Zhang Zhenzhong, Zhang Ling, Li Weichun
CAAC Academy, Civil Aviation Flight University of China, Chengdu, 618307, China.
School of Modern Posts, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Sci Rep. 2025 Jan 2;15(1):481. doi: 10.1038/s41598-024-84663-y.
In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving practical allocation problems. This study focuses on the problem of allocating orders to parallel machines with varying efficiencies under uncertain and high-dimensional conditions. To maximize the expected profit of order processing, a mathematical model for a high-dimensional stochastic optimization problem is developed, considering the uncertainty due to potential customer order cancellations in a real-world production. By integrating an intelligent optimization algorithm for the order assignment problem with a scenario generation approach, a novel framework for intelligent stochastic optimization is proposed. This framework employs an intelligent optimization algorithm suitable for the generalized assignment problem to search for improved solutions and utilizes the scenario generation method to produce the necessary scenarios for evaluating solutions in high-dimension. Experimental results demonstrate that the proposed approach effectively addresses the high-dimensional stochastic order allocation problem, outperforming the compared method in terms of efficiency and capability.
在实际的供应链运作中，高效的订单分配显著提高了供应链的整体效率。实际生产环境受到众多不确定性因素的困扰，如不可预测的客户订单，这极大地增加了解决实际分配问题的复杂性。本研究聚焦于在不确定和高维条件下，将订单分配给效率各异的并行机器的问题。为了使订单处理的预期利润最大化，考虑到实际生产中潜在客户订单取消所带来的不确定性，建立了一个高维随机优化问题的数学模型。通过将订单分配问题的智能优化算法与场景生成方法相结合，提出了一种新颖的智能随机优化框架。该框架采用适用于广义分配问题的智能优化算法来搜索改进解，并利用场景生成方法生成必要的场景，以在高维中评估解。实验结果表明，所提出的方法有效地解决了高维随机订单分配问题，在效率和能力方面优于比较方法。
