ACADSTAFF UGM

CREATION
Title : Multi-Operator Hybrid Genetic Algorithm-Simulated Annealing for Reentrant Permutation Flow-Shop Scheduling
Author :

Ir. Achmad Pratama Rifai, S.T, M.Eng, Ph.D (1) PUTRI ADRIANI K (2) Setyo Tri Windras Mara, S.T., M.Sc., M.B.A. (3) Ir. Rachmadi Norcahyo, S.T., M.T (4) Siti Zawiah Md Dawal (5)

Date : 0 2021
Keyword : Genetic algorithm, Hybrid algorithm, Multiple operators Reentrant permutation flow- shop, Simulated annealing. Genetic algorithm, Hybrid algorithm, Multiple operators Reentrant permutation flow- shop, Simulated annealing.
Abstract : This study develops an improved hybrid genetic algorithm-simulated annealing (IGASA) algorithm to solve the reentrant flow-shop scheduling problem with permutation characteristics. The reentrant permutation flow-shop (RPFS) allows the jobs to visit certain machines more than once and has been proven to be an -hard problem. The proposed improved hybrid algorithm integrates the simulated annealing (SA) and genetic algorithm (GA) to obtain the near-optimal solutions by considering three objectives: minimizing the makespan, the average completion time, and total tardiness. The multi-operator mechanism is proposed for the crossover and mutation operations to improve and maintain the diversity of individuals throughout the generation. The effectiveness and robustness of the proposed method are examined in the data sets of various-sized instances with different degrees of complexity. The results highlight that the proposed hybrid algorithm is a promising alternative in solving the RPFS scheduling problem.
Group of Knowledge : Teknik Industri
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 16875-Article Text-51444-1-10-20210411 (1).pdf
Document Type : Bukti Published
Bukti Published View
2 form-L1-permohonan_penghargaan-karya-ilmiah-sudah-terbit-2021.pdf
Document Type :
View
3 Paper 3.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
4 Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time (1).pdf
Document Type : [PAK] Cek Similarity
[PAK] Cek Similarity View
5 1_merged.pdf
Document Type : [PAK] Bukti Korespondensi Penulis
[PAK] Bukti Korespondensi Penulis View
6 Multi-operator hybrid genetic algorithm-simulated annealing for reentrant permutation flow-shop scheduling.pdf
Document Type : [PAK] Cek Similarity
[PAK] Cek Similarity View