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No / Reconfigurable manufacturing systems (RMSs) are designed based on the current and future requirements of the market and the manufacturing system (MS). The first stage of designing an RMS at the tactical level is the evaluation of economic and manufacturing/operational feasibility. Because of risk and uncertainty in an RMS environment, this major task must be performed precisely before investment in the detailed design. The present paper highlights the importance of manufacturing capacity and functionality for the feasibility of an RMS design during reconfiguration processes. Due to uncertain demands of product families, the RMS key-design factors, i.e. capacity value, functionality degree and reconfiguration time, are characterized by the identified fuzzy sets. Consequently, an integrated structure of the analytical hierarchical process and fuzzy set theory is presented. The proposed model provides additional insights into a feasibility study of an RMS design by considering both technical and economical aspects. The fuzzy analytical hierarchical process model is examined in an industrial case study by means of Expert Choice software. Finally, the fuzzy multicriteria model is sensitively analysed within the fuzzy domains of those attributes, which are considered to be critical for the case study.

The capacity value (CV) of a power generation unit indicates the extent to which it contributes to the generation system adequacy of a region’s bulk power system. Given the capricious nature of the wind resource, determining wind generation’s CV is nontrivial, but can be understood simply as how well its power output temporally correlates with a region’s electricity load during times of system need. Both wind generation and load are governed by weather phenomena that exhibit variability across all timescales, including low frequency weather cycles that span decades. Thus, a data-driven determination of wind’s CV should involve the use of long-term (i.e., multiple decades) coincident load and wind data. In addition to the challenge of finding high-quality, long-term wind data, existing load data more than several years old is of limited utility due to shifting end usage patterns that alter a region’s electricity load profile. Due to a lack of long-term data, current industry practice does not adequately account for the effects of weather variability in CV calculations. To that end, the objective of this thesis is to develop a model to “hindcast” what the historic regional load in New England would have been if governed by the conjoined influence of historic weather and a more current load profile. Modeling focuses exclusively on summer weekdays since this period is typically the most influential on CV. The summer weekday model is developed using multiple linear regression (MLR), and features a separate hour-based model for eight sub-regions within New England. A total of eighty-four candidate weather predictors are made available to the model, including lagged temperature, humidity, and solar insolation variables. A reanalysis weather dataset produced by the National Aeronautics and Space Administration (NASA) – the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset – is used since it offers data homogeneity throughout New England over multiple decades, and includes atmospheric fields that may be used for long-term wind resource characterization. Weather regressors are selected using both stepwise regression and a genetic algorithm(GA) based method, and the resulting models and their performance are compared. To avoid a tendency for overfitting, the GA-based method employs triple cross-validation as a fitness function. Results indicate a regional mean absolute percent error (MAPE) of less than 3% over all hours of the summer weekday period, suggesting that the modeling approach developed as part of this research has merit and that further development of the hindcasting model is warranted.

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