A comparative study of dispatching rules in dynamic flowshops and jobshops

Efficient dispatching rules for scheduling in a job shop

Batch drying of banana pieces - effect of stepwise change in drying air temperature on drying kinetics and product colour

Optimal experiment design is the definition of the conditions under which an experiment is to be conducted in order to maximize the accuracy with which the results are obtained. This paper summarizes a number of methods by which the parameters of the mathematical model of the system are estimated and describes the application of the Fisher information matrix. Examples are given for thermal property estimation in which the estimation is affected both by measurement noise, which is present during any experiment, but also by uncertainties in the parameters of the model used to describe the system.

https://iopscience.iop.org/article/10.1088/0957-0233/9/6/003/pdf

Optimal experiment design

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

"A Scientific Approach to the Design of Experiments One Factor Designs Factorial Designs Nested Designs Restrictions on Randomization Play it Again, Sam Two Level Fractional Designs Other Fractional Designs Response Surface Designs Appendices Key Word Index "

Design of experiments : a no-name approach

A random error component is ilntroduced into linear models for analyzing data from designed experiments. This error component is called a 'restriction error' because it is used whenever a restriction on randomization occurs in the design. Discussion of the usefulness of this restriction error in randomized complete block designs, nested factorials, and split plot designs is given. Accompanying each discussion is an example of an experiment on prosthetic cardiac valves. Further, the restrictioin error concept is used to look at the controversy regarding a pooled split plot error.

Virgil L. Anderson

Papers

Design of experiments : a no-name approach

Restriction Errors for Linear Models (An Aid to Develop Models for Designed Experiments)

Restrictions on Randomization

Other Fractional Designs