Application of Non-Dominated Sorting Genetic Algorithm (NSGA-II) to Increase the Efficiency of Bakery Production: A Case Study

Minimizing the makespan is an important research topic in manufacturing engineering because it accounts for significant production expenses. In bakery manufacturing, ovens are high-energy-consuming machines that run throughout time. Finding optimal combination of and oven idle time decisive objective space can result substantial financial savings. This paper investigates hybrid no-wait flow shop problems from bakeries. Production scheduling multiple goods lines optimized using Pareto-based multi-objective optimization algorithms, non-dominated sorting genetic algorithm (NSGA-II), a random search algorithm. NSGA-II improved NSGA, leading to better convergence spread solutions space, by removing computational complexity adding elitism diversity strategies. Instead single solution, set represents trade-offs between objectives, improve cost-effectiveness. Computational results actual instances show algorithms significantly outperform existing schedules. The finds complete cases, whereas procedure only delivers subset. study shows application reduce 1.7% 26% while minimizing up 12%. Furthermore, penalizing best marginal amount, alternative minimize 61% compared schedule. proposed strategy be effective small medium-sized bakeries lower costs CO2 emissions.