Masood 2016 Many-objective genetic programming for job-shop scheduling.pdf (231.54 kB)

Many-objective genetic programming for job-shop scheduling

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conference contribution
posted on 31.03.2021, 03:17 by Atiya Masood, Yi Mei, Gang Chen, Mengjie Zhang
In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective JSS with two or three objectives, not to mention the many-objective JSS with more than three objectives. In this paper, we investigate the many-objective JSS, which takes all the objectives into account. On the other hand, dispatching rules have been widely used in JSS due to its flexibility, scalability and quick response in dynamic environment. In this paper, we focus on evolving a set of trade-off dispatching rules for many-objective JSS, which can generate non-dominated schedules given any unseen instance. To this end, a new hybridized algorithm that combines Genetic Programming (GP) and NSGA-III is proposed. The experimental results demonstrates the efficacy of the newly proposed algorithm on the tested job-shop benchmark instances. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

History

Preferred citation

Masood, A., Mei, Y., Chen, G. & Zhang, M. (2016, January). Many-objective genetic programming for job-shop scheduling. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE (pp. 209-216). IEEE. https://doi.org/10.1109/CEC.2016.7743797

Conference name

2016 IEEE Congress on Evolutionary Computation (CEC)

Conference Place

IEEE

Conference start date

24/07/2016

Conference finish date

29/07/2016

Title of proceedings

Proceedings of the IEEE Congress on Evolutionary Computation (CEC)

Series

IEEE Congress on Evolutionary Computation

Contribution type

Published Paper

Publication or Presentation Year

01/01/2016

Pagination

209-216

Publisher

IEEE

Publication status

Published

Exports