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Related Concept Videos

Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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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.
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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.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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A matheuristic for customized multi-level multi-criteria university timetabling.

Fabian Dunke1, Stefan Nickel1

  • 1Institute for Operations Research, Discrete Optimization and Logistics, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany.

Annals of Operations Research
|June 26, 2023
PubMed
Summary

This study introduces a multi-level planning process for university timetabling, optimizing lecture and tutorial schedules. The approach generates high-quality, customized student timetables using a matheuristic and artificial neural network metamodel.

Keywords:
Artificial neural network meta-modelMatheuristicMulti-criteria decision makingStudent schedulingUniversity timetabling

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Area of Science:

  • Operations Research
  • Educational Technology
  • Computer Science

Background:

  • University course timetables are crucial for educational programs, balancing student and lecturer preferences with normative criteria like workload and idle time.
  • Modern challenges include customizing timetables for individual student needs, integrating online courses, and adapting to flexible learning environments, especially post-pandemic.
  • Optimizing curricula with lectures and tutorials allows for personalized student assignments to tutorial slots.

Purpose of the Study:

  • To develop and evaluate a multi-level planning process for university timetabling that optimizes both macro-level (lecture/tutorial plans) and micro-level (individual student timetables) scheduling.
  • To integrate individual student preferences and online course components into the timetabling process.
  • To enhance timetable quality by balancing normative criteria and individual preferences through advanced computational methods.

Main Methods:

  • A multi-level planning process involving a tactical level for lecture and tutorial plans and an operational level for individual student timetables.
  • Implementation of a mathematical-programming-based planning process within a matheuristic framework, utilizing a genetic algorithm for optimization.
  • Development of an artificial neural network metamodel as a proxy for evaluating the fitness function, which involves the entire planning process.

Main Results:

  • The developed multi-level planning process successfully generates high-quality university schedules.
  • The matheuristic approach, incorporating a genetic algorithm and ANN metamodel, effectively optimizes lecture plans, tutorial plans, and individual student timetables.
  • Computational results demonstrate the procedure's capability in achieving well-balanced timetable performance criteria across the university program.

Conclusions:

  • The proposed multi-level planning process offers a robust solution for complex university timetabling challenges, balancing diverse requirements.
  • The integration of genetic algorithms and artificial neural networks provides an efficient method for optimizing educational scheduling.
  • This approach facilitates the creation of customized and high-quality timetables, enhancing the overall university educational experience.