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相关概念视频

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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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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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.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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在使用元启发式算法的随机需求下,优化和库存管理使用元启发式算法.

Nguyen Duy Tan1, Hwan-Seong Kim1, Le Ngoc Bao Long1

  • 1Department of Logistics, Korea Maritime and Ocean University, Busan, Republic of Korea.

PloS one
|January 5, 2024
PubMed
概括

这项研究优化了多期库存系统,通过使用一种新的优化方法最大限度地提高利并最大限度地减少存储空间. 该方法提高了供应链的性能,并为数字供应链管理提供了洞察力.

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科学领域:

  • 运营研究 运营研究
  • 供应链管理 供应链管理
  • 优化技术 优化技术

背景情况:

  • 在多期库存系统中,随机需求对优化利和存储提出了挑战.
  • 传统的库存模型往往难以有效地平衡竞争目标.
  • 有效的库存管理对于整体供应链性能和弹性至关重要.

研究的目的:

  • 开发和验证一种非线性编程模型,用于在随机需求下模拟库存操作.
  • 实施多目标灰狼优化 (MOGWO) 方法,同时实现利最大化和存储空间减少.
  • 为加强数字供应链管理提供一种新的决策策略,以应对市场波动.

主要方法:

  • 开发一种非线性编程模型,以模拟随机需求的库存动态.
  • 应用多目标灰狼优化 (MOGWO) 算法来解决复杂的库存问题.
  • 在四个实际场景中进行数值分析和灵敏度分析,以验证模型的有效性.

主要成果:

  • MOGWO方法成功地确定了最佳解决方案,平衡利最大化和存储空间最小化.
  • 数字结果证实了拟议方法在实际库存管理场景中的有效性.
  • 敏感性分析验证了获得的最佳解决方案的稳定性和可靠性.

结论:

  • 拟议的库存优化战略有效地平衡了利和存储空间,提高了供应链的性能.
  • 基于MOGWO的方法为面临市场波动的数字供应链提供了一个新的决策框架.
  • 这项研究为寻求改善库存管理实践和运营效率的企业提供了宝贵的见解.