How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

Abstract

The hybridization between evolutionary genetic algorithm and tabu search has been proposed in this paper to address flow shop scheduling. It accommodates jobs that need to be rearranged and executed on identical machines serially. High agility is required in the manufacturing process, especially for the garment industry to be able to stand facing competitors. The manufacturing related to scheduling to deliver a product as early as possible, the tardiness, and waiting time are also concerned. A Genetic Algorithm was widely used to deal with this; which finds an optimal solution to the problems because it can obtain a more optimal solution. Unfortunately, it is easy to get stuck in optimum local (early convergence is faster). The tabu search algorithm works as a local explorer to better find and exploit the optimum local area, which can be combined with a Genetic Algorithm. This study aims to minimize the three objectives mentioned above to increase production agility. These strategies are evaluated on Taillard benchmark problems to show the significance of the proposed algorithm. The outcomes prove that the hybrid mechanism can boost the solution quality by 2.75% compared to our previous work and can resolve all of Taillard instances better. It has been proven by a 0.28% percentage relative deviation, which shows the error rate is lower and means better.