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Decision making problems are amongst the most common challenges facing managers at different management levels in the organisation: strategic, tactical, and operational. However, prior reaching decisions at the operational level of the management hierarchy, operations management departments frequently have to deal with the optimisation process to evaluate the available decision alternatives. Industries with complex supply chain structures and service organisations that have to optimise the utilisation of their resources are examples. Conventionally, operational decisions used to be taken centrally by a decision making authority located at the top of a hierarchically-structured organisation. In order to take decisions, information related to the managed system and the affecting externalities (e.g. demand) should be globally available to the decision maker. The obtained information is then processed to reach the optimal decision. This approach usually makes extensive use of information systems (IS) containing myriad of optimisation algorithms and meta-heuristics to process the high amount and complex nature of data. The decisions reached are then broadcasted to the passive actuators of the system to put them in execution. On the other hand, recent advancements in information and communication technologies (ICT) made it possible to distribute the decision making rights and proved its applicability in several sectors. The market-based approach is as such a distributed decision making mechanism where passive actuators are delegated the rights of taking individual decisions matching their self-interests. The communication among the market agents is done through market transactions regulated by auctions. The system’s global optimisation, therefore, raise from the aggregated self-oriented market agents. As opposed to the centralised approach, the main characteristics of the market-based approach are the market mechanism and local knowledge of the agents. The existence of both approaches attracted several studies to compare them in different contexts. Recently, some comparisons compared the centralised versus market-based approaches in the context of transportation applications from an algorithm perspective. Transportation applications and routing problems are assumed to be good candidates for this comparison given the distributed nature of the system and due to the presence of several sources of uncertainty. Uncertainty exceptions make decisions highly vulnerable and necessitating frequent corrective interventions to keep an efficient level of service. Motivated by the previous comparison studies, this research aims at further investigating the features of both approaches and to contrast them in the context of a distributed task allocation problem in light of environmental uncertainty. Similar applications are often faced by service industries with mobile workforce. Contrary to the previous comparison studies that sought to compare those approaches at the mechanism level, this research attempts to identify the effect of the most significant characteristics of each approach to face environmental uncertainty, which is reflected in this research by the arrival of dynamic tasks and the occurrence of stochasticity delays. To achieve the aim of this research, a target optimisation problem from the VRP family is proposed and solved with both approaches. Given that this research does not target proposing new algorithms, two basic solution mechanisms are adopted to compare the centralised and the market-based approach. The produced solutions are executed on a dedicated multi-agent simulation system. During execution dynamism and stochasticity are introduced. The research findings suggest that a market-based approach is attractive to implement in highly uncertain environments when the degree of local knowledge and workers’ experience is high and when the system tends to be complex with large dimensions. It is also suggested that a centralised approach fits more in situations where uncertainty is lower and the decision maker is able to make timely decision updates, which is in turn regulated by the size of the system at hand.

Doctor of Philosophy / Department of Computing and Information Sciences / Scott A. DeLoach / Coalition formation is a key cooperative behavior of a system of multiple autonomous agents. When the capabilities of individual agents are not su fficient for the improvement of well-being of the individual agents or of the entire system, the agents can bene t by joining forces together in coalitions. Coalition formation is a technique for finding coalitions that are best fi tted to achieve individual or group goals. This is a computationally expensive task because often all combinations of agents have to be considered in order to find the best assignments of agents to coalitions. Previous research has therefore focused mainly on small-scale or otherwise restricted systems. In this thesis we study coalition formation in large-scale multi-agent systems. We propose an approach for coalition formation based on multi-agent simulation. This approach allows us to find coalitions in systems with thousands of agents. It also lets us modify behaviors of individual agents in order to better match a specific coalition formation application. Finally, our approach can consider both social welfare of the multi-agent system and well-being of individual self-interested agents. Power distribution systems are used to deliver electric energy from the transmission system to households. Because of the increased availability of distributed generation using renewable resources, push towards higher use of renewable energy, and increasing use of electric vehicles, the power distribution systems are undergoing signi ficant changes towards active consumers who participate in both supply and demand sides of the electricity market and the underlying power grid. In this thesis we address the ongoing change in power distribution systems by studying how the use of renewable energy can be increased with the help of coalition formation. We propose an approach that lets renewable generators, which face uncertainty in generation prediction, to form coalitions with energy stores, which on the other hand are always able to deliver the committed power. These coalitions help decrease the uncertainty of the power generation of renewable generators, consequently allowing the generators to increase their use of renewable energy while at the same time increasing their pro fits. Energy stores also bene t from participating in coalitions with renewable generators, because they receive payments from the generators for the availability of their power at speci fic time slots. We first study this problem assuming no physical constraints of the underlying power grid. Then we analyze how coalition formation of renewable generators and energy stores in a power grid with physical constraints impacts the state of the grid, and we propose agent behavior that leads to increase in use of renewable energy as well as maintains stability of the grid.

