Improving Manufacturing Agility Through Optimal Production Scheduling System

Abstract

The garment industry is a global industry that requires high agility in response to changing market demands that are quickly changing. Short product cycles with unpredictable demand often make the industry unable to meet consumer needs. In increasing the agility of production to deliver products to customers as fast as possible, the production scheduling system must be designed optimally. Recently algorithm hybridization is used because the combination of more than one algorithm is more optimal. Genetic Algorithm (GA) is a metaheuristic algorithm is applied in various production scheduling and its power can be improved by combining it with the Tabu Search (TS). The GA is the best metaheuristic algorithm to output the optimal scheduling with less execution time but has the disadvantage –easily trapped in local optimum (early convergence is faster). The TS algorithm works as a local search algorithm with a faster computation time than GA. This study aims to minimize the total time to complete the work (minimizing makespan) by combining TS into GA in conducting local searches to increase industrial agility. The results obtained are GA-TS hybridization can provide a more optimal solution for the production scheduling in the garment so that agility can increase.