改进了模糊多目标运输问题,使用三角模糊数字
1Department of Mathematics, SAS, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Heliyon
|July 15, 2024
概括
这项研究解决了模糊的多目标运输问题 (FMOTP),不确定成本,供应和需求. 一种新的方法将模糊的数据转化为清晰的值,为复杂的物流挑战提供最佳解决方案.
科学领域:
- 运营研究 运营研究
- 模糊的数学 模糊的数学
- 供应链管理 供应链管理
背景情况:
- 运输问题往往涉及不确定的参数,如成本,供应和需求,由于市场波动.
- 传统方法与不准确的数据作斗争,需要模糊的方法.
研究的目的:
- 在模糊的环境中开发一个强大的方法来解决多目标运输问题 (MOTP).
- 使用模糊数字处理不准确的运输成本,供应和需求数据.
- 在不确定的条件下确定模糊MOTP (FMOTP) 的最佳解决方案.
主要方法:
- 用模糊参数对FMOTP进行数学表述.
- 模糊算法的应用,将FMOTP分解为低级,中级和高级的脆脆的MOTP.
- 将模糊的问题减少到一个清晰的MOTP.
- 利用和平均值来解决每个目标函数.
主要成果:
- 介绍了解决FMOTP的系统程序.
- 该方法有效地将模糊参数转换为可解决的清晰格式.
- 这种方法有助于为具有固有的不确定性的运输物流找到最佳解决方案.
结论:
- 拟议的方法为解决FMOTP提供了一个有效的框架.
- 这项研究有助于在不确定性下在运输和物流领域做出更好的决策.
- 该技术增强了在动态和不精确的市场环境中找到最佳解决方案的能力.
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