In this thesis we consider a set of mobile users that employ cloud-based computation offloading. In computation offloading, user energy consumption can be decreased by uploading and executing jobs on a remote server, rather than processing the jobs locally. In order to execute jobs in the cloud however, the user uploads must occur over a base station channel which is shared by all of the uploading users. Since the job completion times are subject to hard deadline constraints, this restricts the feasible set of jobs that can be remotely processed, and may constrain the users ability to reduce energy usage. The system is modelled as a competitive game in which each user is interested in minimizing its own energy consumption. The game is subject to the real-time constraints imposed by the job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The thesis shows that for a variety of parameters, a game where each user independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-Seidel method for determining this equilibrium is introduced. Results are presented which illustrate that the system always converges to a Nash equilibrium using the Gauss-Seidel method. Data is also presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions. We find that the solutions perform well compared to a lower bound on total energy performance. / Thesis / Master of Applied Science (MASc)

In this dissertation, we study several quantitative approaches centered on supply chain management, sustainability development, performance measurement frameworks, and environmental regulation mechanisms. The topic of sustainability has been of great interest for the past few years in academia. Many governments also have taken actions to incentivize firms to reduce their negative environmental and social impacts. It is unclear, however, how successful policy makers have been in reducing the sustainability threats. This raises the question of ``how can policy makers play an effective role in helping businesses become more sustainable, while complying with entrepreneurs and investors' expectations?'' This dissertation is organized on the basis of six chapters. Having reviewed the literature and research directions of sustainable supply chain management in Chapter 1, we present a review of sustainability performance measurement frameworks in Chapter 2. In addition to proposing a framework to assess sustainability efficiency in supply chains, we discuss research questions with a focus on the social aspect of sustainability development. In Chapter 3, we develop a two-stage data envelopment analysis model with an application to the energy sector. This approach measures relative efficiencies of a number of comparable decision makers and does not require predetermined weights of indicators. We relax some restricting assumptions used in previous studies and obtain a nonlinear problem, for which we develop a solution method. Chapter 4 investigates a more general multi-stage assessment framework that monitors suppliers, manufacturers, distributers, and retailers' sustainable practices. The major finding is developing a multi-stage data envelopment analysis to measure supply chains' sustainability efficiency. In Chapter 5, we investigate market-based schemes with a focus on curbing pollution emitted by business entities and develop a game-theoretic formulation. Finally, we summarize the major contributions of this dissertation and future research directions in Chapter 6. / Thesis / Doctor of Philosophy (PhD)

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