Biologically inspired genetic algorithm to minimize idle time of the assembly line balancing

Abstract

Abstract-Assembly line balancing (ALB) is a well-known combinatorial optimization problem in production and operations management area. Due to the NP-hard nature of the ALB problem, many attempts have been made to solve the problem efficiently. In this study, biologically inspired evolutionary computing tool which is genetic algorithm (GA) is adopted to solve the ALB problem with the objective of minimizing the idle time in the workstation. The key issue in solving ALB is how to generate a feasible task sequence which does not violate the precedence constraints. This task sequencing is a vital work to be solved prior assigning tasks to workstation. In order to generate only feasible solution, a repairing strategy based topological sort is integrated in the GA procedure. The ALB test problems benchmarked from the literature are used in the study and the computational results show that the proposed approach is capable to obtain feasible solution with minimum idle time for a simple model assembly line.

Nature-Inspired Algorithms Applications to Power System Optimization

“…Genetic Algorithms as (biologically inspired approaches) [44,45] , are search algorithms based on natural genetics that provide robust search capabilities in complex spaces, and thereby offer a valid approach to problems requiring efficient and effective search processes [39,40]. Some of the advantages of a GA include that it [4].…”