Paper Info
Title
Artificial-Molecule-Based Chemical Reaction Optimization for Flow Shop Scheduling Problem With Deteriorating and Learning Effects
Abstract
Industry 4.0 is widely accepted to guide a novel and promising production paradigm where many advanced intelligent machines and latest technologies are utilized. The self-optimization and self-training behaviors of advanced intelligent machines make them more and more proficient when processing jobs; while the abrasion of their components reduces their work efficiency in the manufacturing process. Therefore, we address a flow shop scheduling problem with deteriorating and learning effects, where the processing time of jobs is a function of their starting time and positions in a schedule. In order to solve it efficiently, an artificial-molecule-based chemical reaction optimization algorithm is proposed. A set of artificial molecules are constructed based on some elitist solutions and adaptively injected into the population, which can enhance and balance exploration and exploitation abilities. The simulation experiments are carried out on a set of stochastic test problems with different sizes. The experimental results show that the proposed algorithm performs better than its peer algorithms in solving the investigated problem.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2911028
IEEE ACCESS
Keywords
Field
DocType
Industry 4.0,deteriorating and learning effects,flow shop scheduling,chemical reaction optimization,artificial molecules
Population,Mathematical optimization,Learning effect,Job shop scheduling,Computer science,Flow shop scheduling,Schedule,Optimization algorithm,Manufacturing process,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yaping Fu1142.54
MengChu Zhou28989534.94
Xiwang Guo311.36
Liang Qi415627.14