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在不确定的信息下,用于p-hub中位数问题的模糊区间优化方法
Yu Wang1, Tao Zhu1, Kaibo Yuan1
1School of Economics and Management, Civil Aviation Flight University of China, Guanghan, China.
本研究引入了三角模糊数模型和遗传禁忌搜索算法,以优化不确定的p-hub中位数问题. 这种新的方法提高了解决方案质量,并减少了物流网络设计的计算时间.
科学领域:
- 运营研究 运营研究
- 物流和供应链管理的物流和供应链管理.
- 优化理论 优化理论
背景情况:
- 随机和强大的优化方法为由于参数离散而导致的不确定的p-hub中位数问题提供了次优化解决方案.
- 现有的方法在复杂,不确定的物流网络设计中努力保持信息完整性.
研究的目的:
- 为非严格的无容量多配置p-hub中位数问题提出一个新的三角模糊数模型.
- 开发一种增强的优化方法,将模糊逻辑与元启发学相结合,以提高效率和准确性.
主要方法:
- 开发了一个三角模糊数模型来表示p-hub中位数问题的不确定性.
- 整合了三角形模糊数值评估指数与遗传-tabu搜索算法进行优化.
- 在算法代过程中使用会员函数关系计算了模糊中心方案的适应性.
主要成果:
- 与遗传算法相比,拟议的遗传禁忌搜索算法将平均计算时间减少了49.05%,与禁忌搜索相比,减少了40.93%.
- 这种新的方法在不确定的环境中将总成本降低了1.47% (vs.随机),2.80% (vs.强大) 和8.85% (vs.实数优化).
结论:
- 三角模糊数模型和集成的遗传禁忌搜索算法有效地解决了不确定的p-hub中位数问题.
- 这种方法提高了优化速度和解决方案质量,在物流网络设计中优化了传统的优化技术.
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