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.

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Workforce scheduling is widely used within most industries. A well-outlined and efficient schedule gives cost savings, such as reduced number of overtime hours, increases overall utilization, and facilitates meeting demands. A large and complex schedule, for example, scheduling of a health care workforce, needs to consider many parameters when constructed; it is essential to account for all critical constraints regarding who can dispense a particular medicine, laws restricting the health care system, etcetera. This thesis evaluates two different methods for implementing a workforce scheduling system for one of Linköping’s most well-known restaurants and bars for students, using mixed integer programming and heuristics. Flamman Pub & Disco recruits new employees prior to every semester. Usually, the workforce consists of around 100 employees, and the vast majority of them work either in the bar or in the kitchen. Historically, the scheduling process has been handled manually using Excel. This does, however, take up much time for the operations manager, something considered frowned upon. Therefore, this thesis suggests an automated scheme for future scheduling processes. Because Flamman is a student organization, they do not hold the capital to invest in expensive licensed optimization software. However, literature studies have shown that heuristics such as large neighborhood search can generate sufficient performance, and therefore the investigation of free-of-charge software using a heuristic approach is conducted. The constructed framework uses a mixed integer programming model, which also lays the cornerstone for the two heuristics: a reverse constructive heuristic and a large neighborhood search. The results retrieved from the analysis prove that a heuristic can be a helpful tool for upcoming recruitment periods. There are, however, recommended areas for improvement regarding the current state of the heuristic.

Hundreds of steel products need to be scheduled on a single or parallel machine in a steel plant every week. A good feasible schedule may save the company millions of dollars compared to a bad one. Single and parallel machine scheduling are also encountered often in many other industries, making it a crucial research topic for both the process system engineering and operations research communities. Single or parallel machine scheduling can be a challenging combinatorial optimization problem when a large number of jobs are to be scheduled. Each job has unique job characteristics, resulting in different setup times/costs depending on the processing sequence. They also have specific release dates to follow and due dates to meet. This work presents both an exact method using mixed-integer quadratic programming, and an approximate method with metaheuristics to solve real-world large-scale single/parallel machine scheduling problems faced in a steel plant. More than 1000 or 350 jobs are to be scheduled within a one-hour time limit in the single or parallel machine problem, respectively. The objective of the single machine scheduling is to minimize a combined total changeover, total earliness, and total tardiness cost, whereas the objective of the parallel machine scheduling is to minimize an objective function comprising the gaps between jobs before a critical time in a schedule, the total changeover cost, and the total tardiness cost. The exact method is developed to benchmark computation time for a small-scale single machine problem, but is not practical for solving the actual large-scale problem. A metaheuristic algorithm centered on variable neighborhood descent is developed to address the large-scale single machine scheduling with a sliding-window decomposition strategy. The algorithm is extended and modified to solve the large-scale parallel machine problem. Statistical tests, including Student's t-test and ANOVA, are conducted to determine efficient solution strategies and good parameters to be used in the metaheuristics. / Thesis / Master of Applied Science (MASc)

As exploration in space increases through the use of larger telescopes, more sophisticated structures, and physical exploration, the use of autonomous robots will become instrumental to build and maintain the infrastructures required for this exploration. These systems must be autonomous to deal with the infeasibility of teleoperation due signal delay and task complexity. The reality of using robots in the real world without direct human input will require the autonomous systems to have the capability of responding to errors that occur in an assembly scenario on their own. As such, a system must be in place to allow for the sequencing and allocation of tasks to the robotic workforce autonomously, giving the ability to re-plan in real world stochastic environments. This work presents four contributions towards a system allowing for the autonomous sequencing and allocation of tasks for in-space assembly problems. The first contribution is the development of the Stochastic Assembly Problem Definition (SAPD) to articulate all of the features in an assembly problem that are applicable to the task sequencing and allocation. The second contribution is the formulation of a mixed integer program to solve for assembly schedules that are optimal or a quantifiable measurement from optimal. This contribution is expanded through the development of a genetic algorithm formulation to utilize the stochastic information present in the assembly problem. This formulation extends the state-of-the-art techniques in genetic algorithms to allow for the inclusion of new constraints required for the in-space assembly domain. The third contribution addresses how to estimate a robot's ability to complete a task if the robot must be assigned to a task it was previously not expected to work on. This is accomplished through the development of four metrics and analyzed through the use of screw theory kinematics. The final contribution focuses on a set of metrics to guide the selection of a good scheduling method for different assembly situations. The experiments in this work demonstrate how the developed theory can be utilized and shows the scheduling systems producing the best or close to the best schedules for assemblies. It also shows how the metrics used to quantify and estimate robot ability are applied. The theory developed in this work provides another step towards autonomous systems that are capable of assembling structures in-space without the need for human input. / Doctor of Philosophy / As space exploration continues to progress, autonomous robots are needed to allow for the necessary structures to be built in-space, on Mars, and on the Lunar surface. Since it is not possible to plan for every possible thing that could go wrong or break, the robots must be able to figure out how to build and repair structures without human input. The work presented here develops a framework that allows this in-space assembly problem to be framed in a way the robots can process. It then provides a method for generating assembly schedules that describe very good, if not the best way to complete the assembly quickly while still taking into account randomness that may be present. Additionally, this work develops a way to quantify and estimate how good robots will be at a task they have not attempted before. Finally, a set of considerations are proposed to aid in determining what scheduling method will work best for different assembly scenarios. The experiments in this work demonstrate how the developed theory can be used and shows the scheduling systems producing the best or close to the best schedules for assemblies. It also shows how the methods used to define robot ability are applied. The work developed here provides another step towards autonomous systems that are capable of assembling structures in-space without the need for human input.