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This paper proposes a new hybrid multiattribute decision making (MADM) model which deals with the interactions that usually exist between hostel attributes in the process of measuring the students' satisfaction towards a set of hostels and identifying the optimal strategies for enhancing their satisfaction. The model uses systematic random stratified sampling approach for data collection purpose as students dwelling in hostels are "naturally" clustered by block and gender, factor analysis for extracting large set of hostel attributes into fewer independent factors, -measure for characterizing the interactions shared by the attributes within each factor, Choquet integral for aggregating the interactive performance scores within each factor, Mikhailov's fuzzy analytical hierarchy process (MFAHP) for determining the weights of independent factors, and simple weighted average (SWA) operator to measure the overall satisfaction score of each hostel. A real evaluation involving fourteen Universiti Utara Malaysia (UUM) hostels was carried out in order to demonstrate the model's feasibility. The same evaluation was performed using an additive aggregation model in order to illustrate the effects of ignoring the interactions shared by attributes in hostel satisfaction analysis.

Determining 2 values of fuzzy measure prior to applying Choquet integral normally turns into a complex undertaking, especially when the decision problem entails a large number of evaluation attributes,. Many patterns of fuzzy measure have thus been suggested to deal with this complexity.-measure is one such pattern. However, the original-measure identification method was found to be unsuccessful in providing clear-cut indications on the relationships held by the attributes. A revised version of the method was then introduced to tackle this issue, but unfortunately it requires a large amount of initial data from the respondents compared to the original method. This paper therefore proposes an alternate version of-measure identification method that synchronously compensates the shortcomings associated with each existing method. The proposed method uses interpretive structural modelling (ISM) to uncover the actual relationships held by the attributes. The outputs of ISM (i.e. digraph, driving power and dependence power) are then utilised to determine the inputs required to identify the complete set of-measure values. A supplier selection problem was used to demonstrate the feasibility of the method. Also, the usability of the method was compared over the existing ones. .

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Abstract. Selection of good supplier is important to determine the performance and profitability of SMEs food processing industry. The lack of managerial capability on supplier selection in SMEs food processing industry affects the competitiveness of SMEs food processing industry. This research aims to determine the ideal criteria of supplier for food processing industry using Analytical Hierarchy Process (AHP). The research was carried out in a quantitative method by distributing questionnaires to 50 SMEs food processing industries. The collected data analysed using Expert Choice software to rank the supplier selection criteria. The result shows that criteria for supplier selection are ranked by cost, quality, service, delivery and management and organisation while purchase cost, audit result, defect analysis, transportation cost and fast responsiveness are the first five sub-criteria. The result of this research intends to improve managerial capabilities of SMEs food processing industry in supplier selection.