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Related Concept Videos

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

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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.
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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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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Conditional Neural Heuristic for Multiobjective Vehicle Routing Problems.

Mingfeng Fan, Yaoxin Wu, Zhiguang Cao

    IEEE Transactions on Neural Networks and Learning Systems
    |March 22, 2024
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    Summary
    This summary is machine-generated.

    A new conditional neural heuristic (CNH) effectively solves multiobjective vehicle routing problems (MOVRPs) by considering context, preferences, and size. This approach improves Pareto front approximation and outperforms existing methods across various problem scales.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Combinatorial Optimization

    Background:

    • Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) lack performance due to insufficient exploitation of instance context, preferences, and problem size.
    • This limitation hinders their ability to accurately approximate the exact Pareto front (PF).

    Purpose of the Study:

    • To propose a novel conditional neural heuristic (CNH) that fully leverages instance context, preferences, and size for improved MOVRP solutions.
    • To enhance the approximation of the exact Pareto front (PF) and overall heuristic performance.

    Main Methods:

    • Developed an encoder-decoder structured policy network incorporating a dual-attention-based encoder to relate preferences and instance contexts.
    • Implemented a size-aware decoder using sinusoidal encoding to explicitly incorporate problem size.
    • Customized the REINFORCE algorithm with stochastic preferences (SPs) for enhanced training.

    Main Results:

    • The CNH achieved favorable approximation to the PF, demonstrating higher hypervolume (HV) and lower optimality gap (Gap) compared to existing methods.
    • A single trained CNH model outperformed specialized models trained for specific problem sizes.
    • Ablation studies validated the effectiveness of the key architectural designs.

    Conclusions:

    • The proposed CNH significantly advances neural heuristics for MOVRPs by effectively integrating context, preferences, and size.
    • CNH offers a versatile and high-performing solution capable of addressing diverse problem scales with a single model.