Assembly line balancing problems that occur in real world situations are dynamic and are fraught with various sources of uncertainties such as the performance of workers and the breakdown of machinery. This is especially true in the clothing industry. The problem cannot normally be solved deterministically using existing techniques. Recent advances in computing technology, especially in the area of computational intelligence, however, can be used to alleviate this problem. For example, some techniques in this area can be used to restrict the search space in a combinatorial problem, thus opening up the possibility of obtaining better results. Among the different computational intelligence techniques, genetic algorithms (GA) is particularly suitable. GAs? are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. In this paper, we present the details of a GA and discuss the main characteristics of an assembly line balancing problem that is typical in the clothing industry. We explain how such problems can be formulated for genetic algorithms to solve. To evaluate the appropriateness of the technique, we have carried out some experiments. Our results show that the GA approach performs much better than the use of a greedy algorithm, which is used by many factory supervisors to tackle the assembly line balancing problem.
http://ceres.emeraldinsight.com/vl=2257708/cl=25/nw=1/rpsv/~1099/v10n1/s2/p21 (local filename: p21.pdf)
This paper describes an implementation of an assembly line balancing problem, where tasks are assigned to workers based on their skill level in order to minimise slack time, and so ensure fastest production. It goes into a fair amount of detail regarding the representation, and mutation and crossover operators that it uses. The representation is simply a value representing a worker, and the position in the genome represents the task. This application may end up deriving down to an instance of the travelling salesman problem.