The ambition of this project is to contribute to the development of optimisation techniques for underground mining. This resulted in a mathematical model to optimise a type of underground transportation system called the ramp. The ramp is a tunnel from the underground mining areas which trucks use to transport material up to the surface. We consider the case where the ramp only fits one truck at a time and it therefore needs passing bays where trucks can meet. We were inspired by an article which optimised the positions of the passing bays and the schedule for the trucks, during a certain time period. We extended that work by proposing a new mathematical model that can handle a more general and complex mine. The result from optimally solving the model gives the positioning of the passing bays and a schedule which completes a number of trips down and up the ramp as quickly as possible. The model can be used both for long-term and short-term planning. The long-term planning regards the positions of the passing bays. The model can therefore be used before the passing bays are constructed to gain insights about where to place them. The short-term planning is about finding an optimal trip schedule given the placement of the passing bays. The model can therefore also be used to provide a haulage schedule for an upcoming time period.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19112号 / 情博第558号 / 新制||情||99(附属図書館) / 32063 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 阿久津 達也, 教授 岡部 寿男, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This report considers an application of mixed-integer disjunctive programming (MIDP)where a theoretical robot can jump from one point to another and where the number ofjumps is to be minimized. The robot is only able to jump to the north, south, east andwest. Furthermore, the robot should also be able to navigate and jump around or across anypotential obstacles on the way. The algorithm for solving this problem is set to terminatewhen the robot has reached a set of end coordinates. The goal of this report is to find amethod for solving this problem and to investigate the time complexity of such a method.The problem is converted to big-M representation and solved numerically. Gurobi is theoptimization solver used in this thesis. The model created and implemented with Gurobiyielded optimal solutions to problems of the form above of varying complexity. For most ofcases tested, the time complexity appeared to be linear, but this is likely due to presolvingperformed by Gurobi before running the optimization. Further tests are needed to determinethe time complexity of Gurobi’s optimization algorithm for this specific type of problem.

This research presents a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse resource constrained cyber network defender to optimally deploy security countermeasures to protect against attackers having an uncertain budget. This risk-averse conditional-value-at-risk model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop an exact algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Computational experiments demonstrate that the application of all the acceleration techniques reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, numerical results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains.

In this era of constant evolution of Internet, Network Virtualization is a powerful platform for the existence of heterogeneous and customized networks on a shared infrastructure. Virtual network embedding is pivotal step for network virtualization and also enables the usage of virtual network mapping techniques. The existing state- of-the-art mapping techniques addresses the issues relating to bandwidth, processing capacity and location constraints very effectively. But due to the advancement of real- time and delay sensitive applications on the Internet, there is a need to address the issue of delay in virtual network mapping techniques. As none of the existing state- of-the-art mapping algorithms do not address this issue, in this thesis we address this issue using VHub-Delay and other mapping algorithms. Based on the study and observations, we designed a new mapping technique that can address the issue of delay and finally the effectiveness of the mapping technique is validated by extensive simulations.

Nowadays Network Virtualization provides a new perspective for running multiple, relatively independent applications on same physical network (the substrate network) within shared substrate resources. This method is especially useful for researchers or investigators to get involved into networking field within a lower barrier. As for network virtualization, Virtual Network Mapping (VNM) problem is one of the most important aspects for investigation. Within years of deeply research, several efficient algorithms have been proposed to solve the Virtual Network Mapping problem, however, most of the current mapping algorithm assumes that the virtual network request topology is known or given by customers, in this thesis, a new VNM assumption based on traffic matrix is proposed, also using existing VNM benchmarks, we evaluated the mapping performance based on various metrics, and by comparing the new traffic matrix based VNM algorithm and existing ones, we provide its advantages and shortcomings and optimization to this new VNM algorithm.

An Open Locating-Dominating Set (OLD set) is a subset of vertices in a graph such that every vertex in the graph has a neighbor in the OLD set and every vertex has a unique set of neighbors in the OLD set. This can also represent where sensors, capable of detecting an event occurrence at an adjacent vertex, could be placed such that one could always identify the location of an event by the specific vertices that indicated an event occurred in their neighborhood. By the open neighborhood construct, which differentiates OLD sets from identifying codes, a vertex is not able to report if it is the location of the event. This construct provides a robustness over identifying codes and opens new applications such as disease carrier and dark actor identification in networks. This work explores various aspects of OLD sets, beginning with an Integer Linear Program for quickly identifying the optimal OLD set on a graph. As many graphs do not admit OLD sets, or there may be times when the total size of the set is limited by an external factor, a concept called maximum covering OLD sets is developed and explored. The coverage radius of the sensors is then expanded in a presentation of Mixed-Weight OLD sets where sensors can cover more than just adjacent vertices. Finally, an application is presented to optimally monitor criminal and terrorist networks using OLD sets and related concepts to identify the optimal set of surveillance targets.

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The goal of this thesis is to create an optimization model to optimize the routing of trains within railway stations. This problem is known as the train platforming problem, and the model we present is an integer programming model. By this model we aim to optimize factors such as walking distance, switch usage or platform usage. We validate the model by implementing the model for Linköping station, which is a typical mid size station in the Swedish railway network. This implementation is done for different time horizons, ranging from 2 hours to one day, which corresponds to train sets ranging from 27 to 265 trains. In the conclusion we see that the model is efficient for optimizing the train platforming problem for the implemented station and timetables, and that the model has a possibility to optimize the four objectives tested. Furthermore we see that optimizing certain objectives gives solutions that are also good with regards to other objective functions. / Målet med den här uppsatsen är att skapa en optimeringsmodell för att optimera valet av vägar för tåg genom tågstationer. Modellen vi presenterar är en heltalsmodell, där syftet är att minimera bland annat gångavstånd, användningen av tågväxlar eller användningen av perronger. För att testa modellen presenterar vi en implementation av modellen för stationen i Linköping, vilken är en typisk mellanstor station i det svenska tågnätet. Impplementeringen är gjord för olika tidslängder, från 2 timmar till ett dygn vilket motsvarar dataset från 27 till 265 tåg. Vi drar slutsatsen att modellen på ett effektivt sätt kan lösa valet av tågvägar genom stationen, för de fyra tidtabeller och den station vi har implementerat. Vidare ser vi att modellen har potential att optimera de fyra målfunktioner vi testat och att optimering av några av målfunktionerna ger lösningar som är bra även med hänsyn till de andra målfunktionerna.