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混合鱼优化算法,用于在供应链管理中增强有限容量车辆的路由

Vu Hong Son Pham¹, Van Nam Nguyen², Nghiep Trinh Nguyen Dang¹

¹Department of Construction Engineering and Management, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam.

Scientific reports

|January 9, 2024

概括

一个新的混合鱼优化算法 (hGWOA) 有效地将车辆路由距离最小化,以满足有限容量的需求. 这种先进的方法优化了交付路线,在复杂的场景中优于现有的算法.

科学领域:

运营研究运营研究
人工智能的人工智能
优化算法优化算法

背景情况:

有限容量的车辆路线问题 (VRP) 对物流和供应链效率至关重要.
尽量减少运输距离是减少运营成本和环境影响的关键目标.
现有的优化算法面临着复杂的大规模VRP实例的挑战.

研究的目的:

开发一个先进的混合优化算法来解决容量化车辆路由问题 (CVRP).
为了最大限度地减少一支为多个客户服务的车队所需的总运输距离.
提高现有的VRP优化技术的效率和性能.

主要方法:

提出了一个混合鱼优化算法 (hGWOA),集成灰狼优化器 (GWO) 和鱼优化算法 (WOA).
hGWOA结合了GWO的狩猎机制和WOA的利用阶段,具有新的状态和探索增强.
使用23个基准函数和CEC2017基准测试套件来评估性能.

主要成果:

与GWO,WOA,狮优化器 (ALO) 和龙算法 (DA) 相比,hGWOA在最小化交付距离方面表现出卓越的性能.
该算法有效地处理车辆路线的不同规模和复杂性的场景.
通过案例研究的验证,包括水泥交付和现实世界的越南物流场景,证实了它的实际应用.

结论:

拟议的混合鱼优化算法 (hGWOA) 在解决容量化车辆路由问题方面取得了重大进展.
在物流运营中,hGWOA为优化运输距离提供了更高效和有效的解决方案.
该研究强调了混合元启发学在解决复杂的现实世界优化挑战方面的潜力.

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