Simulating any system involves coming up with a probabilistic model for the inputs, coming up with a relationship between the inputs and outputs, and then observing the outputs over many realizations of the inputs. Optimization, on the other hand, involves finding an input to a function that makes it as small (or large) as possible subject to some constraints. In some cases a system is simulated in order to optimize some quality of the outputs as a function of the system's parameters. However, there are plenty of applications of both simulation and optimization that are very distinct and do not readily relate these two disciplines.

A simulation model is developed when randomness in a real system cannot be ignored. It is hard to incorporate randomness in optimization models, since the randomness must be described through mathematical equations which are often analytically intractable. There are popular methods such as Monte Carlo which involves random variables that may be approximated through simulation. Optimization models in general need analytically solvable equations. This means they may not accurately represent a real system when there is randomness. They are more useful for deterministic problems.

Stochastic programming and approximate dynamic programming often use simulation to estimate expected values within an optimization framework. Discrete-event simulation models may be optimized by building metamodels or using metaheuristcs which need only the output of the simulation model in order to evaluate the quality of alternative parameter vectors.

“Unconventional” has the physical substrate or its organization significantly departing from the de facto norm used for positioning the model for verification and validation. The substrate can be Physical objects or things (billiard ball, or domino). Some unintuitive and pedagogical examples that a model can be made out of almost anything, verified and validated are, Light, Molecules , Molecular Dynamics, Mechanical Neurons , Fluidics and Human Modeling

Unconventional Computational Models span across all the above substrates seamlessly. Simulation, Optimization and integration in these Cyber – Physical Systems is a challenge. This conference is a very unique coming together of these disciplines in the practice of Industrial Engineering and Management.



Topics of Interest include [but not limited to]:

Unconventional Modelling, Simulation and Optimization:

Artificial Life
Artificial Society including Smart Cities and Global Village
Digital Economy & Big Data Analytics
Cellular Automata
Computational Models
Connected Systems including Automobiles & Internet of Things
Corporate Strategy
Cyber – Physical Systems
Fuzzy Control and Modeling
Geosciences & Geographical Information Systems
Intelligence including Artificial Intelligence
Linguistic Information Processing, Machine Learning & Automata
Multiple Attribute Decision Making
Nature Inspired Computing & Computational Intelligence
Operations Research
Probability and Possibility Theories
Reasoning Systems including Approximation Strategies
Remote Sensing
Spatial Data Analysis including Clustering
Statistical Learning Applied in Social Sciences
Transportation Systems
Management Engineering:

Economics
Enterprise Management Models and Practices
Knowledge and Technology Management
Mathematics for Management
Financial Engineering
Management engineering
Technology Involvement & Intelligent Agricultural Management & Manufacturing
Telemedicine & Healthcare including modeling the spread of Epidemic Diseases