A Block - Based Improved NSGA - II Algorithm for Solving Multi - Objective Permutation Flow Shop Scheduling Problem

DOI

10.2991/cecs-18.2018.38How to use a DOI?

Keywords

permutation flow shop scheduling problem; multi-objective optimization; NSGA-II; artificial chromosome.

Abstract

Aiming at the problem of minimizing the maximum makespan and minimizing the delay time of multi-objective permutation flow shop scheduling problem (MOPFSP), a block-based artificial chromosome non-dominated sorting genetic algorithm II (NSGA-II) is proposed. The algorithm combines the stochastic mechanism and the opposition-based learning mechanism to generate the initial solution to balance the diversity and quality of the initial population. An elite population is generated through the operation of several generations of NSGA-II, and a location matrix and a dependency matrix are established for the elite population by using ant information density. Based on the two matrix mining blocks, the blocks and non-blocks are recombined to form artificial chromosomes.The algorithm will be tested by Reeves instance and Taillard instance, and compared with the results obtained by other algorithms to verify the effectiveness of the algorithm.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xiaobing Pei
AU  - Chunhua Zhang
PY  - 2018/07
DA  - 2018/07
TI  - A Block - Based Improved NSGA - II Algorithm for Solving Multi - Objective Permutation Flow Shop Scheduling Problem
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 217
EP  - 226
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.38
DO  - 10.2991/cecs-18.2018.38
ID  - Pei2018/07
ER  -