发表机构
Johannes Kepler University Linz; Hankuk University of Foreign Studies（林茨约翰内斯·开普勒大学; 韩国外国语大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对双目标混合整数规划中的可持续供应链网络设计问题，提出重用加权求和法先前迭代的Benders割来初始化主问题，从而显著减少计算时间。
AI 中文摘要
我们研究了可应用Benders分解的双目标混合整数规划问题，Benders分解是一种在现实应用中广泛用于利用问题结构的技术。我们的研究聚焦于一个可持续供应链网络设计问题，该问题旨在同时最小化经济和环境目标。为处理双目标性质，我们采用加权求和法，该方法需要以不同权重值重复求解相似问题。为解决这一问题，我们提出了一种直接的加速技术，即重用加权求和法先前迭代中生成的Benders割。该方法使得Benders分解主问题能够以预生成的割进行初始化。我们的计算实验表明，重用这些割显著减少了计算时间。
英文摘要
We investigate bi-objective mixed integer programming to which Benders decomposition can be applied, a widely used technique for exploiting problem structures in real-world applications. Our study focuses on a sustainable supply chain network design problem that aims to minimise both economic and environmental objectives. To address the bi-objective nature, we employ the weighted sum method, which requires solving similar problems repeatedly with different weight values. To tackle this, we propose a straightforward acceleration technique that reuses Benders cuts generated in previous iterations of the weighted sum method. This approach enables the Benders decomposition master problem to be initialised with pre-generated cuts. Our computational experiments demonstrate that reusing these cuts significantly reduces computing time.
