A data mining-based solution method for flow shop scheduling problems

ÖZCAN B. C., Yavuz M., FIĞLALI A.

SCIENTIA IRANICA, cilt.28, sa.2, ss.950-969, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 28 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.24200/sci.2020.50995.1957
  • Dergi Adı: SCIENTIA IRANICA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Arab World Research Source, Communication Abstracts, Compendex, Geobase, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.950-969
  • Anahtar Kelimeler: Data mining, Flow shop scheduling, Heuristic, Path relinking algorithm, Optimization, ANT-COLONY ALGORITHMS, C-I, OPTIMIZATION, MINIMIZATION, HEURISTICS
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Scheduling is the process of determining where and when to perform manufacturing measures, which is required to conduct activities in a timely, efficient, and cost-effective manner. In this paper, an algorithm is proposed as a solution to the flow shop scheduling problem which holds an important place in the scheduling literature. The path relinking algorithm and data mining are used to solve the flow shop scheduling problem studied here. While DM is used for globally searching the solution space, path relinking is used for local search. Data mining is a method for extracting the embedded information in a cluster that includes implicit information. Path relinking is an algorithm that advances by making binary displacements in order to convert the initial solution to the guiding solution and it is repeated by assigning the best obtained solution within this process to the starting point. The efficiency of the model for Taillard's flow shop scheduling problems was tested. Consequently, it is possible to solve the large-size problem without considerable mathematical background. The obtained results showed that the proposed method comparatively performed as good as other metaheuristic methods. (C) 2021 Sharif University of Technology. All rights reserved.