Optimization designs refer to experimental design strategies that are specifically structured to optimize a process, product, or system. The goal of these designs is to identify the combination of factors (inputs) that lead to the best possible outcome (response) according to a defined objective, such as maximizing performance, minimizing cost, or finding the most efficient operating conditions.
Optimization designs are particularly useful in fields like manufacturing, engineering, product development, and process improvement, where the goal is often to find the “optimal” settings of several variables that maximize or minimize a particular response variable (e.g., efficiency, yield, cost, etc.).
Once a screening design is used and influential factors are identified, the next goal usually consists of optimising their settings.
This module covers the construction of experimental designs for optimisation applications. Several types of optimisation designs exist. Data modelling is carried out with response surface methodology – RSM.
Course Outline
The recommended duration for this course is 2 online session(s).
The most popular version of this course comprises 2 sessions described as follows:Session 1: Context of Use & Objectives - Design of Optimal Designs- Objective
- Key ideas
- Statistical concepts
- Central Composite Designs
- Box-Behnken Designs
- Optimal Designs
- Response surface methodology (RSM)
- Modelling: training, validation
- Use of Model