An Optimization Model for Accounts Receivable and Payable Matching and Cash Flow Turnover Based on Non-Dominated Sorting Genetic Algorithm (NSGA-II)
Zhe Fang · 2025
Cash flow management has always been a core problem that companies are particularly troubled by when optimizing their financial performance, especially for large companies with complex financial operations. With the increasing global competition and high economic uncertainty, companies are more eager to optimize working capital and reduce financial risks. Traditional methods, such as single-objective optimization, are often a bit limited - they cannot take into account multiple goals at the same time, such as managing cash flow, accounts receivable, accounts payable, and liquidity. So, our study proposed a multi-objective optimization model based on the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the cash flow of enterprises. This model has several main goals: one is to increase the speed of capital turnover, one is to reduce the volatility of cash flow, and the other is to make the match between accounts receivable and accounts payable more reasonable. It sounds complicated, but it is actually quite effective. We used the financial report data of listed companies in different industries to make an evaluation, and then compared this model with the traditional single-objective optimization method. The results are quite impressive - based on the NSGA-II model, the capital turnover efficiency has increased by about 12% to 18%, and the cash flow volatility has decreased by 7% to 13%. So, this model is not only effective in improving the efficiency of cash flow management, but it can also adapt to the needs of different types of industries. Compared with traditional methods, this multi-objective optimization model does provide a more comprehensive and flexible solution, and is more suitable for today's complex and changing financial environment.