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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Multimachine Stability01:25

Multimachine Stability

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Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

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Related Experiment Video

Updated: Jun 8, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Capacity planning for electronic waste management facilities under uncertainty: multi-objective multi-time-step model

Poonam Khanijo Ahluwalia1, Arvind K Nema

  • 1TATA Consulting Engineers Limited, Kailash Colony Extension, New Delhi, India. poonamkahluwalia@yahoo.co.in

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA
|October 12, 2010
PubMed
Summary

This study develops a decision model for optimal computer waste facility location and capacity planning. It balances costs against environmental, health, and social risks under uncertain waste generation.

Related Experiment Videos

Last Updated: Jun 8, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Area of Science:

  • Environmental Management
  • Operations Research
  • Waste Management

Background:

  • Municipal authorities face challenges in facility location and capacity decisions for waste management.
  • Informal sectors, like backyard workshops in India, currently manage computer waste with rudimentary practices.
  • Uncertainty in computer waste generation quantities complicates current management strategies.

Purpose of the Study:

  • To develop a multi-objective decision-support model for selecting optimal facility configurations (location and capacity).
  • To address the trade-offs between cost, environmental risk, health risk, and socially perceived risk.
  • To simultaneously account for uncertainty in waste generation quantities.

Main Methods:

  • A multi-time-step, multi-objective decision-support model was employed.
  • The model analyzes trade-offs between cost and various risk factors.
  • It considers both existing and proposed facility configurations.

Main Results:

  • The study provides a framework for optimizing facility networks for computer waste.
  • It quantifies the impact of uncertainty in waste generation on decision-making.
  • The model facilitates informed decisions balancing economic and risk-related factors.

Conclusions:

  • A comprehensive decision-support model is crucial for effective computer waste management.
  • Simultaneously addressing cost, risks, and uncertainty leads to more robust facility planning.
  • This approach supports sustainable waste management practices in developing nations.