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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Normal and Tangetial Components: Problem Solving01:24

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Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Decision Making: P-value Method01:09

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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...
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Friction: Problem Solving01:21

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Friction is an essential force that influences the motion of objects in daily life. Depending on the situation, it can be either beneficial or problematic. Consider a bus with a mass of three megagrams and its center of mass at a specific point, moving along a banked road at a constant speed. The coefficient of static friction between the tires and the road is 0.5. Find the maximum angle of the banked road at which the bus would not slip or tip.
Initially, a visual representation of the...
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Updated: Jun 21, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Improved fuzzy multi-objective transportation problem with Triangular fuzzy numbers.

A Kokila1, G Deepa1

  • 1Department of Mathematics, SAS, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Heliyon
|July 15, 2024
PubMed
Summary

This study addresses the fuzzy multi-objective transportation problem (FMOTP) with uncertain costs, supply, and demand. A novel method transforms fuzzy data into crisp values, enabling optimal solutions for complex logistics challenges.

Keywords:
Fuzzy multi-objective transportation problemFuzzy optimal solutionFuzzy setHarmonic meanTriangular fuzzy number

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Area of Science:

  • Operations Research
  • Fuzzy Mathematics
  • Supply Chain Management

Background:

  • Transportation problems often involve uncertain parameters like cost, supply, and demand due to market fluctuations.
  • Traditional methods struggle with imprecise data, necessitating fuzzy approaches.

Purpose of the Study:

  • To develop a robust methodology for solving the Multi-Objective Transportation Problem (MOTP) in a fuzzy environment.
  • To handle imprecise transportation costs, supply, and demand data using fuzzy numbers.
  • To determine optimal solutions for the Fuzzy MOTP (FMOTP) under uncertain conditions.

Main Methods:

  • Mathematical formulation of the FMOTP with fuzzy parameters.
  • Application of fuzzy arithmetic to decompose the FMOTP into lower, medium, and upper-level crisp MOTPs.
  • Reduction of the fuzzy problem to a crisp MOTP.
  • Utilization of the harmonic mean to address each objective function.

Main Results:

  • A systematic procedure for solving the FMOTP is presented.
  • The method effectively converts fuzzy parameters into a solvable crisp format.
  • The approach facilitates finding optimal solutions for transportation logistics with inherent uncertainties.

Conclusions:

  • The proposed method provides an effective framework for solving the FMOTP.
  • This research contributes to better decision-making in transportation and logistics under uncertainty.
  • The technique enhances the ability to find optimal solutions in dynamic and imprecise market environments.