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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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Related Experiment Video

Updated: Sep 1, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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Clustering-based iterative heuristic framework for a non-emergency patients transportation problem.

Jamal Abdul Nasir1, Yong-Hong Kuo1,2, Reynold Cheng2,3,4

  • 1Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong.

Journal of Transport & Health
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

This study optimizes non-emergency patient transportation (NEPT) by balancing operational costs and patient inconvenience. A novel heuristic framework effectively manages NEPT services, reducing waiting and travel times for better patient care.

Keywords:
ClusteringDial-a-ride problemsNon-emergency patients transportationShared healthcare mobilityVehicle routing

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Area of Science:

  • Operations Research
  • Healthcare Logistics
  • Public Health

Background:

  • Non-emergency patient transportation (NEPT) is crucial for aging populations and during disease outbreaks.
  • Optimizing NEPT services requires balancing operational efficiency with patient comfort.

Purpose of the Study:

  • To develop and evaluate a model for optimizing NEPT services, focusing on patient inconvenience and operational costs.
  • To address the specific challenges of NEPT in Hong Kong.

Main Methods:

  • A mixed-integer linear programming (MILP) formulation was developed to model the NEPT problem.
  • A clustering-based iterative heuristic framework was designed to solve large-scale NEPT instances efficiently.
  • The MILP model incorporates patient inconvenience measures (waiting time, extra ride time) into the objective function.

Main Results:

  • The heuristic framework achieved an optimality gap of less than 5% for tested instances.
  • Sensitivity analysis revealed trade-offs between operating costs and patient inconvenience penalties.
  • The weighted objective function allows for flexible prioritization of performance measures.

Conclusions:

  • The proposed approach effectively balances operational costs and patient discomfort in NEPT.
  • Decision-makers can use this research to enhance NEPT service quality and efficiency.