Exact(5)
This project prepares students for a career in chemical engineering and aids their performance in multi-task problem solving using a teamwork approach.
The proposed methodology mainly involves development of customer satisfaction and cost models using fuzzy regression, generation of product utility functions using chaos-based fuzzy regression, formulation of a multi-objective optimization model and its solving using a non-dominated sorting genetic algorithm-II (NSGA-II).
A mixed-methods design was chosen to investigate in-depth processes during domain-specific problem solving, using a computer-based office simulation.
The primary focus of the project was to measure four core-competencies: literacy, reading, numeracy (basic mathematical and computation skills) and problem solving, using a point scale of 0 to 500, with 0 being the lowest competency level and 500 the greatest.
It consists of 50 items graded in difficulty testing problem solving using a range of algebraic and geometric techniques.
Similar(55)
This model is solved using a computationally efficient genetic algorithm.
These aspects are solved using a multi-cellular genetic algorithm.
The problem is solved using a multi-objective evolutionary algorithm.
The equations were solved using a Matlab procedure.
The differential equations are solved using a power series approach.
The governing equations are solved using a minimising Newton Raphson technique.
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