In today's fast pace society, analysts are expected to make quick educated decisions out of very complex situations. This is not an easy task to achieve but, if equipped with the proper decision tools, it can be effectively accomplished. Cognitive Maps have been commonly used in the past for this purpose but they lack a certain precision. Recently the use of Fuzzy Cognitive Maps (FCMs) has been proposed as an alternative for representing and analyzing complex systems that create outcomes that are not always easily definable. This thesis will introduce its readers to this versatile decision analysis tool and its many uses in a diversity of fields. More precisely, it will help analyze and determine how Internet transforms relationships between car parts suppliers and automobile manufacturers. With the help of the Car Internet Research Program (CIRP), 17 semi-structured interviews of executive heads belonging to upstream suppliers in the automotive sector were considered as means of this research. Summarized and analysed through the help of FCMs, these exchanges give an approximate picture of the state of current events in the automotive industry, more precisely in regards to supplier and manufacturer connectivity issues.

In today's competitive business world, it is important for decision makers to have access to decision support tools. One of these decision making areas is supplier selection in e-procurement. Supplier selection is a multi-criteria decision making process that deals with the optimization of conflicting objectives. If it is not supported by a system, this would be a complex and time consuming process. In spite of the fact that the term "supplier selection" is commonly used in the literature, few efforts have been dedicated to develop a system based on any of these methods. In this thesis, we propose a web based supplier selection decision support system based on the fuzzy analytic hierarchy process (AHP) method. To validate our choice of the Fuzzy AHP model and also to validate the conceptual design of our supplier selection decision support system, we conducted a case study in a local hospital.

Product realization is a very complex, interdisciplinary process. At early design stages, decisions must be made not only about physical attributes of the design, but also about scheduling and resource allocation for many product and manufacturing engineering activities, as well as purchasing, finance, marketing, etc. Typically, complex interdependencies exist among these disparate activities, and it is difficult to predict how decisions will affect overall organizational objectives of low cost, high quality and short time-to-market. Many decision support needs in this process seem to fall in a gap between emerging design-for-manufacture models (which evaluate design attributes for cost of a specific manufacturing activity) and management-level models (such as very abstract but comprehensive PERT-type networks). This research addresses this "gap." In the proposed model there are three distinct object representations that together define a product realization problem: product attributes, activities, and resources. In the first stage of the model, two relational matrices are used to (i) match product attributes to the required design and manufacturing activities, and (ii) then match the activities to the resources required for realization. In the second stage, an activity network is generated from the data in the relational matrices. The network is assembled from predefined "templates" of activities which have default precedence relationships (for example, sequences of prototyping and tooling activities). This activity network is then used to simulate aggregate cash flow. There are several applications envisioned for a computer tool based on this model: as a "prospectus" for new product designs to assess aggregate cost and development time within a specific organizational context; to assist managers in "concurrent" scheduling of design, tooling, and other preproduction activities; as a vehicle for budget negotiation between engineers and financial managers during the design process; and as an aid for value analysis. After reporting results of a field study and prototype computer implementation, I conclude that the model could potentially be used for decision support, but several important conceptual and implementation limitations remain to be addressed.

Complexity of manufacturing processes has hindered methodical specification of machine settings for improving productivity. For processes where analytical models are available (e.g., turning and grinding), modeling uncertainty caused by diversity of process conditions and time-variability has precluded the application of traditional optimization methods to minimize cost or production time. In these cases, the machine settings are selected conservatively to ensure part quality satisfaction at the expense of longer production times. In other cases, where it is prohibitively difficult to represent the process by an analytical model (e.g., injection molding), the machine settings are assigned either by trial and error, based on heuristic knowledge of an experienced operator, or statistical Design of Experiments methods which require a comprehensive empirical model between the inputs and part quality attributes. The purpose of this thesis is to present Recursive Constraint Bounding (RCB) as a general methodology for machine setting selection in manufacturing processes. In RCB, measurements of part quality attributes (e.g., size and surface integrity) are used as feedback to assess optimality/integrity of the process, and the machine settings are adjusted by formulating and solving a customized optimization problem with the objective of improving part quality or reducing production time. RCB is applied to cylindrical plunge grinding, where an approximate model is available, and injection molding, where adequate process models are unavailable. For cylindrical plunge grinding, cycle-time is minimized while satisfying constraints. For injection molding, machine settings are selected so as to satisfy part quality constraints.

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Recent advances in the study of fire spread and behavior of building materials under fire have helped designers to set minimum standards for both structural and finishing materials for different types of building occupancies. However, when a fire breaks out in a building, the immediate hazard is to the occupants, yet there are no precisely defined ways of designing adequate means of escape. It is hypothesized that this apparent lack of research in this direction is due in part to the differences in the design of buildings as a result of unique site conditions or the building configuration itself. Coupled with this uniqueness of design is the tendency of humans to panic when an emergency arises leading to unpredictable actions. Given that deterministic models are not capable of handling such unpredictable behavior, designs based purely on the intuition of the designer can lead to very disastrous results in case of an emergency. Two methodologies for the design and evaluation of building facilities and regional emergency evacuation plans have been proposed. A building plan provided by the decision maker is translated into a mathematical format useful for analysis. The analysis is performed and the feasible alternatives given to the designer or decision maker. The methodologies were tested on several examples including evacuation of a medical facility which was used as a case study. Both methodologies for routing and resource allocation efficiently solved the problem, thus aiding the designer in identifying critical parameters. Finally the dissertation proposes future work for the Emergency Evacuation Problem. This work includes incorporating the models that were developed here in a decision support environment. This enhancement would improve the decision making process as it would enable the designer to interactively test various design strategies.

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