Double (or multiple) marginalization is often identified as the main source of a decentralized supply chain's (SC's) inefficiency. In its core lies the fact that if the agents constituting the SC choose their output prices according to the golden rule of profit maximization (that normally applies to a single firm that produces independently and sells directly to the end consumer), the prices in the SC tend to spiral up to an inefficient (equilibrium) level where both the consumer surplus and the SC's total profit are diminished. The aim of this paper is to analyze and quantify the impact of multiple marginalization on the behaviour of SC's that vary with respect to their structure (i.e. the number of agents and the links between them) and the shape of their cost and demand functions. The main gauge of this impact is the efficiency of a SC, defined as the ratio of the profit of a SC whose agents behave according to the model of multiple marginalization, and the potential profit of the SC (i.e. the maximum profit attainable under the conditions of complete coordination of prices within the chain). Besides efficiency, some other properties of a SC are studied, e.g. the distribution of the SC's profit among the individual agents or cost externalities within the SC. Three different models of multiple marginalization are studied in the paper. The first one is a linear model of multiple marginalization (i.e. a model with linear demand and cost functions); in this simplified setting we derived explicit formulae for values of the studied indicators. The second model is analogous to the first one only that it allows for non-linear demand and cost functions; in this case, the analysis is carried out using computer experiments with numeric algorithms. The last one is a dynamic model of multiple marginalization which studies the abovementioned price spiral through multi-agent simulation.

This thesis provides a software prototype of Container Terminal Management system with the help of a Multi Agent systems technology. The goal that has been tried to achieve during this research work was to solve the management issues residing in a CT. The software prototype can be implemented as simulation software that will help the Terminal Managers to take necessary decisions for the better productivity of CT. The CTs are struggling with taking proper management decisions. There are many policies implemented but the use of a certain policy at a proper time is the main issue. It is possible with simulation software to visualize the affects of decisions taken by the implementation of a policy and see the expected output. This can really improve the performance of a CT. The management decision problem is solved by modeling the whole CT in a computer modeling language. The prototype shows all the actors appearing in a CT in the form of Agents and these agents are responsible for carrying out certain tasks. The prototype is the final contribution along with partial implementation. The model is proposed to be a web based system which removes the platform dependability problem and provide availability online. / Qazi Sohail Ahmad: qazisohail101@gmaili.com Syed Sikandar Bakht: sikandarbakht101@gmail.com

This thesis provides a software prototype of Container Terminal Management system with the help of a Multi Agent systems technology. The goal that has been tried to achieve during this research work was to solve the management issues residing in a CT. The software prototype can be implemented as simulation software that will help the Terminal Managers to take necessary decisions for the better productivity of CT. The CTs are struggling with taking proper management decisions. There are many policies implemented but the use of a certain policy at a proper time is the main issue. It is possible with simulation software to visualize the affects of decisions taken by the implementation of a policy and see the expected output. This can really improve the performance of a CT. The management decision problem is solved by modeling the whole CT in a computer modeling language. The prototype shows all the actors appearing in a CT in the form of Agents and these agents are responsible for carrying out certain tasks. The prototype is the final contribution along with partial implementation. The model is proposed to be a web based system which removes the platform dependability problem and provide availability online.

