A Branch & Cut Algorithm to Compute Nondominated Solutions in MOLFP via Reference Points

Basedon some previous work ona Branch & Bound algorithm to compute nondominated solutions in multiobjective linear fractional programming (MOLFP) problems using reference points, we now present a new algorithm where special cuts are introduced. These cuts stem from some conditions that identify parts of the feasible region where the nondominated solution that optimizes the current achievement scalarizing function (ASF) cannot be obtained. Introducing the cuts substantially improves the performance of the previous algorithm. The results of several tests carried out to evaluate the performance of the new Branch & Cut algorithm against the old Branch & Bound are reported.