An uncertain multi-objective programming model for machine scheduling problem

This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with known uncertainty distributions. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize t...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of machine learning and cybernetics Ročník 8; číslo 5; s. 1493 - 1500
Hlavní autoři: Ning, Yufu, Chen, Xiumei, Wang, Zhiyong, Li, Xiangying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017
Springer Nature B.V
Témata:
Artificial Intelligence
Completion time
Complex Systems
Computational Intelligence
Control
Engineering
Fuzzy sets
Genetic algorithms
Independent variables
Mathematical programming
Mechatronics
Multiple objective analysis
Original Article
Pattern Recognition
Random variables
Release dates
Robotics
Scheduling
Set theory
Subject specialists
Systems Biology
Machine scheduling problem
Uncertain variable
Uncertain programming
Genetic algorithm
ISSN:1868-8071, 1868-808X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with known uncertainty distributions. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize the maximum completion time or makespan, and second objective is to minimize the maximum tardiness time. A genetic algorithm is employed to solve the proposed uncertain machine scheduling model, and its efficiency is illustrated by some numerical experiments.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-016-0522-2