Paper Info
Title
Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm
Abstract
Machine-loading problem of a flexible manufacturing system is known for its complexity. This problem encompasses various types of flexibility aspects pertaining to part selection and operation assignments along with constraints ranging from simple algebraic to potentially very complex conditional constraints. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to constraint violations and large number of generations. This paper extends the simple genetic algorithm and proposes a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. To achieve this aim, three new genetic operators—constraint based: initialization, crossover, and mutation are introduced. The methodology developed here helps avoid getting trapped at local minima. The application of the algorithm is tested on standard data sets and its superiority is demonstrated. The solution approach is illustrated by a simple example and the robustness of the algorithm is tested on five well-known functions.
Year
DOI
Venue
2006
10.1016/j.ejor.2005.06.025
European Journal of Operational Research
Keywords
Field
DocType
Genetic algorithm,Flexible manufacturing system,Machine loading
Mathematical optimization,Crossover,Algorithm,Robustness (computer science),Constraint satisfaction dual problem,Heuristics,Flexible manufacturing system,Initialization,Mathematics,Genetic algorithm,Difference-map algorithm
Journal
Volume
Issue
ISSN
175
2
0377-2217
Citations 
PageRank 
References 
12
1.12
15
Authors
5
Name
Order
Citations
PageRank
Akhilesh Kumar115714.15
Prakash2664.84
M. K. Tiwari31240115.22
R. Shankar4110496.32
Alok Baveja512011.99