A Cellular-rearranging of Population in Genetic Algorithms to Solve Assembly-Line Balancing Problem

Abstract

Assembly line balancing problem (ALBP) is the allocating of assembly tasks to workstations with consideration of some criteria such as time and the number of workstations. Due to the complexity of ALB, finding the optimum solutions in terms of the number of workstations in the assembly line needs suitable meta-heuristic techniques. Genetic algorithms have been used to a large extent. Due to converging to the local optimal solutions to the most genetic algorithms, the balanced exploration of the new area of search space and exploitation of good solutions by this kind of algorithms as a good way can be sharpened with some meta-heuristic. In this paper, the modified cellular (grid) rearranging-population structure is developed. The individuals of the population are located on cells according to the hamming distance value among individuals as neighbours before regenerations and a family of cellular genetic algorithms (CGAs) is defined. By using the cellular structure and the rearrangements, some of the family members can find better solutions compared with others in the same iterations, and they behave much more reasonably in order to acquire the solution in terms of the number of workstations and the smoothly balanced task assignment on criteria conditions.

Garment production line optimization using production information based on real‐time power monitoring data

“…[19][20][21] GA-based process optimization has long been studied using machine learning techniques. 22 Cheshmehgaz et al analyzed the relocation of cellular personnel using a GA. 23 Li et al proposed an active learning GA. 24 Barathwaj et al considered the application of a GA for reducing the number of workers in mixed-model assembly lines. 25 Matondang and Jambak applied a GA to the balancing of assembly lines in complex real-world manufacturing environments.…”