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Related Concept Videos

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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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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Maximum Power Flow and Line Loadability01:23

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
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A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering

Gewen Huang1,2, Yuanhang Qi3, Yanguang Cai2

  • 1Information and Network Center, Jiaying University, Meizhou 514015, China.

Biomimetics (Basel, Switzerland)
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for humanitarian aid logistics, optimizing vehicle routes to minimize disaster relief wait times and operational costs. A novel algorithm, DGWO-F2OPT, significantly improves efficiency and solution quality compared to existing methods.

Keywords:
cumulative capacitated vehicle routing problemdynamic grey wolf optimizer algorithmemergency logisticsfloating 2-optmulti-objective

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Area of Science:

  • Operations Research
  • Logistics and Supply Chain Management
  • Disaster Management

Background:

  • Humanitarian aid delivery faces challenges in efficient vehicle scheduling to minimize recipient wait times.
  • Traditional vehicle routing models often overlook practical factors like driver workload and on-site decision-making needs.
  • Evaluating driver intensity using operation times and employing multi-objective modeling are crucial for practical aid logistics.

Purpose of the Study:

  • To introduce a bi-objective model, the multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT).
  • To minimize cumulative wait time in disaster-affected areas and reduce extra expenditures from excess rescue vehicle operation time.
  • To develop an efficient optimization algorithm for solving the MO-CCVRP-OT model.

Main Methods:

  • Proposed a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), integrating real number encoding, random key decoding, and ROV rules.
  • Implemented a dynamic non-dominated solution set update strategy to enhance algorithm performance.
  • Introduced a multi-objective improved floating 2-opt (F2OPT) local search for efficient problem-solving and faster convergence.

Main Results:

  • DGWO-F2OPT achieved a 6.22% lower average fitness value for its utopia optimum solution compared to DGWO-2OPT.
  • DGWO-F2OPT demonstrated a 16.49% lower average fitness value than NS-2OPT in comparative trials.
  • The MO-CCVRP-OT model showed an 18.72% closer Euclidean distance to the utopian point than the traditional CVRP-OT.

Conclusions:

  • The proposed DGWO-F2OPT algorithm effectively solves the MO-CCVRP-OT, balancing humanitarian aid delivery speed with operational cost efficiency.
  • The novel algorithm significantly outperforms existing methods in terms of solution quality and convergence speed.
  • MO-CCVRP-OT provides a more comprehensive approach to humanitarian logistics, considering critical real-world operational constraints.