Paper Info
Title
A discrete gravitational search algorithm for the blocking flow shop problem with total flow time minimization
Abstract
The blocking flow shop problem (BFSP) is one of the key models in the flow shop scheduling problem in the manufacturing systems. Gravitational Search Algorithm (GSA) is an algorithm based on the population for solving various optimization problems. However, GSA is scarcely applied to solve the BFSP as it is designed to solve the continuous problems. In this paper, a Discrete Gravitational Search Algorithm (DGSA) is presented for solving the BFSP with the total flow time minimization. A new variable profile fitting (VPF) combined with NEH heuristic, named VPF _ NEH(n), is introduced for balancing the quality and the diversity of the initial population to configure the DGSA. The three operators including the variable neighborhood operators (VNO), the path relinking and the plus operator are implemented during the location updating of the candidates. The objective of the operation is to prevent the premature convergence of the population and to balance the exploration and exploitation in the process of optimization. The expected runtime of the DGSA is analyzed by the level-based theorem. The simulated results indicate that the effectiveness and superiority of the DGSA.
Year
DOI
Venue
2019
10.1007/s10489-019-01457-w
Applied Intelligence
Keywords
Field
DocType
Gravitational search algorithm, Blocking flow shop problem, Total flow time, Constructive heuristic, Variable neighborhood search
Population,Mathematical optimization,Heuristic,Variable neighborhood search,Premature convergence,Computer science,Flow shop scheduling,Minification,Operator (computer programming),Artificial intelligence,Optimization problem,Machine learning
Journal
Volume
Issue
ISSN
49
9
0924-669X
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Fuqing Zhao112922.63
Feilong Xue251.06
Yi Zhang340077.93
Weimin Ma442726.76
Chuck Zhang511715.72
Houbin Song6122.19