only one objective. No existing study considered solving multi-objective DFJSS using genetic programming. In order to capture multi-objective nature of job shop scheduling and provide different trade-offs between conflicting objectives, in this paper, two well-known multi-objective optimisation frameworks, i.e. non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2), are incorporated into the genetic programming hyper-heuristic method to solve the multi-objective DFJSS problem. Experimental results show that the strategy of NSGA-II incorporated into genetic programming hyper-heuristic performs better than SPEA2-based GPHH, as well as the weighted sum approaches, in the perspective of both training performance and generalisation.",
-
notes = "also known as \cite{8790112},
INSPEC Accession Number: 18888605 IEEE Catalog Number: CFP19ICE-ART",
