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This study proposes three new versions of the well-known linear programming technique for multidimensional preference analysis (LINMAP). LINMAP addresses the multi-criteria decision problem by analyzing individual differences in preferences in relation to a set of prespecified incentives in multidimensional attribute space. The proposed models satisfy the decision-maker’s specific needs, such as determining a fixed number of incentives to be active or assigning a minimum/maximum weight for the active incentives. The performance of the developed models is assessed using information from a case study in which a decision-maker desires to determine an optimal portfolio of incentives based on the preferences of individuals surveyed. Experimental results confirm that the proposed models could obtain solutions according to the decision-maker’s needs, yielding a better selection of incentives to activate and their corresponding distribution of the weights than those of the original LINMAP model. Moreover, the consistency of the proposed models is evaluated by performing a sensitivity analysis over database variations of the case study and comparing the outcomes with the results provided in the original case study. Overall, this work is promising when creating a design portfolio, considering individuals’ different preferences.

An improved LINMAP for multicriteria decision: designing customized incentive portfolios in an organization