| Abstract: | In this paper a new method of presenting the overall (focused) structure of the efficient criterion vectors (N) for large-scale MOLP is proposed. The proposed algorithm ASEOV (approximation of the set of efficient objective vectors) determines the representative subset of N and ensures full coverage of N , with corresponding coverage precision indicated. ASEOV works in objective space directly and eliminates unnecessary computational effort at the collapsing extreme points of X , which are transformed to non-extreme points in objective space. The Tchebycheff metric is employed to measure the coverage precision. ASEOV allows a decision maker (DM) to control the determination procedure by assessing the coverage allowance for each criterion. When the DM's preference is available, ASEOV can focus contouring on the subset of N which fits with the extracted preference. Combined with proper interactive methods, this focused contour over N can reduce the DM's burden, inconsistency or cognitive bias in assessing his preference from which to derive the final best-compromise solution. An illustrative example is presented. |
