Healthcare system simulation using witness

Simulation techniques have a proven track record in manufacturing industry as well as other areas such as healthcare system improvement. In this study, simulation model of a health center in Malaysia is developed through the application of WITNESS simulation software which has shown its flexibility and capability in manufacturing industry. Modelling procedure is started through process mapping and data collection and continued with model development, verification, validation and experimentation. At the end, final results and possible future improvements are demonstrated

Tactical production planning in a hybrid Make-to-Stock-Make-to-Order environment under supply, process and demand uncertainties: a robust optimisation model

In this paper, we consider a hybrid Make-to-Stock-Make-to-Order environment to develop a novel optimisation model for medium-term production planning of a typical multi-product firm based on the competencies of the robust optimisation methodology. Three types of uncertainties: Suppliers, processes and customers, are incorporated into the model to construct a robust practical model in an uncertain business environment. The modelling procedure is started with applying deterministic linear programming to develop a new multi-objective approach for the combination of multi-product multi-period production planning and aggregate production planning problems. Then, the proposed deterministic model is transformed into a robust optimisation framework and the solution procedure is designed according to the Lp-Metric methodology. Next, using the IBM ILOG CPLEX optimisation software, the proposed model is evaluated by applying the data collected from an industrial case study. Final results illustrate the applicability of the proposed model

An analytical approach to calculate the charge density of biofunctionalized graphene layer enhanced by artificial neural networks

Graphene, a purely two-dimensional sheet of carbon atoms, as an attractive substrate for plasmonic nanoparticles is considered because of its transparency and atomically thin nature. Additionally, its large surface area and high conductivity make this novel material an exceptional surface for studying adsorbents of diverse organic macromolecules. Although there are plenty of experimental studies in this field, the lack of analytical model is felt deeply. Comprehensive study is done to provide more information on understanding of the interaction between graphene and DNA bases. The electrostatic variations occurring upon DNA hybridization on the surface of a graphene-based field-effect DNA biosensor is modeled theoretically and analytically. To start with modeling, a liquid field effect transistor (LGFET) structure is employed as a platform, and graphene charge density variations in the framework of linear Poisson- Boltzmann theories are studied under the impact induced by the adsorption of different values of DNA concentration on its surface. At last, the artificial neural network is used for improving the curve fitting by adjusting the parameters of the proposed analytical model