Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm

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 co...

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Bibliographic Details
Published in:European journal of operational research Vol. 175; no. 2; pp. 1043 - 1069
Main Authors: Kumar, Akhilesh, Prakash, Tiwari, M.K., Shankar, Ravi, Baveja, Alok
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2006
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Exact sciences and technology
Flexible manufacturing system
Flexible manufacturing systems
Genetic algorithm
Genetic algorithms
Heuristic
Machine loading
Operational research and scientific management
Operational research. Management science
Scheduling, sequencing
Studies
Genetic algorithm
Flexible manufacturing system
Machine loading
Standard test
Heuristic method
Mathematical model
Workload
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary: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.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2005.06.025