Many-objective genetic programming for job-shop scheduling
conference contributionposted on 2021-03-31, 03:17 authored by Atiya Masood, Yi MeiYi Mei, Gang ChenGang Chen, Mengjie ZhangMengjie 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.