Stepwise contouring of efficient criterion vectors for large-scale MOLP

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.