Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

A stochastic averaging procedure for obtaining the probability density function (PDF) of the response for a strongly nonlinear single-degree-of-freedom system, subjected to both multiplicative and additive random excitations is presented. The procedure uses random Van Der Pol transformation, Ito’s equation of limiting diffusion process and stochastic averaging technique as outlined by Zhu and others. However, the equations are rederived in generalized form and arranged in such a way that the procedure lends itself to a numerical computational scheme using FFT. The main objective of the modification is to consider highly irregular nonlinear functions which cannot be integrated in closed form and also to solve problems where analytical expressions for probability density function cannot be obtained. The procedure is applied to obtain the PDF of the response of Duffing oscillator subjected to additive and multiplicative random excitations represented by rational power spectral density functions (PSDFs). The results are verified by digital simulation. It is shown that the procedure provides results which compare very well with those obtained from simulation analysis not only for wide-band excitations but also for very narrow-band excitations, which are weak (when normalized with respect to mass of the system.)

Particle swarm optimization (PSO) is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The central idea is to use PSO to move in the direction towards optimal solution rather than searching the entire feasibleregion. Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or classification boundary for a data set. Our results on the Iris, Pima, Wine, Thyroid, Balance, Bupa, Haberman, and TAE datasets show that the proposed method works better than a neural network and the performance is close to that of a support vector machine

Computations of incompressible fluid flow and heat transfer around a square obstacle with a near by adiabatic wall have been performed in a horizontal plane. The ranges of dimensionless control parameters considered are Prandtl number (Pr) = 10–100, Reynolds number (Re) = 1–150 and gap ratio (G) = 0.25–1.The steady-flow regime is observed up to Re = 121 for G = 0.5, and beyond this Re, time-periodic regime is observed. The shift to a time-periodic regime from a steady regime occurred at greater Re than that for an unconfined square obstacle. With increasing Pr, increase in average Nusselt number values is recorded for all Re and G studied. The heat transfer augmentation is approximately 1332% at Re = 150 (Pr = 100, G = 0.25) with regard to the corresponding values at Re = 1. Lastly, a correlation for j_h factor is determined for the preceded conditions.

In this paper, a new model order reduction technique is presented by combining the benefits of the meta-heuristic cuckoo search optimization and Eigen permutation methods for order reduction of higher order continuous-time systems. In the proposed approach, the numerator and the denominator polynomials of reduced order model are determined by Cuckoo search and Eigen permutation approaches, respectively. The proposed approach preserves the stability of the original system into the lower order model as the Eigen permutationretains the dominant pole with simultaneous cluster formation of the remaining real and complex poles. The effectiveness of the proposed method is validated by single-input single-output and multiple-inputs multiple-outputs numerical examples.

In the present letter, we model the pre-stretch and frequency parameter effect on the dielectric permittivity of a dielectric elastomer. The present work is the extension of our most recent work (Kumar and Sarangi in Mech Mater 128:1–10, 2019) in case of deformable smart material. In particular, pre-stretching aswell as frequency parameter variables majorly affect the deformation mechanism of a dielectric elastomeric material. In line with this, we first develop a new amended permittivity model of a dielectric elastomeric material based on the fundamental laws of physics. This amended permittivity model successfully enrols the prestretch and frequency parameter effect on the dielectric permittivity of a dielectric elastomer. Additionally, the amended permittivity model also successfully resolves the existing numerical inaccuracies of the previously known permittivity models in large deformations. Next, the formulated amended permittivity model is calibrated with some available experimental data, and compared to a known permittivity model existing in the literature.