Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study

Mitsuo Gen, John R. Cheng, Krisanarach Nitisiri, Hayato Ohwada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Scheduling is one of the very important tools for treating a complex combinatorial optimization problem (COP) model, where it can have a major impact on the productivity of a manufacturing process. The most well known models of scheduling are confirmed as NP-hard or NP-complete problems. The aim of scheduling is to find a schedule with the best performance through selecting resources for each operation, the sequence for each resource and the beginning time. Genetic algorithm is one of the most efficient methods among metaheuristics for solving the real-world manufacturing problems. In this paper we firstly survey the literature on genetic algorithms (GAs) with GPU acceleration. A parallel multiobjective GA (MoGA) acceleration with CUDA (Compute Unified Device Architecture) will be introduced. A parallel hybrid multiobjective GA with learning is introduced through a real-world case study of the train scheduling problem and numerical experiments on GPU for multiobjective GA approaches are also demonstrated.

Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Management Science and Engineering Management, ICMSEM 2020 - Volume 1
EditorsJiuping Xu, Gheorghe Duca, Syed Ejaz Ahmed, Fausto Pedro García Márquez, Asaf Hajiyev
PublisherSpringer
Pages322-339
Number of pages18
ISBN (Print)9783030498283
DOIs
Publication statusPublished - 2020
Event14th International Conference on Management Science and Engineering Management, ICMSEM 2020 - Chisinau, Moldova, Republic of
Duration: 30 Jul 20202 Aug 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1190 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference14th International Conference on Management Science and Engineering Management, ICMSEM 2020
CountryMoldova, Republic of
CityChisinau
Period30/07/202/08/20

Keywords

  • Graphics Processing Units (GPUs)
  • Machine Learning (ML)
  • Multiobjective Genetic Algorithm (MoGA)
  • Train scheduling

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  • Cite this

    Gen, M., Cheng, J. R., Nitisiri, K., & Ohwada, H. (2020). Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study. In J. Xu, G. Duca, S. E. Ahmed, F. P. García Márquez, & A. Hajiyev (Eds.), Proceedings of the 14th International Conference on Management Science and Engineering Management, ICMSEM 2020 - Volume 1 (pp. 322-339). (Advances in Intelligent Systems and Computing; Vol. 1190 AISC). Springer. https://doi.org/10.1007/978-3-030-49829-0_24