• Home
  • About
  • Policies
  • Contact
    • Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
Advanced Search
View Item 
  •   Home
  • İktisadi İdari Bilimler Fakültesi / Faculty of Economics and Administrative Sciences
  • İşletme / Business Administration
  • Makaleler / Articles
  • View Item
  •   Home
  • İktisadi İdari Bilimler Fakültesi / Faculty of Economics and Administrative Sciences
  • İşletme / Business Administration
  • Makaleler / Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Particle swarm optimization and differential evolution for the single machine total weighted tardiness problem

Thumbnail
Author
Taşgetiren, M. Fatih
Liang, Yun-Chia
Şevkli, Mehmet
Gençyılmaz, Güneş
Type
Article
Date
2006-11-15
Language
en_US
Metadata
Show full item record
Abstract
In this paper we present two recent metaheuristics, particle swarm optimization and differential evolution algorithms, to solve the single machine total weighted tardiness problem, which is a typical discrete combinatorial optimization problem. Most of the literature on both algorithms is concerned with continuous optimization problems, while a few deal with discrete combinatorial optimization problems. A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems. The performance of these two recent population based algorithms is evaluated on widely used benchmarks from the OR library. The computational results show that both algorithms show promise in solving permutation problems. In addition, a simple but very efficient local search method based on the variable neighbourhood search (VNS) is embedded in both algorithms to improve the solution quality and the computational efficiency. Ultimately, all the best known or optimal solutions of instances are found by the VNS version of both algorithms.
Subject
particle swarm optimization
differential evolution
total weighted tardiness
single machine scheduling problem
evolutionary algorithms
scheduling problem
sequencing problems
bound algorithm
search
branch
neighborhood
minimize
costs
job
parçacık sürüsü optimizasyonu
diferansiyel evrim
toplam ağırlıklı gecikme
tek makine çizelgeleme problemi
evrimsel algoritmalar
zamanlama sorunu
sıralama problemleri
sınır algoritması
arama
şube
komşuluk
maliyetler
iş
URI
http://hdl.handle.net/11413/975
Collections
  • Makaleler / Articles [49]
  • Scopus Publications [724]
  • WoS Publications [1016]

İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS

İKU Kütüphane


Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeLanguageBy PublisherRightsPubmedScopusWoSThis CollectionBy Issue DateAuthorsTitlesSubjectsTypeLanguageBy PublisherRightsPubmedScopusWoS

My Account

Login

İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS