Related Experiment Video
Updated: Jun 28, 2025
11:53
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
13.0K
A robust optimization model for multi-objective blood supply chain network considering scenario analysis under
Saeed Khakshouri Fariman1, Kasra Danesh2, Mostafa Pourtalebiyan3
1Department of Industrial Engineering, Eshragh Institute of Higher Education, Bojnourd, Iran. Fariman.saeed@eshragh.ac.ir.
Scientific Reports
|April 24, 2024
Summary
This study optimizes blood supply chains during natural disasters using mathematical models. Effective management minimizes shortages and costs, ensuring critical blood needs are met post-crisis.
Area of Science:
- Operations Research
- Disaster Management
- Public Health
Background:
- Natural disasters like floods and earthquakes cause significant loss of life and resources.
- Efficient blood supply chains are critical for disaster response due to blood's perishable nature.
- Existing supply chains struggle to meet fluctuating blood demands during crises.
Purpose of the Study:
- To develop a multi-objective mathematical programming model for optimizing blood supply chains in post-crisis scenarios.
- To allocate blood resources effectively to demand facilities based on quantity and location.
- To identify optimal locations for new blood donation and medical facilities.
Main Methods:
- Development of a robust optimization mathematical programming model.
- Utilization of real-world data from a case study in Iran.
- Inclusion of scenario analysis to enhance model realism and applicability.
Main Results:
- The model successfully allocates blood to demand facilities, considering various crisis situations.
- Identification of optimal locations for new blood donation centers and medical facilities.
- Demonstration of reduced shortages and expenses through optimized supply chain design.
Conclusions:
- Effective management and optimized supply chain design are crucial for handling blood requirements during natural disasters.
- The proposed model provides a framework for minimizing blood shortages and associated costs.
- The research highlights the importance of proactive planning in disaster preparedness for blood supply.
Related Concept Videos
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
45
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
45
01:26Pharmacokinetic Models: Comparison and Selection Criterion
69
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
69
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
52
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...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
52
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
120
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
120
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
60
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
60
01:09Decision Making: P-value Method
5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K