We focus on bi-objective optimization problems whose feasible solutions can be described as 0/1 integer linear programs and propose two ILP heuristics, boundary induced neighborhood search (BINS) and directional local branching. Their main idea is to combine the features and explore the neighborhoods of solutions that are relatively close in the objective space. A two-phase ILP-based heuristic framework relying on BINS and directional local branching is introduced. Moreover, a new exact method called adaptive search in objective space (ASOS) is also proposed. ASOS combines features of the e-constraint method with the binary search in the objective space and uses heuristic solutions produced by BINS for guidance. Our new methods are computationally evaluated on two problems of particular relevance for the design of FTTx-networks. Comparison with other known exact methods (relying on the exploration of the objective space) is conducted on a set of realistic benchmark instances representing telecommunication access networks from Germany. Link to the article
LEITNER, M., LJUBIC, I., SINNL, M. and WERNER, A. (2016). ILP Heuristics and a New Exact Method for Bi-Objective 0/1 ILPs: Application to FTTx-Network Design. Computers & Operations Research, 72, pp. 128-146.