Disasters especially from natural phenomena are inevitable. The affected areas recover from the aftermath of a natural disaster with the support from various agents participating in humanitarian operations. There are several domains of the operation, and distributing relief aids is one. For distribution, satisfying the demand for relief aid is important since the condition of the environment is unfavorable to affected people and resources needed for the victim’s life are scarce. However, it becomes problematic when the logistic agents believed to be work properly fail to deliver the emergency goods because of the capacity loss induced from the environment after disasters. This study was proposed to address the problem of logistic agents’ unexpected incapacity which hinders scheduled distribution. The decrease in a logistic agent’s supply capability delays<br>achieving the goal of supplying required relief goods to the affected people which further endangers them. Regarding the stated problem, this study explored the importance of<br>setting the profile of logistic agents that can survive for certain duration of times. Therefore, this research defines the “survivability” and the profile of logistic agents for surviving the last mile distribution through agent based modeling and simulation. Through simulations, this study uncovered that the logistic exercise could gain survivability with the certain number and organization of logistic agents. Proper formation of organization establish the logistics’ survivability, but excessive size can threaten the survivability.

<div>The problem of optimally placing sensors can often be formulated as a facility location problem. In the literature of operations research, facility location problems are mathematical optimization problems where one or more facilities must be placed in relation to a given number of demand points or customers. Within the context of sensor placement, for example, this translates to placing wireless communication nodes that connect to a set of users or placing smoke detectors to adequately cover a region for safety assurances. However, while the classical facility location problem has been extensively studied, its direct applicability to and effectiveness for the optimal sensor placement problem can be diminished when real-world uncertainties are considered. In addition, the physics of the underlying systems in optimal sensor placement problems can directly impact the effectiveness of facility location formulations. Extensions to existing location formulations that are tailored for the system of interest are necessary to ensure optimal sensor network design.</div><div><br></div><div>This dissertation focuses on developing and applying problem-specific optimal sensor placement methods under uncertainty in sensor performance. With the classical discrete facility location problems as a basis, our models are formulated as mixed-integer linear and nonlinear programs that, depending on the specific application, can also be in the form of a stochastic program, a robust optimization framework, or require probability distributions for uncertain parameters. We consider optimal placement problems from three different areas, particularly the optimal placement of data concentrators in Smart Grid communications networks, the optimal placement of flame detectors within petrochemical facilities, and the optimal selection of infectious disease detection sites across a nation. For each application, we carefully consider the underlying physics of the system and the uncertainties and then develop extensions of previous sensor placement formulations that effectively handle these qualities. In addition, depending on the degree of nonlinear complexity of the problem, specific relaxations and iterative solution strategies are developed to improve the ability to find tractable solutions. All proposed models are implemented in Pyomo, a Python-based optimization modeling language, and solved with state-of-the-art optimization solvers, including IPOPT, Gurobi, and BARON for nonlinear, mixed-integer, and mixed-integer nonlinear programs, respectively. Numerical results show that our tailored formulations for the problems of interest are effective in handling uncertainties and provide valuable sensor placement design frameworks for their respective industries. Furthermore, our extensions for placement of sensors under probabilistic failure are appropriately general for application in other areas.<br><b></b><i></i><u></u><br></div>

While the focus of this work is on turbo equalization, there is also an examination of equalization techniques including MMSE linear and DFE equalizers and Precoding. The losses and capacity associated with the ISI channel are also examined. Iterative decoding of concatenated codes is briefly reviewed and the MAP algorithm is explained.

While the focus of this work is on turbo equalization, there is also an examination of equalization techniques including MMSE linear and DFE equalizers and Precoding. The losses and capacity associated with the ISI channel are also examined. Iterative decoding of concatenated codes is briefly reviewed and the MAP algorithm is explained.

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Conceptual Modelling (CM) has received little attention in the area of Modelling and Simulation (M&S) and more specifically in Discrete Event Simulation (DES). It is widely agreed that CM is least understood despite its importance. This is however, not the case in other fields of science and engineering (especially, computer science, systems engineering and software engineering). In Computer Science (CS) alone, CM has been extensively used for requirements specification and some well-established methods are in practice. The aim of the thesis is to propose a CM framework based on the principles of software engineering and CS. The development of the framework is adapted from a well-known software engineering method called Structured Analysis and Design Technique (SADT), hence it is called SADT CM. It is argued that by adapting approaches from CS, similar benefits can be achieved in terms of formality, understanding, communication and quality. A comprehensive cross-disciplinary review of CM in CS and M&S is undertaken, which highlights the dearth of standards within M&S CM when compared to CS. Three important sub-fields of CS are considered for this purpose namely, information systems, databases and software engineering. The review identifies two potential methods that could be adopted for developing a M&S CM framework. The first method called PREView was found unsuitable for M&S CM in DES domain. Hence, the thesis concentrates on developing the framework based on SADT. The SADT CM framework is evaluated on three-in depth test cases that investigate the feasibility of the approach. The study also contributes to the literature by conducting a usability test of the CM framework in an experimental setting. A comprehensive user-guide has also been developed as part of the research for users to follow the framework.

<div>With the rapid development of clean energy technologies, various bottlenecks on supplies of related critical materials emerged. Since supply chains of critical materials often involved with multiple layers of markets with different characteristics, to better identify bottlenecks and increase critical material availability, it is vital to have better understanding and projection on these markets.</div><div>Agent-based modeling is a bottom-up approach that can imitate heterogenous objects in a changing environment. Therefore, it is an excellent tool to simulate markets with fierce competition and fast revolution. This work demonstrates the application of agent-based modeling by discussing three different topics related to critical material demand and supply induced by clean energy products.</div><div>The first application focused on LED residential lighting market. LED lighting market grew rapidly and introduced potential demand on several critical materials including indium. The work modeled consumers as heterogenous and irrational agents in network purchasing new bulbs based</div>

<div>Social influence networks are collections of entities dealing with a shared issue on which they have individual opinions. These opinions are dynamic, changing over time due to influence from other entities. Mechanisms within the network can affect how influence leads to opinion change, such as the strength and number of social ties between agents and the decision models used by an individual to process information from its neighbors. In real-world scenarios, these mechanisms are often hidden. Much effort in social network analysis involves proposing models and attempting to replicate target output data with them. Can we instead use the evolution of opinions in a network to infer these mechanisms directly?</div><div><br></div><div>This work explores how opinion change in social influence networks can be used to determine characteristics of those networks. Broadly, this is accomplished by simulating social influence networks using various designs and initial conditions to generate opinion data, and then identifying relationships between response variables and changes to the simulation inputs. Key inputs include the population size, the influence model that controls how agents change their opinions, the network structure, the activation regime that controls the sequencing of opinion updates, and probability distributions for communication errors. Analyzing the opinions of individual agents can provide insights about the individuals (microscopic), but in this work, focus is on insights into the social influence network as a complete system (macroscopic), so opinion data is aggregated according to each response variable.</div><div><br></div><div>Response variables are designed through the lens of complexity theory. Three types of complexity measurements are applied to opinion data: regression, entropy, and a new complexity measure. In each case, relationships between design factors and response variables are diverse. The influence model and the distribution of communication errors---a factor often omitted from the literature---are consistently impactful, with their various settings producing distinct profiles in time series plots of the measurements. Activation regime is impactful to some entropy measures. Network structure has little impact on the new complexity measure, and population size has little impact in general. Overall, distinctive relationships can exist between opinions and design factors. These relationships, as well as the measures and problem-solving approaches used in this work, may be helpful to analysts working to infer the properties of real-world social influence networks from the opinion data those systems generate.</div>