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Civil-Comp Proceedings
ISSN 1759-3433 CCP: 84
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping, G. Montero and R. Montenegro
Paper 49
Application of Genetic Algorithms for Balancing Assembly Lines J. Zurek, O. Ciszak and R. Staniek
Division of Technology Planning, Institute of Mechanical Technology, Faculty of Mechanical Engineering and Management, Poznan University of Technology, Poland J. Zurek, O. Ciszak, R. Staniek, "Application of Genetic Algorithms for Balancing Assembly Lines", in B.H.V. Topping, G. Montero, R. Montenegro, (Editors), "Proceedings of the Fifth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 49, 2006. doi:10.4203/ccp.84.49
Keywords: assembly, assembly automatisation, assembly line balancing, genetic algorithms.Summary
Assembly line balancing is a task that is very difficult using optimization methods. This is
caused by high rates of production on the automatic lines where it is possible to
obtain high savings without investment. Only organization
changes are required.
The basic parameter of this task is the finite set of operations . For each operation there is an assigned manufacturing time as well as a graph of sequence constraints showing the demand sequence of performance in relation to each other [2]. This graph can be presented in graphical form or can be coded as a file, a table or a dynamic structure. The solution of the task is the division of the operation set into subdivisions , preserving their minimal number and as the same time the following condition must be fulfilled:
where T is defined as the production pace of the line.
In order to solve the optimization task by means of the genetic algorithm one has
to start with the first step of coding the chromosome. In classic genetic algorithms
the chromosome is the binary chain with the constant length
Balancing of the assembly line is the problem that is assigned to the theory of
combination tasks and in such a case there is no direct representations in the
chromosome of the genetic algorithm. It is caused by the two constraints: production
pace and the graph of sequence constrains. It should be mentioned that for the task
with For the algorithm developed the binary chromosome with the length equal to the product of the coding number and the number of operations has been applied. Selection of operation is performed out of the operation list that are actually accessible. That means they do not have any predecessor. Such a list is dynamically created and operations are not sorted in growing sequence in relation to their realization time and the number. The algorithm has been tested on several tasks. The results of the task have already been known and this fact enabled authors to evaluate the proposed algorithm. The first tested task has been marked as "sawyer". The assembly process in this case has been composed of 30 operations and the sum of times was 324 units, the shortest time was 1 unit and the longest 25 units. The graph of the sequence constraints is composed of 32 edges. With the application of the algorithm presented it was very easy to find optimal solutions. The main aim of the investigations performed by authors, concerned the effectiveness and usability of genetic algorithms for solving the tasks applied in assembly line balancing. Based on the results it is possible to state that the program, based on the classic genetic algorithm enabled optimal results to be obtained for many of the tasks. References
- 1
- D.E. Goldberg, "Algorytmy genetyczne i ich zastosowania", WNT, Warszawa 1998.
- 2
- J. Zurek, O. Ciszak, "Modelowanie oraz symulacja kolejnosci montazu czesci i zespoów maszyn za pomoca teorii grafów", Wydawnictwo Politechniki Poznanskiej, Poznan, 1999.
- 3
- Z. Michalewicz, "Algorytmy genetyczne + struktury danych = programy ewolucyjne", WNT, Warszawa 1999.
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