In activity based modelling the concept of rescheduling is very important in order to gain dynamic scheduling of activities and to adjust the effect of unexpected incidents in individual agendas to keep them realistic and valid. This report describes a new framework to investigate algorithms for rescheduling on a large scale. This framework models the information of traffic demand and results of micro simulation of traffic on a loaded network; it enables agents to adapt their schedules by providing them with information about the traffic flow. A perception filter for each agent is included in this framework. It models the concept that some agents can notice the broadcast traffic information about the incident and get their own prediction of the expected delay, while other agents who do not notice the information can become aware only by experiencing traffic jam. Initial agendas are created by means of the FEATHERS activity based schedule generator for mutually independent agents. FEATHERS has no knowledge about the actual transportation network but makes use of an impedance matrix that specifies the minimal travel time between traffic analysis zones. The matrix specifies a free-flow value for the uncongested case and correction values for the loaded network. In this new framework the network state can be changed by agent behaviour and external incidents; the effect of this change in network state is perceived differently by each agent through a perception filter, and according to the perceived value individual adaption is calculated by a ReScheduler. The modified behaviour again creates new traffic demand hence creating a new traffic state; this phenomenon continues for the complete day. Each activity in the agenda is assumed to generate some utility. Each individual is assumed to maximize the total utility over the day. The ReScheduler is implemented using a marginal utility function that monotonically decreases with activity duration. This results in a monotonically converging relaxation algorithm to efficiently determine the new activity timing when less time is available for activities due to increased travel time caused by the incident.

A UAV swarm is simulated using Horizon Simulation Framework. The asset utilized for the swarm agent is a simplified model of the MQ-1 Predator, a large fixed-wing aircraft. The simulated swarm utilizes a decentralized cooperative control approach to command the assets through the use of digital pheromones and a pheromone map. Each vehicle operates at steady-state flight conditions of 36 m/s with an altitude of 1,800 m, and utilize an LQR set-point controller to maneuver through the pheromone map. All pheromone and aircraft related models are written in Python to expand the HSF scripting capability and include airborne scenarios. The simulation study focuses in the variation of three parameters in the repelling pheromone model. The first two are the update and deposit parameters with values of 2, 10, and 18. The third is the threshold parameter with values of 1e-02, 1e-10, and 1e-18. The lower parameter values provide more time-on-target while the higher parameters allow the swarm to search the surrounding area by only visiting the grid-space once.

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator.

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator.

This project presents an investigation into the viability of alternative knowl- edge acquisition strategies in knowledge management systems. The goal of this project is to illustrate that alternative means of knowledge acquisition can have a significant effect on the quality of the knowledge base. To accomplish this a modification of a wiki system, dubbed Prometheus, is proposed that uses a threshold based user vote acquisition mechanism. A simulation approach is used to compare a model of the Prometheus system against a model of a standard wiki system. A simulation framework is described that facilitates comparison between models of knowledge systems. The simu- lation framework is used to compare the knowledge systems in three different scenarios in an attempt to determine the conditions in which the Prometheus system may produce a higher quality knowledge base. The results of these ex- periments are presented along with some discussion and areas for future work.

Modeling and simulations are useful tools to help understand knee function and injuries. As there are more muscles in the human knee joint than equations of motion, optimization protocols are required to solve a problem. The purpose of this thesis was to improve the biofidelity of these simulations by adding in vivo constraints derived from experimental intra-cortical pin data and stability-inspired objective functions within an OpenSim-Matlab forward-inverse dynamics simulation framework on lower limb muscle activation predictions. Results of this project suggest that constraining the model knee joint’s ranges of motion with pin data had a significant impact on lower limb kinematics, especially in rotational degrees of freedom. This affected muscle activation predictions and knee joint loading when compared to unconstrained kinematics. Furthermore, changing the objective will change muscle activation predictions although minimization of muscle activation as an objective remains more accurate than the stability inspired functions, at least for gait. /// La modélisation et les simulations in-silico sont des outils importants pour approfondir notre compréhension de la fonction du genou et ses blessures. Puisqu’il y a plus de muscles autour du genou humain que d’équations de mouvement, des procédures d’optimisation sont requises pour résoudre le système. Le but de cette thèse était d’explorer l’effet de changer l’objectif de cette optimisation à des fonctions inspirées par la stabilité du genou par l’entremise d’un cadre de simulation de dynamique directe et inverse utilisant MatLab et OpenSim ainsi qu'un model musculo-squelétaire contraint cinématiquement par des données expérimentales dérivées de vis intra-corticales, sur les prédictions d’activation musculaire de la jambe. Les résultats de ce projet suggèrent que les contraintes de mouvement imposées sur le genou modélisé ont démontré des effets importants sur la cinématique de la jambe et conséquemment sur les prédictions d'activation musculaire et le chargement du genou. La fonction objective de l'optimisation change aussi les prédictions d’activations musculaires, bien que la fonction minimisant la consommation énergétique soit la plus juste, du moins pour la marche.

