Bi-level programming techniques are developed for decentralized decision problems with decision makers located in a two-level decision making system; the upper decision maker is termed the leader while the lower is the follower. Both the leader and the follower try to optimise their own objective functions and the corresponding decisions do not control but do affect those of the other level. This research aims at solving bi-level decision problems with five extensions, i.e. multiple leaders/followers/objectives, fuzzy coefficients and goals. By using particle swarm optimisation and/or cut set and/or goal programming and/or Nash equilibrium concept, related mathematical models and corresponding algorithms are developed to solve fuzzy linear bi-level decision problems, fuzzy linear multi-objective bi-level decision problems, fuzzy linear multi-follower multi-objective bi-level decision problems, fuzzy linear goal bi-level decision problems, multi-leader one-follower bi-level decision problems, one-leader multi-follower bi-level decision problems, and multileader multi-follower bi-level decision problems. A fuzzy bi-level decision support system is then developed which implements all the algorithms to support bi-level decision making with different features. Finally, by using these bi-level models and algorithms, we explore possible applications in the fields of railway train set organisation, railway wagon flow management, strategic bidding in the electricity market, and supply chains to solve real world bi-level decision problems. The results of experiments show that the models and algorithms are effective for solving real world bi-level decision problems.

Bi-level programming techniques are developed for decentralized decision problems with decision makers located in a two-level decision making system; the upper decision maker is termed the leader while the lower is the follower. Both the leader and the follower try to optimise their own objective functions and the corresponding decisions do not control but do affect those of the other level. This research aims at solving bi-level decision problems with five extensions, i.e. multiple leaders/followers/objectives, fuzzy coefficients and goals. By using particle swarm optimisation and/or cut set and/or goal programming and/or Nash equilibrium concept, related mathematical models and corresponding algorithms are developed to solve fuzzy linear bi-level decision problems, fuzzy linear multi-objective bi-level decision problems, fuzzy linear multi-follower multi-objective bi-level decision problems, fuzzy linear goal bi-level decision problems, multi-leader one-follower bi-level decision problems, one-leader multi-follower bi-level decision problems, and multileader multi-follower bi-level decision problems. A fuzzy bi-level decision support system is then developed which implements all the algorithms to support bi-level decision making with different features. Finally, by using these bi-level models and algorithms, we explore possible applications in the fields of railway train set organisation, railway wagon flow management, strategic bidding in the electricity market, and supply chains to solve real world bi-level decision problems. The results of experiments show that the models and algorithms are effective for solving real world bi-level decision problems.

Thesis (Ph. D.)--Victoria University (Melbourne, Vic.), 2008. / Includes bibliographical references.

Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Adjo Amekudzi; Committee Member: Catherine Ross; Committee Member: Josias Zietsman; Committee Member: Michael Meyer; Committee Member: Randall Guensler.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 146-150). Also available via the Internet.

Thesis (doctoral)--University of Jyväskylä, 1991. / Summary in Finnish. Thesis t.p. inserted. Includes bibliographical references (p. 174-189).

Thesis (PhD.) - University of Queensland, 2003. / Includes bibliography.

Dissertation (PhD)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

In several types of embedded systems the applications are deployed both as software and as hardware components. For such systems, the partitioning decision is highly important since the implementation in software or hardware heavily influences the system properties. In the industry, it is rather common practice to take deployment decisions in an early stage of the design phase and based on a limited number of aspects. Often such decisions are taken based on hardware and software designers‟ expertise and do not account the requirements of the entire system and the project and business development constraints. This approach leads to several disadvantages such as redesign, interruption, etc. In this scenario, we see the need of approaching the partitioning process from a multiple decision perspective. As a consequence, we start by presenting an analysis of the most important and popular Multiple Criteria Decision Analysis (MCDA) methods and tools. We also identify the key requirements on the partitioning process. Subsequently, we evaluate all of the MCDA methods and tools with respect to the key partitioning requirements. By using the key partitioning requirements the methods and tools that the best suits the partitioning are selected. Finally, we propose two MCDA-based partitioning processes and validate their feasibility thorough an industrial case study.

Discrete choice experiments (DCEs) are widely used across economic disciplines to value multi-attribute commodities. DCEs ask survey-respondents to choose between mutually exclusive hypothetical alternatives that are described by a set of common attributes. The analysis of DCE data assumes that respondents consider and trade all attributes before making these choices. However, several studies show that many respondents ignore attributes. Respondents might choose not to consider all attributes to simplify choices or as a preference, because some attributes are not important to them. However, empirical approaches that account for attribute non-consideration only assume simplifying choice behaviour. This thesis shows that this assumption may lead to misleading welfare conclusions and therefore suboptimal policy advice. The analysis explores 'why' attribute are ignored using statistical analysis or by asking respondents. Both approaches are commonly used to identify attribute non-consideration in DCEs. However, the results of this thesis suggest that respondents struggle to recall ignored attributes and their reasons for non-consideration unless attributes are ignored due to non-valuation. This questions the validity of approaches in the literature that rely on respondents' ability to reflect on their decision rule. Further analysis explores how the complexity of choices affects the probability that respondents do not consider all attributes. The results show that attribute consideration first increases and then decreases with complexity. This raises questions about the optimal design complexity of DCEs. The overall findings of the thesis challenge the applicability of current approaches that account for attribute non-consideration in DCEs to policy analysis and emphasis the need for further research in this area.