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

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