在差异化措施下可行的强大的随机优化,并应用于公平的人道主义响应规划.
Aakil M Caunhye1, Douglas Alem1
1Business School, The University of Edinburgh, 29 Buccleuch Place, Edinburgh, EH8 9JU UK.
概括
这项研究为强大的随机优化引入了新的分歧函数,使复杂模型更实用. 这些方法通过平衡有效性和公平性来改善人道主义物流中的决策.
科学领域:
- 运营研究 运营研究
- 优化理论 优化理论
- 决策科学 决策科学 决策科学
背景情况:
- 强大的随机优化模型对于在不确定性下做出决策至关重要.
- 现有的基于f-分歧的模型带来了重大的数值挑战,特别是混合整数决策.
- 需要切实可行的近似来提高这些模型的适用性.
研究的目的:
- 开发新的分歧函数,用于对两个阶段强大的随机优化模型的可处理的近似.
- 保持多功能性,在模拟不同的模糊性厌恶,同时确保数值可处理性.
- 在人道主义物流环境中实施和验证这些新方法.
主要方法:
- 提出新的分歧函数,产生具有可控数值困难的强大的对应函数.
- 开发技术来模仿现有的f-差异,而不影响可行性.
- 适用于人道主义行动的现实位置分配模型中的应用,包括有效性-股权权衡.
主要成果:
- 与现有的f-分歧相比,在强大的对应方的可行性方面取得了显著的改进.
- 通过一种新的目标功能,在人道主义响应中实现了更大的公平性.
- 提高运营计划的稳定性,以应对概率估计的变化.
结论:
- 拟议的分歧函数为强大的随机优化提供了一个实用的方法.
- 该方法有效地平衡了人道主义行动的有效性和公平性.
- 这种方法提高了不确定性下规划的可靠性.
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