This research work focuses on the optimal algorithms of decision making and forecasting respectively, in order to achieve a better prediction. Decision making techniques and forecasting methods are investigated due to the poor accuracy of forecasting in comparison with real world data. The uncertainty of real world data leads to the use of type-1 fuzzy sets, type-2 fuzzy sets, fuzzy decision tree and fuzzy time-series for fuzzy data-mining - to which they are applied for the look-ahead based interval-valued fuzzy decision tree with optimal perimeter of the neighbourhood (LAIVFDT-OPN) model, and high-order type-2 fuzzy time series (HO-T2FTS) model. In the experiment with a real world business, a ‘computerised maintenance integration management system’ (CMIMS) is constructed as a simulation model for a case study. The CMIMS model consists of the LAIVFDT-OPN and HO-T2FTS models. It is also applied to a set of real world data from a factory in Thailand. Due to the significant uncertainty involved in machine maintenance, most tasks in machine diagnosis are still carried out manually by technicians. In this research, a prototype of CMIMS employing fuzzy data mining to diagnose machine maintenance is constructed. Considering the special features of machine maintenance data, fuzzy decision trees and fuzzy time series are adopted in the proposal method. To represent the uncertain fuzzy memberships, interval-valued fuzzy decision trees are proposed and an optimal neighbourhood perimeter is defined for look-ahead fuzzy decision trees. Based on the existing first-order type-2 time-series and high-order type-1 fuzzy time series, an improved high-order type-2 fuzzy time series method is put forward. In this case study, the CMIMS model can be used to analyse and evaluate uncertain data. It also can be employed to facilitate decision making in machine equipment status, and forecast machine maintenance plan in the future in stead of technicians. Our results demonstrated that the proposal method is effective in fuzzy decision support for machine maintenance.

While much research has shown that being in a supportive relationship can buffer individuals from both the physical and psychological effects of stressful life events (House, Landis, & Umberson, 1988; Uchino, 2004), research concerning actual support exchanges, specifically receiving support, has been associated with negative effects (Gleason, Iida, Bolger, & Shrout, 2003; Gleason, Iida, Shrout, & Bolger, 2008). Understanding the differential effects of this process on mood and health is the focus of this study. The current paper adds to the existing literature by first, theoretically replicating previously established support transaction patterns and their effects on mood within committed couples and second, investigating the role that personality (i.e., perceived control) plays in moderating the effects of support on mood and health outcomes. In a daily diary study of 78 couples expecting their first child, I investigate the within and between-person associations between control, support, mood, and health. Couples were asked to independently complete three weeks of daily diaries at three different time points (i.e., during their third trimester, infancy, and toddlerhood). With the exception of women in their third trimester of pregnancy, we theoretically replicate previous support patterns and the effects on mood and find both state- and trait- level control to be important in this process such that the greater an individual's sense of control, the more he or she is buffered from negative influence of support transactions. Hypotheses concerning support and health are only partially supported in that receiving support and negative health symptoms are positively associated. / text

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This thesis describes the development and application of a decision support system for logistics strategy modelling. The decision support system that is developed enables the modelling of logistics systems at a strategic level for any country or area in the world. The model runs on IBM PC or compatible computers under DOS (disk operating system). The decision support system uses colour graphics to represent the different physical functions of a logistics system. The graphics of the system is machine independent. The model displays on the screen the map of the area or country which is being considered for logistic planning. The decision support system is hybrid in term of algorithm. It employs optimisation for allocation. The customers are allocated by building a network path from customer to the source points taking into consideration all the production and throughput constraints on factories, distribution depots and transshipment points. The system uses computer graphic visually interactive heuristics to find the best possible location for distribution depots and transshipment points. In a one depot system it gives the optimum solution but where more than one depot is involved, the optimum solution is not guaranteed. The developed model is a cost-driven model. It represents all the logistics system costs in their proper form. Its solution very much depends on the relationship between all the costs. The locations of depots and transshipment points depend on the relationship between inbound and outbound transportation costs. The model has been validated on real world problems, some of which are described here. The advantages of such a decision support system for the formulation of a problem are discussed. Also discussed is the contribution of such an approach at the validation and solution presentation stages.

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