Open AccessSolving flow shop scheduling problem based on improved non-dominated genetic algorithm
Author(s) -
Tiankui Wang,
Chunlei Ji,
Yuanfeng Hao,
Jianhua He
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012005
Subject(s) - sorting , mathematical optimization , computer science , coding (social sciences) , flow shop scheduling , genetic algorithm , pareto principle , convergence (economics) , sorting algorithm , multi objective optimization , algorithm , chromosome , pareto optimal , scheduling (production processes) , job shop scheduling , mathematics , schedule , statistics , chemistry , economics , gene , economic growth , operating system , biochemistry
Aiming at the multi-objective problem of flow workshop problem, a multi-objective optimization model was constructed and an improved non-dominated sorting genetic algorithm was proposed. Firstly, aiming at these problems, this paper proposes a two-stage chromosome coding method to adapt to the new production scenarios. Secondly, a new adaptive method is proposed to improve the convergence speed and the superiority of Pareto solution set. Finally, simulation results show that the optimality of the improved non-dominated sorting genetic algorithm is improved greatly.