SavedAdd your journal

Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2014 / Cilt: 16 - Sayı: 48

DJOB SHOP SCHEDULING WITH KRILL HERD ALGORITHM

KRİL SÜRÜSÜ ALGORİTMASI İLE ATÖLYE ÇİZELGELEME

İLKER GÖLCÜK
Adil BAYKASOĞLU
Selen MADENOĞLU
Pages
61–75
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study provides some idea about krill herd algorithm’s possible performance for solving other combinatorial optimization problems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Krill herd algorithm is a recently proposed swarm based metaheuristic algorithm for solving real life problems.
📊
Key Findings & Data:In the literature, the performance of the algorithm has been tested on non-linear continuous optimization problems.
💡
Conclusion & Impact:This study provides some idea about krill herd algorithm’s possible performance for solving other combinatorial optimization problems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Krill sürüsü algorithması · Atölye çizelgeleme problemi · Metasezgisel algoritmalar

Abstract

Krill herd algorithm is a recently proposed swarm based metaheuristic algorithm for solving real life problems. In the literature, the performance of the algorithm has been tested on non-linear continuous optimization problems. In this study, the performance of the krill herd algorithm is tested on job shop scheduling problems, which is one of combinatorial optimization problems, for the first time in the literature. Job shop scheduling problems are one of the most complex and important problems with representative characteristics to other hard combinatorial optimization problems. This study provides some idea about krill herd algorithm’s possible performance for solving other combinatorial optimization problems.

Özet

Kril sürüsü algoritması gerçek hayat problemlerini çözmek amacıyla yakın dönemde literatüre kazandırılmış sürü temelli metasezgisel algoritmalardar biridir. Algoritmanın performansı literatürde sürekli-sayı değişkenlere sahip doğrusal olmayan optimizasyon problemleri üzerinde denenmiştir. Bu çalışmada kril sürüsü algoritmasının performansı literatürde ilk kez kombinatoryal optimizasyon problemlerinden biri olan atölye tipi çizelgeleme problemleri üzerinde test edilmiştir. Atölye tipi çizelgeleme problemleri, diğer zor kombinatoryal optimizasyon problemlerini temsil eden önemli bir problem türü olduğundan, bu çalışma algoritmanın diğer kombinatoryal problemlerdeki olası performansı hakkında ön bilgiler vermektedir

🔖

Cite This Article

GÖLCÜK, İ., BAYKASOĞLU, A., MADENOĞLU, S. (2014). DJOB SHOP SCHEDULING WITH KRILL HERD ALGORITHM. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 16 - Sayı: 48, 61–75. https://ajindex.com/articles/213387

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. Adams J., Balas E., Zawack D. (1988): "The Shifting Bottleneck Procedure for Job Shop Scheduling", Management Science, Cilt 34, No. 3, s.391-401.
  2. Aiex R. M., Binato S., Resende M. G. C. (2003): "Parallel GRASP with Path-Relinking for Job Shop Scheduling", Parallel Computing, Cilt 29, No. 4, s.393-430.
  3. Anandaraman C. (2011): "An Improved Sheep Flock Heredity Algorithm for Job Shop Scheduling and Flow Shop Scheduling Problems", International Journal of Industrial Engineering Computations, Cilt 2, No. 4, s749-764. Baker K. R. (1974): "Introduction to Sequencing and Scheduling", New York: Wiley. Baykasoğlu A. (2002): "Linguistic-Based Meta-Heuristic Optimization Model for Flexible Job Shop Scheduling", International Journal of Production Research, Cilt 40, No. 17, 4523- 4543. Baykasoğlu A., Hamzadayı A., Köse S. Y. (2014): "Testing the Performance of Teaching– Learning Based Optimization (TLBO) Algorithm on Combinatorial Problems: Flow Shop and Job Shop Scheduling Cases", Information Sciences, Cilt 276, No. 0, s.204-218.
  4. Beasley J. E. (1990): "OR-Library: Distributing Test Problems by Electronic Mail", Journal of the Operational Research Society, Cilt 41, No. 11, s.1069-1072.
  5. Bierwirth C., Mattfeld D. C. (1999): "Production Scheduling and Rescheduling with Genetic Algorithms", Evolutionary Computation, Cilt 7, No. 1, s.1-17.
  6. Brucker P., Jurisch B., Sievers B. (1994): "A Branch and Bound Algorithm for the Job-Shop Scheduling Problem", Discrete Applied Mathematics, Cilt 49, No. 3, s.107-127.
  7. Cheng R., Gen M., Tsujimura Y. (1996): "A Tutorial Survey of Job-Shop Scheduling Problems Using Genetic Algorithms—I. Representation", Computers and Industrial Engineering, Cilt 30, No. 4, s.983-997.
  8. Fisher H., Thompson G. L. (1963): "Probabilistic Learning Combinations of Local Job Shop Scheduling Rules", Englewood Cliffs, NJ: Prentice-Hall. Gandomi A. H., Alavi A. H. (2012): "Krill Herd: A New Bio-Inspired Optimization Algorithm", Communications in Nonlinear Science and Numerical Simulation, Cilt 17, No. 12, s.4831-4845.
  9. Gen M., Cheng R. (2000): "Genetic Algorithms and Engineering Optimization", Cilt 7, John Wiley & Sons. Giffler B., Thompson G. L. (1960): "Algorithms for Solving Production-Scheduling Problems", Operations Research, Cilt 8, No. 4, s.487-503.
  10. Gonçalves J. F., Resende M. G. C. (2014): "An Extended Akers Graphical Method with a Biased Random-Key Genetic Algorithm for Job-Shop Scheduling", International Transactions in Operational Research, Cilt 21, No. 2, s.2