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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.

Natural disasters and extreme events are often characterized by their violence and unpredictability, resulting in consequences that in severe cases result in devastating physical and ecological damage as well as countless fatalities. In August 2005, Hurricane Katrina hit the Southern coast of the United States wielding serious weather and storm surges. The brunt of Katrina’s force was felt in Louisiana, where the hurricane has been estimated to total more than $108 billion in damage and over 1,800 casualties. Hurricane Rita followed Katrina in September 2005 and further contributed $12 billion in damage and 7 fatalities to the coastal communities of Louisiana and Texas. Prior to making landfall, residents of New Orleans received a voluntary, and then a mandatory, evacuation order in an attempt to encourage people to move themselves out of Hurricane Katrina’s predicted destructive path. Consistent with current practice in nearly all states, this evacuation order did not include or convey any information to individuals regarding route selection, shelter availability and assignment, or evacuation timing. This practice leaves the general population free to determine their own routes, destinations and evacuation times independently. Such freedom often results in inefficient and chaotic utilization of the roadways within an evacuation region, quickly creating bottlenecks along evacuation routes that can slow individual egress and lead to significant and potentially dangerous exposure of the evacuees to the impending storm. One way to assist the over-burdened and over-exposed population during extreme event evacuation is to provide an evacuation strategy that gives specific information on individual route selection, evacuation timing and shelter destination assignment derived from effective, strategic pre-planning. For this purpose, we present a mixed integer linear program to devise effective and controlled evacuation networks to be utilized during extreme event egress. To solve our proposed model, we develop a solution methodology based on Benders Decomposition and test its performance through an experimental design using the Central Texas region as our case study area. We show that our solution methods are efficient for large-scale instances of realistic size and that our methods surpass the size and computational limitations currently imposed by more traditional approaches such as branch-and-cut. To further test our model under conditions of uncertain individual choice/behavior, we create an agent-based simulation capable of modeling varying levels of evacuee compliance to the suggested optimal routes and varying degrees of communication between evacuees and between evacuees and the evacuation authority. By providing evacuees with information on when to evacuate, where to evacuate and how to get to their prescribed destination, we are able to observe significant cost and time increases for our case study evacuation scenarios while reducing the potential exposure of evacuees to the hurricane through more efficient network usage. We provide discussion on scenario performance and show the trade-offs and benefits of alternative batch-time evacuation strategies using global and individual effectiveness measures. Through these experiments and the developed methodology, we are able to further motivate the need for a more coordinated and informative approach to extreme event evacuation.

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.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23320号 / 情博第756号 / 新制||情||129(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 伊藤 孝行, 教授 畑山 満則 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21356号 / 工博第4515号 / 新制||工||1703(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 准教授 QURESHI,Ali Gul, 准教授 SCHMOECKER,Jan-Dirk / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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.