Department of Logistics, Korea Maritime and Ocean University, Busan, Republic of Korea.
Department of Mechatronics, Ho Chi Minh City University of Technology (HCMUT)-Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam.
PLoS One. 2024 Jan 5;19(1):e0286433. doi: 10.1371/journal.pone.0286433. eCollection 2024.
This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model's sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.
本研究考虑了多周期库存系统，以优化随机需求下的利润和存储空间。提出了一个基于随机需求的非线性规划模型来模拟库存操作。使用多目标灰狼优化（MOGWO）方法实现有效的库存管理系统，在最大化利润的同时减少存储空间。使用数值结果来确认优化解决方案的有效性。在四个实际场景中进行了多目标库存优化的数值分析和测试。对库存模型进行了敏感性分析，以进一步验证优化解决方案。特别是，所提出的方法允许企业在优化利润的同时，调节库存管理所需的存储空间。通过库存管理策略和库存管理实践，可以显著提高供应链绩效。最后，新的决策策略可以为有效管理数字供应链网络提供新的思路，以应对市场波动。
