This paper presents a genetic algorithm to solve the hybrid flow shop scheduling problem to minimize the total tardiness. Practical assumptions as unrelated machines and machine eligibility are considered. The proposed algorithm incorporates a new decoding method developed for total tardiness objective, which is able to obtain tight schedule meanwhile guarantee the influence of the chromosome on the schedule. The proposed algorithm has been calibrated with a full factorial design of experiment, and compared to several calibrated state-of-art algorithms on 450 instances with different size and correlation patterns of operation processing time. The results validate the effectiveness of the proposed algorithm.

A genetic algorithm for the hybrid flow shop scheduling with unrelated machines and machine eligibility

YU, CHUNLONG;Semeraro, Quirico;Matta, Andrea
2018-01-01

Abstract

This paper presents a genetic algorithm to solve the hybrid flow shop scheduling problem to minimize the total tardiness. Practical assumptions as unrelated machines and machine eligibility are considered. The proposed algorithm incorporates a new decoding method developed for total tardiness objective, which is able to obtain tight schedule meanwhile guarantee the influence of the chromosome on the schedule. The proposed algorithm has been calibrated with a full factorial design of experiment, and compared to several calibrated state-of-art algorithms on 450 instances with different size and correlation patterns of operation processing time. The results validate the effectiveness of the proposed algorithm.
2018
Genetic algorithm; Hybrid flow shop; Scheduling; Computer Science (all); Modeling and Simulation; Management Science and Operations Research
File in questo prodotto:
File Dimensione Formato  
Yu et al. 2018.pdf

Accesso riservato

: Publisher’s version
Dimensione 4.25 MB
Formato Adobe PDF
4.25 MB Adobe PDF   Visualizza/Apri
HFS_COR_3rd.pdf

Open Access dal 02/08/2021

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 3.92 MB
Formato Adobe PDF
3.92 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1059564
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 101
  • ???jsp.display-item.citation.isi??? 78
social impact