Decomposition-Based Constructive Heuristics for the Linear Ordering Problem
The linear ordering problem (LOP) is a classical NP-hard combinatorial optimization problem. In this paper, we study constructive heuristics for the LOP. We propose a unified recursive framework in which the index set is heuristically partitioned into smaller subsets, sufficiently small subproblems are solved exactly, and the resulting permutations are concatenated. Within this framework, we investigate three different recursive partitioning principles: a rule based on strongly connected components (the level graph method), a cut-based rule (the minimum cut method), and a score-based rule (the recursive Borda method). We first show that the recursive Borda method satisfies the Condorcet criterion, whereas the Borda rule does not. The level graph method and the minimum cut method satisfy a stronger Condorcet-type property, which the recursive Borda method does not satisfy. Computational experiments on xLOLIB instances show that the recursive Borda method provides the best average solution quality among the tested constructive heuristics. They also show that, after applying insertion-based local search, the differences in final solution quality among these heuristics become much smaller.