Solving the Scheduling Problem of Stainless Steel and Alloy Factory: A Case Study of Stainless Steel and Alloy Factory in Uttaradit Province

This study addresses production scheduling inefficiency in stainless steel and alloy manufacturing by developing mathematical models and employing Greedy (GdyA) and Genetic Algorithms (GA). A 23-factorial design tested GA efficiency, optimized by ANOVA analysis. Genetic Algorithm (GA) methods are to establish a program for production scheduling. The research found that the small problem would get the appropriate parameter value. Optimal GA parameters varied with problem size: the smaller problems favoured a population of 50 or 200 generations, crossover of 1, and mutation of 0.3, while the larger problems performed best with a population of 200 or 50 generations, crossover of 1, and mutation of 0.3. This study is in line with the objective of giving factories an alternative to use the results of the production scheduling answer values ​​to solve the problem. This approach effectively tackles the NP-Hard nature of production scheduling, with GA consistently finding solutions matching the minimum bound. GdyA also achieved the minimum bound in 50% of the tested cases. It can be implemented in a real-world setting. Optimized GA consistently delivered optimal scheduling solutions over three months, confirming the potential to significantly enhance production efficiency.