الفهرس 
State of the Art on Assembly Lines and Genetic AlgorithmsOnly 14 pages are availabe for public view
 |  | from | 228 |  |  |
| | | from | 228 | | |
Abstract
Mixed model two-sided assembly line are common indu Dial practice in
the assembly of large-sized product such as buses and trucks. In a Mixed
model two-sided as embly line, differenr assembly task are carried out
on the same product in parallel at both left and right sides of the line.
The decision problem of optimally balancing the assembly work among
the stations with respect to some objective i known as the a sembly line
balancing problem (ALBP). In this research a Genetic AlgoritJun is developed
to solve the Single-model and Mixed-model Two-sided Assembly
Line Balancing Problem with the objective of finding the minimum number
of stations as well as the minimum number of mated-stations for a
given cycle time.
The developed heuristic algorithm specifies a new method for generating
the initial population. It applies a hybrid crossover and a modified
scramble mutation operators. Moreover, due to the nature of the two-sided
assembly line balancing problem, a proposed station oriented procedure is
adopted for assigning tasks to station. This procedure specifie the ide
of the ta ks that have no preferred direction based on specific rules rather
than a signing these tasks randomly.
A computational study is presented to test the performance of heuristic
algorithm and the side assignment rules. The results showed that the
proposed side assignment rules are effective especially in large problems.
The proposed method of generating the initial population is able to generate
feasible solution allowing more diversity in the population. The by brid crossover and the modified scramble mutation are able to preserve the
feasibility of all solutions throughout all the developed generations. The
Genetic Algorithm is able to find the optimum or near optimum solutions
within a limited number of iterations.