logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines

delete2003-06-01
delete200
PRE
AI
J
Jeffery K. Cochran *
S
Shwu-Min Horng
F
Fowler, JW
DOI：10.1016/S0305-0548(02)00059-Xdelete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this paper we propose a two-stage multi-population genetic algorithm (MPGA) to solve parallel machine scheduling problems with multiple objectives. In the first stage, multiple objectives are combined via the multiplication of the relative measure of each objective. Solutions of the first stage are arranged into several sub-populations, which become the initial populations of the second stage. Each sub-population then evolves separately while an elitist strategy preserves the best individuals of each objective and the best individual of the combined objective. This approach is applied in parallel machine scheduling problems with two objectives: makespan and total weighted tardiness (TWT). The MPGA is compared with a benchmark method, the multi-objective genetic algorithm (MOGA), and shows better results for all of the objectives over a wide range of problems. The MPGA is extended to scheduling problems with three objectives: makespan, TWT, and total weighted completion times (TWC), and also performs better than MOGA.
Keywords:
genetic algorithms
multiple objectives
parallel machine scheduling
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

C
Computers and Operations Research
IF:
4.3
Papers:
6.5K
Citations:
1.8W

Organization

No organization information available