The advent of on-demand transport modes such as ride-hailing and microtransit has challenged forecasters to develop new methods of forecasting the use and impacts of such modes. In particular, there is some professional disagreement about the relative role of activity-based transportation behavior models -- which have detailed understanding of the person making a trip and its purpose -- and multi-agent demand simulations which may have a better understanding of the availability and service characteristics of on-demand services. A particular question surrounds how the relative strengths of these two approaches might be successfully paired in practice. Using daily plans generated by the activity-based model ActivitySim as inputs to the BEAM multi-agent simulation, we construct nine different methodological combinations by allowing the choice to use a pooled ride-hail service in ActivitySim, in BEAM with different utility functions, or in both. Within each combination, we estimate ride-hailing ridership and level of service measures. The results suggest that mode choice model structure drastically affects ride-hailing ridership and level of service. In addition, we see that multi-agent simulation overstates the demand interest relative to an activity-based model, but there may be opportunities in future research to implement feedback loops to balance the ridership and level of service forecasts between the two models.

The Tropical Andes continue to suffer the most radical climatic changes in South America. These changes generate alterations in its ecosystems, and therefore affect local populations, whose livelihoods are dependent on its diversity and functioning. This is particularly true for rural populations who rely on agriculture as their primary source of food and income. Although the biophysical pathways through which climate change can affect these populations have received extensive scientific attention, it is urgent to study the socioeconomic pathways, at scales that allow the development of vulnerability reduction strategies at the local level. The present study is part of the INCA project (International Network on Climate Change), which is a research network that analyses the local strategies of farmers under a changing climate in the Tropical Andes (Lindner et al. 2017). To contribute to this goal this study investigates climate-related vulnerability and climate change adaptation at local scales. First, the current vulnerability of farm household systems (FHSs) to climate-related hazards is assessed. This is done by looking at determinants that are internal (adaptive capital) and external (climate-related hazards) to the FHSs. Based on the recurrence of internal factors, FHSs are categorized into different groups. These groups are validated by observing the effects of climatic events that are specific to each group. The result of the analysis are different typologies or archetypes of climate-related vulnerability. The analysis adopts an archetype approach and develops methods based on multivariate analysis techniques. Second, the study analyzes the impacts of climate change, expressed as an increase in temperature conditions, at local levels. For this purpose, a multi-agent systems model of land-use/cover change is used, specifically the software package MPMAS. The model is the first attempt at a detailed representation of agents-environment interactions in the framework of climate change in the Tropical Andes. The simulation outcomes report on the adaptation of different farm household groups and the effects of climate change on the agricultural landscape. The research was conducted in selected communities in the Central Andes of Peru. The active integration of empirical data with secondary literature in the application of the research methods provided a suitable way to analyze the vulnerability and adaptive capacity of FHSs in the Tropical Andes in a comprehensive manner. Moreover, the use of participatory assessment techniques to obtain empirical data provided an additional perspective for the analysis and improved the understanding of the problem, contributing to deriving analytical generalizations that could hardly be obtained using only quantitative methods. The research results for the study area identify five archetypes of farm household’s vulnerability to climate-related hazards. For each archetype, distinct vulnerability-creating mechanisms are observed. For example, most vulnerable farm households have a very limited amount of adaptive capital: low levels of off-farm employment, few farm animals, small agricultural area, mostly rainfed, and low use of agro-ecological zones. In addition, they occupy predominantly the higher, and therefore less-productive, agro-ecological zones of the watershed. The analysis also makes it possible to derive spatial and thematic priorities for vulnerability reduction that are specific to each archetype. The modeling approach applied proved to be suitable for simulating the impacts of climate change at the local level. In particular, regarding the explicit simulation of FHSs, the productive landscape, and the way in which they interrelate and change in response to an increase in temperature conditions. The incorporation of heterogeneity and dynamics in the modeled population, the use of optimization techniques to simulate decision making, and the multi-periodicity of the model produce non-linearity, uncertainty and trajectory dependence. In addition, the use of vulnerability archetypes is a novel and robust way of creating a heterogeneous population for the initialization of the model. Simulation results show dynamic changes in the agricultural landscape as temperature increases. The area allocated to corn and olluco expands, while potato and oat areas diminish. Investment in tree plantations is largely unaffected. The effects of rising temperatures on farm households’ welfare show a general persistence of poverty in the study area. However, the effect on FHSs income is predominantly positive, allowing some to improve their food poverty position. The FHSs that manage to benefit from an increase in temperature have, on average, larger agricultural and forest areas, a greater amount of savings in the form of animals, hire more salaried labor and practice more mechanized agriculture than the FHSs whose situation did not improve. The results show that, in addition to the effects of climate change on crop productivity, there are other factors influencing land use decisions that deserve more attention in the analysis of vulnerability and climate change impacts. A better understanding of heterogeneity in climate vulnerability and climate impacts is an important step in meeting this demand.

