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一个改进的迁徙鸟类优化算法,用于在模糊环境中闭环供应链网络规划

Yangjun Ren¹, Qiong Chen², Yui-Yip Lau³

¹School of Economics and Management, Changzhou Vocational Institute of Textile and Garment, Changzhou, China.

|June 27, 2024

概括

本研究介绍了一个模糊的编程模型和一个新的迁徙鸟类优化算法,用于不确定的闭环供应链网络设计. 这种方法有效地降低了成本,并提高了供应链利益相关者的决策能力.

科学领域:

运营研究运营研究
供应链管理供应链管理
优化优化优化优化

背景情况:

闭环供应链网络提供经济和环境效益,但在需求,成本和能力方面面临重大不确定性.
传统的网络设计模型很难充分解决这些固有的不确定性.
有效管理这些不确定性对于优化供应链性能至关重要.

研究的目的:

为封闭循环供应链网络设计开发一种新的模糊编程模型,该模型包含不确定性.
提出一个高效的优化算法,迁徙鸟类优化算法与一个新的产品源代码方案,以解决模型.
尽量减少网络的总成本,包括设施位置和运输路线选择.

主要方法:

一种基于可信度衡量的模糊编程模型,用于处理不确定的参数.
一种迁徙鸟类优化算法,采用一种新的产品源代码方案,灵感来自产品包装和交货订单信息.
使用35个示例进行了广泛的测试,将拟议的算法与精确方法 (LINGO) 和其他元启发方法 (GA,ACO,SA) 进行比较.

主要成果:

拟议的迁徙鸟类优化算法在可接受的计算时间内始终为大规模问题提供最佳或接近最佳的解决方案.
模糊编程模型有效地处理不确定性,敏感性分析证实了它的适用性,并产生了有价值的管理见解.
与传统方法相比,新型编码方案提高了启发式算法找到最佳解决方案的可能性.

结论:

开发的模糊编程模型和迁徙鸟类优化算法为设计不确定的闭环供应链网络提供了强大而高效的方法.
该研究为供应链企业和利益相关者提供了科学支持的框架,以改进网络设计和降低成本.
这些发现强调了先进优化技术在管理供应链复杂性和不确定性方面的重要性.

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