A Comparative Analysis of Modern and Traditional Sorting Algorithms for Optimizing Automobile Production Scheduling

This study's objective is to improve production scheduling for automobile manufacturing by analyzing how well modern sorting algorithms function within the framework of production planning. The performance of two modern sorting algorithms, namely Avi sort and RevWay sort, as well as two traditional sorting algorithms, namely Merge sort and Insertion sort, are compared in this study. The purpose of this study is to investigate the applicability and efficiency of these algorithms in the automotive industry, specifically in Indonesia. Synthetic data representing production planning scenarios are generated to analyze the algorithms' performance in terms of runtime and memory allocation. The results highlight the performance of Avi sort and RevWay sort in efficiently prioritizing tasks, allocating resources, and optimizing the production process. However, in general, they are outperformed by the best traditional sorting algorithms, Merge sort and Insertion sort. The findings provide valuable insights for compared sorting algorithms for the automotive industry to enhance production planning strategies, improve resource allocation, and increase overall productivity. Further research can explore additional factors such as shifts, breaks, and overtime, by continuously refining and incorporating advanced sorting algorithms to comprehensively optimize the production scheduling process in the automotive industry.