The onset of Industry 4.0 has provided considerable benefits to Intelligent Cyber-Physical Systems (ICPS), with technologies such as internet of things, wireless sensing, cognitive computing and artificial intelligence to improve automation and control. However, with increasing automation, the “human” element in industrial systems is often overlooked for the sake of standardization. While automation aims to redirect the workload of human to standardized and programmable entities, humans possess qualities such as cognitive awareness, perception and intuition which cannot be automated (or programmatically replicated) but can provide automated systems with much needed robustness and sustainability, especially in unstructured and dynamic environments. Incorporating tangible human skills and knowledge within industrial environments is an essential function of “Human-in-the-loop” (HITL) Systems, a term for systems powerfully augmented by different qualities of human agents. The primary challenge, however, lies in the realistic modelling and application of these qualities; an accurate human model must be developed, integrated and tested within different cyber-physical workflows to 1) validate the assumed advantages, investments and 2) ensure optimized collaboration between entities. Agricultural Robotic Systems (ARS) are an example of such cyber-physical systems (CPS) which, in order to reduce reliance on traditional human-intensive approaches, leverage sensor networks, autonomous robotics and vision systems and for the early detection of diseases in greenhouse plants. Complete elimination of humans from such environments can prove sub-optimal given that greenhouses present a host of dynamic conditions and interactions which cannot be explicitly defined or managed automatically. Supported by efficient algorithms for sampling, routing and search, HITL augmentation into ARS can provide improved detection capabilities, system performance and stability, while also reducing the workload of humans as compared to traditional methods. This research thus studies the modelling and integration of humans into the loop of ARS, using simulation techniques and employing intelligent protocols for optimized interactions. Human qualities are modelled in human “classes” within an event-based, discrete time simulation developed in Python. A logic controller based on collaborative intelligence (HUB-CI) efficiently dictates workflow logic, owing to the multi-agent and multi-algorithm nature of the system. Two integration hierarchies are simulated to study different types of integrations of HITL: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks and classification accuracy are measured and compared for different collaboration protocols within each hierarchy, to verify the impact of chosen sampling and search algorithms. The experiments performed show the statistically significant advantages of HUB-CI based protocol over traditional protocols in terms of collaborative task performance and disease detectability, thus justifying added investment due to the inclusion of HITL. The results also discuss the competitive factors between both integrations, laying out the relative advantages and disadvantages and the scope for further research. Improving human modelling and expanding the range of human activities within the loop can help to improve the practicality and accuracy of the simulation in replicating an HITL-ARS. Finally, the research also discusses the development of a user-interface software based on ARS methodologies to test the system in the real-world.<br>

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