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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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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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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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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.
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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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Markov decision process design: A framework for integrating strategic and operational decisions.

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This study presents a new framework for system design under uncertainty, integrating design and operational phases to minimize both initial and long-term costs for repeated use systems.

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Area of Science:

  • Operations Research
  • Systems Engineering
  • Decision Science

Background:

  • Designing systems for repeated use often involves balancing upfront design costs with future operational expenses.
  • Uncertainty in operational conditions complicates optimal system design, making traditional approaches insufficient.

Purpose of the Study:

  • To develop a unified modeling framework for the optimal design of systems intended for repeated use under uncertainty.
  • To simultaneously minimize initial design costs and expected future operational costs.

Main Methods:

  • The study integrates a mixed-integer program for the design phase and discounted-cost infinite-horizon Markov decision processes for the operational phase.
  • A bilevel mixed-integer linear programming formulation was derived to address the integrated problem.
  • Computational studies were conducted on realistic problem instances.

Main Results:

  • The proposed framework successfully integrates system design and operational planning under uncertainty.
  • The bilevel formulation provides a method for simultaneously optimizing design and operational costs.
  • Numerical results demonstrate the solvability of realistic instances.

Conclusions:

  • The developed modeling framework offers an effective approach to optimally design systems for repeated use in uncertain environments.
  • This research provides a valuable tool for decision-makers aiming to minimize total system costs over its lifecycle.