本論文研究股票分類與否對投資組合報酬有無差別，因此以目標規畫方式提出兩個混合整數線性規劃模型建立投資組合。在考量市場風險上，兩模型的差別在於一個是單股比重的限制，另一個是類股數目的限制。兩模型中均考慮交易數量為整數與實務中的交易成本，且採用了0-1決策變數，決定股票及類股的選取與否。並以台灣股票市場作為實證研究對象，探討兩模型投資組合在市場不同走勢下的表現，同時也觀察股票分類後，探討選幾個類股數會有較佳的績效，並分析投資組合建立後多久應該進行調整。 / This thesis studies the effect of return of a portfolio while restricting the number of asset category. Two mixed-integer linear programming models are proposed by using the goal programming technique. In consideration of the risk, the difference between these two models is that one focuses on a single stock restriction, and the other is on the asset category restriction. The integer restriction and transaction cost are included in the model while using binary decision variable to indicate the selection of an asset and the selection a category. Finally, an empirical study will be presented by applying to Taiwan’s stock market. The performances of these two models are discussed. Moreover, the best number of category in the portfolio and the best timing of rebalance are also investigated.

<p>In this thesis we address the problem of optimal code generation for irregular architectures such as Digital Signal Processors (DSPs).</p><p>Code generation consists mainly of three interrelated optimization tasks: instruction selection (with resource allocation), instruction scheduling and register allocation. These tasks have been discovered to be NP-hard for most architectures and most situations. A common approach to code generation consists in solving each task separately, i.e. in a decoupled manner, which is easier from a software engineering point of view. Phase-decoupled compilers produce good code quality for regular architectures, but if applied to DSPs the resulting code is of significantly lower performance due to strong interdependences between the different tasks.</p><p>We developed a novel method for fully integrated code generation at the basic block level, based on dynamic programming. It handles the most important tasks of code generation in a single optimization step and produces an optimal code sequence. Our dynamic programming algorithm is applicable to small, yet not trivial problem instances with up to 50 instructions per basic block if data locality is not an issue, and up to 20 instructions if we take data locality with optimal scheduling of data transfers on irregular processor architectures into account. For larger problem instances we have developed heuristic relaxations.</p><p>In order to obtain a retargetable framework we developed a structured architecture specification language, xADML, which is based on XML. We implemented such a framework, called OPTIMIST that is parameterized by an xADML architecture specification.</p><p>The thesis further provides an Integer Linear Programming formulation of fully integrated optimal code generation for VLIW architectures with a homogeneous register file. Where it terminates successfully, the ILP-based optimizer mostly works faster than the dynamic programming approach; on the other hand, it fails for several larger examples where dynamic programming still provides a solution. Hence, the two approaches complement each other. In particular, we show how the dynamic programming approach can be used to precondition the ILP formulation.</p><p>As far as we know from the literature, this is for the first time that the main tasks of code generation are solved optimally in a single and fully integrated optimization step that additionally considers data placement in register sets and optimal scheduling of data transfers between different registers sets.</p>

In this thesis we address the problem of optimal code generation for irregular architectures such as Digital Signal Processors (DSPs). Code generation consists mainly of three interrelated optimization tasks: instruction selection (with resource allocation), instruction scheduling and register allocation. These tasks have been discovered to be NP-hard for most architectures and most situations. A common approach to code generation consists in solving each task separately, i.e. in a decoupled manner, which is easier from a software engineering point of view. Phase-decoupled compilers produce good code quality for regular architectures, but if applied to DSPs the resulting code is of significantly lower performance due to strong interdependences between the different tasks. We developed a novel method for fully integrated code generation at the basic block level, based on dynamic programming. It handles the most important tasks of code generation in a single optimization step and produces an optimal code sequence. Our dynamic programming algorithm is applicable to small, yet not trivial problem instances with up to 50 instructions per basic block if data locality is not an issue, and up to 20 instructions if we take data locality with optimal scheduling of data transfers on irregular processor architectures into account. For larger problem instances we have developed heuristic relaxations. In order to obtain a retargetable framework we developed a structured architecture specification language, xADML, which is based on XML. We implemented such a framework, called OPTIMIST that is parameterized by an xADML architecture specification. The thesis further provides an Integer Linear Programming formulation of fully integrated optimal code generation for VLIW architectures with a homogeneous register file. Where it terminates successfully, the ILP-based optimizer mostly works faster than the dynamic programming approach; on the other hand, it fails for several larger examples where dynamic programming still provides a solution. Hence, the two approaches complement each other. In particular, we show how the dynamic programming approach can be used to precondition the ILP formulation. As far as we know from the literature, this is for the first time that the main tasks of code generation are solved optimally in a single and fully integrated optimization step that additionally considers data placement in register sets and optimal scheduling of data transfers between different registers sets.

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible.

