基于张量器的殖民地优化,用于在线到离线餐厅的定餐设计
IEEE transactions on cybernetics
|February 2, 2026
概括
本研究引入了一种新的方法,用于优化在线到线 (O2O) 服务中的餐厅设置餐饮设计 (SMD). 提出的基于张数的群优化 (TACO) 算法有效地平衡了客户偏好,运营需求和利能力.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 在线到离线 (O2O) 餐厅服务面临复杂的套餐设计 (SMD) 挑战.
- 优化SMD需要平衡客户偏好,运营约束和利.
研究的目的:
- 为O2O-SMD问题开发一个全面的数学公式.
- 提出一个有效的算法来解决O2O-SMD问题.
主要方法:
- 一个统一的数学公式,整合了菜的种类,定价,营养和利能力.
- 一个基于张数的群优化 (TACO) 算法,具有并行处理能力.
- 整合一个本地搜索策略,以提高解决方案的精细化和融合速度.
主要成果:
- 与现有的算法相比,TACO表现出优越的性能.
- 该算法在解决方案质量,可扩展性和计算效率方面取得了重大改进.
- 在真实世界数据上的评估证实了算法的有效性.
结论:
- 拟议的TACO算法为O2O-SMD问题提供了有效和实用的解决方案.
- 塔科的基于张数的方法可以实现高效的并行优化.
- 该方法为餐厅优化其定餐供应提供了一个有价值的工具.
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