Design & development of a simulation model to analyse scheduling rules in an FMS in a virtual manufacturing environment : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University

Due to the rapid changes in the needs of the customer for new products, the future manufacturing systems must cope with these changes. Hence, the need for the manufacturing systems to support these changes in the products with shorter lead times within a single manufacturing facility. The Virtual Manufacturing System (VMS) is one concept which can assist in meeting these demands. The VMS concept enables the manufacturing system designers to emulate and test the performance of the future manufacturing systems. This research has given an overview of the new concepts of Virtual Manufacturing Systems and Virtual Manufacturing in general. A Virtual Reality Software tool has been used to realise the VMS concept. A Virtual Manufacturing Environment representing a Flexible Manufacturing System (FMS) has been modelled. A simulation control language is employed for developing simulation control logics and decision making control logics for the development of the FMS model. The modelled FMS is implemented and tested through simulation experiments. The testing is done by analysing the traditional scheduling rules in a manufacturing facility. Average Machine Utilisation, Mean Flow Time, Average Queue Lengths and the System Production Rate are measured as the System Performance Measures for the evaluation of the scheduling rules. This research has identified that the Virtual Manufacturing Software is a powerful tool which can identify optimum configurations and highlight potential problems before a final and expensive manufacturing system is established physically

Evaluating the Usefulness of Paratransgenesis for Malaria Control

Malaria is a serious global health problem which is especially devastating to the developing world. Mosquitoes are the carriers of the parasite responsible for the disease, and hence malaria control programs focus on controlling mosquito populations. This is done primarily through the spraying of insecticides, or through the use of insecticide treated bed nets. However, usage of these insecticides exerts massive selection pressure on mosquitoes, resulting in insecticide resistant mosquito breeds. Hence, developing alternative strategies is crucial for sustainable malaria control. Here we explore the usefulness of paratransgenesis, i.e., introducing genetically engineered bacteria which secrete anti-plasmodium molecules, inside the mosquito midgut. The bacteria enter a mosquito's midgut when it drinks from a sugar bait, i.e., a sugar solution containing the bacterium. We formulate a mathematical model for evaluating the number of such baits required for preventing an outbreak. We study scenarios where vectors and hosts mix homogeneously as well as heterogeneously. We perform a full stability analysis and calculate the basic reproductive number for both the cases. Additionally, for the heterogeneous mixing scenario, we propose a targeted bait distribution strategy. The optimal bait allocation is calculated and is found to be extremely efficient in terms of bait usage. Our analyses suggest that paratransgenesis can prevent an outbreak, and hence it offers a viable and sustainable path to malaria control

Percolation on Networks with Antagonistic and Dependent Interactions

Drawing inspiration from real world interacting systems we study a system consisting of two networks that exhibit antagonistic and dependent interactions. By antagonistic and dependent interactions, we mean, that a proportion of functional nodes in a network cause failure of nodes in the other, while failure of nodes in the other results in failure of links in the first. As opposed to interdependent networks, which can exhibit first order phase transitions, we find that the phase transitions in such networks are continuous. Our analysis shows that, compared to an isolated network, the system is more robust against random attacks. Surprisingly, we observe a region in the parameter space where the giant connected components of both networks start oscillating. Furthermore, we find that for Erdos-Renyi and scale free networks the system oscillates only when the dependency and antagonism between the two networks is very high. We believe that this study can further our understanding of real world interacting systems

Game Theoretic Analysis of Tree Based Referrals for Crowd Sensing Social Systems with Passive Rewards

Participatory crowd sensing social systems rely on the participation of large number of individuals. Since humans are strategic by nature, effective incentive mechanisms are needed to encourage participation. A popular mechanism to recruit individuals is through referrals and passive incentives such as geometric incentive mechanisms used by the winning team in the 2009 DARPA Network Challenge and in multi level marketing schemes. The effect of such recruitment schemes on the effort put in by recruited strategic individuals is not clear. This paper attempts to fill this gap. Given a referral tree and the direct and passive reward mechanism, we formulate a network game where agents compete for finishing crowd sensing tasks. We characterize the Nash equilibrium efforts put in by the agents and derive closed form expressions for the same. We discover free riding behavior among nodes who obtain large passive rewards. This work has implications on designing effective recruitment mechanisms for crowd sourced tasks. For example, usage of geometric incentive mechanisms to recruit large number of individuals may not result in proportionate effort because of free riding.Comment: 6 pages, 3 figures. Presented in Social Networking Workshop at International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India, January 201

Developments Under the Freedom of Information Act

This article reviews authors' recently developed algorithm for identification of nonlinear state-space models under missing observations and extends it to the case of unknown model structure. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. If the model structure is unknown, an approximation of it is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through a real application