The pervasiveness and operational autonomy of mesh-based wireless sensor networks (WSNs) make them an ideal candidate in offering sustained monitoring functions at reasonable cost over a wide area. To extend the functional lifetime of battery-operated sensor nodes, stringent sleep scheduling strategies with communication duty cycles running at sub-1% range are expected to be adopted. Although ultra-low communication duty cycles can cast a detrimental impact on sensing coverage and network connectivity, its effects can be mitigated with adaptive sleep scheduling, node deployment redundancy and multipath routing within the mesh WSN topology. This work proposes a cross-layer organizational approach based on sleep scheduling, called Sense-Sleep Trees (SS-Trees), that aims to harmonize the various engineering issues and provides a method to extend monitoring capabilities and operational lifetime of mesh-based WSNs engaged in wide-area surveillance applications. Various practical considerations such as sensing coverage requirements, duty cycling, transmission range assignment, data messaging, and protocol signalling are incorporated to demonstrate and evaluate the feasibility of the proposed design approach.

This thesis focuses on the survivable routing problem in WDM mesh networks where the objective is to minimize the total number of wavelengths used for establishing working and protection paths in the WDM networks. The past studies for survivable routing suffers from the scalability problem when the number of nodes/links or connection requests grow in the network. In this thesis, a novel path based shared protection framework namely Inter-Group Shared protection (I-GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy. Optimization is performed on these PGs such that sharing of protection wavelengths is considered not only inside a PG, but between the PGs. Simulation results show that I-GSP based integer linear programming model, namely, ILP-II solves the networks in a reasonable amount of time for which a regular integer linear programming formulation, namely, ILP-I becomes computationally intractable. For most of the cases the gap between the optimal solution and the ILP-II ranges between (2-16)%. The proposed ILP-II model yields a scalable solution for the capacity planning in the survivable optical networks based on the proposed I-GSP protection architecture.

Any unexpected disruption to WDM (Wavelength Division Multiplexing) based optical networks which carry data traffic at tera-bit per second may result in a huge loss to its end-users and the carrier itself. Thus survivability has been well-recognized as one of the most important objectives in the design of optical Internet. This thesis proposes a novel survivable routing architecture for the optical Internet. We focus on a number of key issues that are essential to achieve the desired service scenarios, including the tasks of (a) minimizing the total number of wavelengths used for establishing working and protection paths in WDM networks; (b) minimizing the number of affected working paths in case of a link failure; (c) handling large scale WDM mesh networks; and (d) supporting both Quality of Service (QoS) and best-effort based working lightpaths. To implement the above objectives, a novel path based shared protection framework namely Group Shared protection (GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy, and optimization is performed on these PGs. To the best of our knowledge this is the first work done in the area of group based WDM survivable routing approaches where not only the resource sharing is conducted among the PGs to achieve the best possible capacity efficiency, but also an integrated survivable routing framework is provided by incorporating the above objectives. Simulation results show the effectiveness of the proposed schemes.

The pervasiveness and operational autonomy of mesh-based wireless sensor networks (WSNs) make them an ideal candidate in offering sustained monitoring functions at reasonable cost over a wide area. To extend the functional lifetime of battery-operated sensor nodes, stringent sleep scheduling strategies with communication duty cycles running at sub-1% range are expected to be adopted. Although ultra-low communication duty cycles can cast a detrimental impact on sensing coverage and network connectivity, its effects can be mitigated with adaptive sleep scheduling, node deployment redundancy and multipath routing within the mesh WSN topology. This work proposes a cross-layer organizational approach based on sleep scheduling, called Sense-Sleep Trees (SS-Trees), that aims to harmonize the various engineering issues and provides a method to extend monitoring capabilities and operational lifetime of mesh-based WSNs engaged in wide-area surveillance applications. Various practical considerations such as sensing coverage requirements, duty cycling, transmission range assignment, data messaging, and protocol signalling are incorporated to demonstrate and evaluate the feasibility of the proposed design approach.

This thesis focuses on the survivable routing problem in WDM mesh networks where the objective is to minimize the total number of wavelengths used for establishing working and protection paths in the WDM networks. The past studies for survivable routing suffers from the scalability problem when the number of nodes/links or connection requests grow in the network. In this thesis, a novel path based shared protection framework namely Inter-Group Shared protection (I-GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy. Optimization is performed on these PGs such that sharing of protection wavelengths is considered not only inside a PG, but between the PGs. Simulation results show that I-GSP based integer linear programming model, namely, ILP-II solves the networks in a reasonable amount of time for which a regular integer linear programming formulation, namely, ILP-I becomes computationally intractable. For most of the cases the gap between the optimal solution and the ILP-II ranges between (2-16)%. The proposed ILP-II model yields a scalable solution for the capacity planning in the survivable optical networks based on the proposed I-GSP protection architecture.

As more owners seek to develop sustainable buildings, the construction industry is adapting to new requirements in order to meet owner's concerns. Material selection has been identified as an area where designers and contractors can have a significant impact on the sustainable performance of a building. Objective factors such as design considerations and cost constraints can play a role in the selection of materials. However, there may be subjective factors that could also impact the selection of materials. Building upon the potential impact of sustainability perceptions in an optimization model that can be used to help decision makers to select materials, this study defines and tests an instrument to identify and measure such perceptions. The purpose of this dissertation is to develop a conceptual instrument that measures the user-based assessment of product sustainability and validates decision-maker's perceptions in order to evaluate the contribution of subjective characteristics in materials selection. A survey of design and construction students and practitioners is carried out to capture the subjective factors included in the instrument. A Factor Analysis approach is used to refine and validate the measurement instrument and predict decision-makers' sustainability appraisal due to the factors considered.