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相关概念视频

Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Updated: Jun 30, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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条件神经启发式用于多目标车辆路由问题

Mingfeng Fan, Yaoxin Wu, Zhiguang Cao

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    此摘要是机器生成的。

    一个新的条件神经启发式 (CNH) 通过考虑上下文,偏好和大小,有效地解决多目标车辆路由问题 (MOVRPs). 这种方法改进了帕雷托前线近似,并在各种问题尺度上优于现有的方法.

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    科学领域:

    • 运营研究 运营研究
    • 人工智能的人工智能
    • 组合优化的优化.

    背景情况:

    • 对于多目标车辆路由问题 (MOVRPs) 的现有神经启发式方法缺乏性能,原因是对实例上下文,偏好和问题大小的利用不足.
    • 这种限制阻碍了他们准确接近确切的帕雷托前线 (PF) 的能力.

    研究的目的:

    • 提出一种新的条件神经启发式 (CNH),充分利用实例上下文,偏好和大小,以改进MOVRP解决方案.
    • 为了提高精确的帕雷托前线 (PF) 和整体启发性性能的近似性.

    主要方法:

    • 开发了一个编码器-解码器结构化的策略网络,包含一个基于双重注意力的编码器,以关联偏好和实例上下文.
    • 实现了一个使用正弦编码来明确纳入问题大小的尺寸意识解码器.
    • 定制了REINFORCE算法与随机偏好 (SPs) 进行增强训练.

    主要成果:

    • 与现有方法相比,CNH实现了对PF的有利近似,显示了较高的超容量 (HV) 和较低的最佳性差距 (Gap).
    • 单个受过训练的CNH模型的表现优于针对特定问题规模而受过训练的专业模型.
    • 废弃研究验证了关键建筑设计的有效性.

    结论:

    • 拟议的CNH通过有效地整合上下文,偏好和大小,显著提升了MOVRPs的神经启发学.
    • CNH提供了一种多功能和高性能解决方案,能够用单一的模型解决各种各样的问题.