Modelling, simulation and computational optimization form an integrated part of modern design practice in science and technology. For any design and modelling purpose, the ultimate aim is to gain sufficient insight into the system of interest so as to provide more accurate predictions and better designs. As resources are limited in every field, to minimize the cost and energy consumption, and to maximize the performance, profits and efficiency can be crucially important in all designs. The stringent requirements of minimizing environmental impact and carbon footprint require a paradigm shift in scientific thinking and design practice. However, real-world problems are usually far more complex than models can capture and far more nonlinear than optimization tools can handle; consequently, approximations are necessity as well as a practical possibility. Most design optimization typically involves uncertainty in material properties and parameters. In this case, optimal design does not necessarily mean robust. In fact, we often have to settle for the robust, suboptimal design options. After all, we wish to solve our design and modelling problems with sufficiently good accuracy assuming reasonable time expenditures.
Modelling and simulation is getting information about how something will behave without actually testing it in real life. For instance, if we wanted to design a race car, but weren't sure what type of spoiler would improve traction the most, we would be able to use a computer simulation of the car to estimate the effect of different spoiler shapes on the coefficient of friction in a turn. We're getting useful insights about different decisions we could make for the car without actually building the car.
More generally, modeling and simulation is using models, including emulators, prototypes and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The terms "modeling" and "simulation" are often used interchangeably. The use of modeling and simulation in all emerging areas of science and engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included in the body of knowledge of engineering management. M & S has already helped to reduce costs, increase the quality of products and systems, and document and archive lessons learned. M & S is a discipline on its own. Its many application domains often lead to the assumption that M & S is pure application. This is not the case and needs to be recognized by engineering management experts who want to use M & S. To ensure that the results of simulation are applicable to the real world, the engineering manager must understand the assumptions, conceptualizations, and implementation constraints of this emerging field.
Despite of significant progress in the field of modelling, simulation and computational optimization during the last few decades, many challenging issues still remain unresolved. Challenges may be related to various aspects and depend on many intertwined factors. In the current context, such challenges are related to nonlinearity, scale of the problem, time constraint and the complexity of the system. First, many problems are highly nonlinear, and thus their objective landscapes are multimodal. Consequently, multiple optima may be present. Many traditional algorithms do not cope well with such high multimodality. This necessitates new techniques to be developed. Second, many real-world problems may be very large-scale, though most optimization methods are tested over small-scale problems. Third, by far the most important factor concerning the solution process is the time constraint. Solutions have to be obtained within a reasonably time, ideally instantaneously in many applications, which poses additional challenges. Finally, the systems we try to model are usually very complex; however, we often use over-simpli
Prof. Ram Bilas Misra
State University of New York, Republic of Korea
Dr. R.M.L. Avadh University, Faizabad, U.P. India
Prof. Bijay Singh
Punjab Agricultural University, India
Prof. Chandra K. Jaggi
University of Delhi, New Delhi, India
Er. Avadhesh Kumar Maurya
Gautam Buddha Technical University, Lucknow, U.P., India