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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Intelligent Evacuation Route Planning Algorithm Based on Maximum Flow.

Li Liu1, Huan Jin2, Yangguang Liu3

  • 1College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China.

International Journal of Environmental Research and Public Health
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent evacuation route planning algorithm for emergencies, optimizing routes and victim allocation for rapid community evacuation. The method uses a maximum flow algorithm for efficient and precise emergency response.

Keywords:
artificial intelligenceevacuation routingnetwork flow algorithmroute planning

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Area of Science:

  • Operations Research
  • Computer Science
  • Disaster Management

Background:

  • Effective evacuation route planning is critical for minimizing casualties during emergencies like pandemics (e.g., COVID-19) and natural disasters.
  • Existing methods may struggle with the dynamic and complex nature of disaster scenarios, necessitating intelligent optimization.
  • The need for rapid and efficient victim evacuation to safe zones is paramount.

Purpose of the Study:

  • To develop an intelligent optimization algorithm for community evacuation route planning.
  • To address the challenge of determining optimal routes and allocating victims efficiently during emergencies.
  • To provide a computationally efficient and precise solution for time-sensitive evacuation scenarios.

Main Methods:

  • Formulating the evacuation route planning problem as a maximum flow problem.
  • Proposing a novel binary search algorithm integrated with a maximum flow algorithm.
  • Modeling evacuation time as a convex nonlinear function of victim allocation per route.

Main Results:

  • Demonstrated the effectiveness of the proposed algorithm through numerical examples and MATLAB simulations.
  • The algorithm exhibits low computational complexity and high precision in generating evacuation routes.
  • Successfully validated the algorithm's capability for intelligent optimization in evacuation planning.

Conclusions:

  • The developed algorithm offers a practical and effective solution for nonlinear evacuation route planning models.
  • Findings have significant implications for improving community safety and response during various disaster types.
  • The approach is applicable to both human evacuation schemes and robot path planning.