An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR. Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR. Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers' heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms. One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers' comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result. Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR. / PHD / The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than the load, the match can be restored either by increasing the generation or by decreasing the load. Often, during system stress conditions, it is cheaper to decrease certain loads than to increase generation, and this method of achieving power balance is called demand response (DR). Residential sector consumes 37% of the total U.S. electricity consumption and is largely unexplored for demand response purpose, so the focus of the dissertation is on providing solutions to enable residential houses to provide demand response services. This dissertation presents two broad solutions. The first is a set of efficient algorithms that intelligently controls the customers’ heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid while keeping their comfort in mind. The second solution is a simulation software that can help evaluate and experiment with different residential demand response algorithms. The first algorithm is for reducing the collective power consumption of an aggregation of residential HVAC, whereas the second algorithm is for making the collective power follow a signal sent by the grid operators. It is shown that the algorithms presented can intelligently control the HVAC devices such that DR services can be provided to the grid while ensuring that the temperatures of the houses remain within comfortable range. The algorithms can enable demand response service providers to tap into the residential demand response market and earn revenue, while the simulation software can be valuable for future research in this area. The simulation software is simple to use and is designed with extensibility in mind, so adding new features is easy. The software is shown to work well for studying residential building control for demand response purpose and can be a useful tool for future research in residential DR.

Much research has been undertaken to gain insight into business alignment of IT. This alignment basically aims to improve a firm’s performance by an improved harmonization of the business function and the IT function within a firm. The thesis discusses previous approaches and constructs an overall framework, which a potential approach needs to fit in. Being in a highly regulated industry, for airports there is little space left to increase revenues. However, the retailing business has proven to be an area that may contribute towards higher income for airport operators. Consequently, airport management should focus on supporting this business segment. Nevertheless, it needs to be taken into account that smooth airport operations are a precondition for successful retailing business at an airport. Applying the concept of information intensity, the processes of gate allocation and airport retailing have been determined to appraise the potential that may be realized upon (improved) synchronization of the two. It has been found that the lever is largest in the planning phase (i.e. prior to operations), and thus support by means of information technology (for information distribution and improved planning) may help to enable an improved overall retail performance. In order to determine potential variables, which might influence the output, a process decomposition has been conducted along with the development of an appropriate information model. The derived research model has been tested in different scenarios. For this purpose an adequate gate allocation algorithm has been developed and implemented in a purposewritten piece of software. To calibrate the model, actual data (several hundred thousand data items from Frankfurt Airport) from two flight plan seasons has been used. Key findings: The results show that under the conditions described it seems feasible to increase retail sales in the magnitude of 9% to 21%. The most influential factors (besides the constraining rule set and a retail area’s specific performance) proved to be a flight’s minimum and maximum time at a gate as well as its buffer time at gate. However, as some of the preconditions may not be accepted by airport management or national regulators, the results may be taken as an indication for cost incurred, in case the suggested approach is not considered. The transferability to other airport business models and limitations of the research approach are discussed at the end along with suggestions for future areas of research.

This monograph contributes to the methodology of Agent-based Computational Economics. First, we introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling, and flexible specification of design parameters. For the latter, we use an XML-based format enabling the design of flexible models, with the possibility of varying both agent population and parameterization. Further, the tool allows the definition of communication channels to single agents, or groups thereof, and handles the information exchange. Both agents and communication channels can be added and removed at runtime. To handle the heterogeneity arising from both the agents' implementations and the underlying platforms, we introduce an XML-based wrapper technique for mapping the functionality of agents living in an interpreter-based environment to a standardized JAVA interface. Second, we present a collection of artificial economic actors to be used with this framework. Their interplay is demonstrated in two fields of management science: marketing and technological choice. In the field of marketing, the question of choosing the optimal segmentation techniques for market segmentation is investigated, comparing the performance of firm agents with diverse segmentation strategies in a highly customizable artificial consumer market. In the second application, we study the influence of technological efficiency and organizational inertia on the emergence of competition when firms decide myopically. We observe the competitive reaction of a former monopolist to the advent of a new competitor to assess when new, "disruptive" technologies cause the failure of incumbent firms and investigate simple defensive strategies. (author's abstract)

Much research has been undertaken to gain insight into business alignment of IT. This alignment basically aims to improve a firm’s performance by an improved harmonization of the business function and the IT function within a firm. The thesis discusses previous approaches and constructs an overall framework, which a potential approach needs to fit in. Being in a highly regulated industry, for airports there is little space left to increase revenues. However, the retailing business has proven to be an area that may contribute towards higher income for airport operators. Consequently, airport management should focus on supporting this business segment. Nevertheless, it needs to be taken into account that smooth airport operations are a precondition for successful retailing business at an airport. Applying the concept of information intensity, the processes of gate allocation and airport retailing have been determined to appraise the potential that may be realized upon (improved) synchronization of the two. It has been found that the lever is largest in the planning phase (i.e. prior to operations), and thus support by means of information technology (for information distribution and improved planning) may help to enable an improved overall retail performance. In order to determine potential variables, which might influence the output, a process decomposition has been conducted along with the development of an appropriate information model. The derived research model has been tested in different scenarios. For this purpose an adequate gate allocation algorithm has been developed and implemented in a purposewritten piece of software. To calibrate the model, actual data (several hundred thousand data items from Frankfurt Airport) from two flight plan seasons has been used. Key findings: The results show that under the conditions described it seems feasible to increase retail sales in the magnitude of 9% to 21%. The most influential factors (besides the constraining rule set and a retail area’s specific performance) proved to be a flight’s minimum and maximum time at a gate as well as its buffer time at gate. However, as some of the preconditions may not be accepted by airport management or national regulators, the results may be taken as an indication for cost incurred, in case the suggested approach is not considered. The transferability to other airport business models and limitations of the research approach are discussed at the end along with suggestions for future areas of research.