Partial Gr\"obner bases for multiobjective integer linear optimization

TL;DR

This paper introduces partial Gr"obner bases, a novel algebraic structure for solving multiobjective integer linear programs, enabling efficient computation of all efficient solutions through new algorithms.

Contribution

The paper extends Gr"obner bases to multiobjective cases with partial orderings, providing a new methodology and algorithms for solving MOILPs.

Findings

Algorithms successfully compute all efficient solutions.

Computational experiments demonstrate effectiveness on various problem families.

Partial Gr"obner bases serve as test families for multiobjective programs.

Abstract

In this paper we present a new methodology for solving multiobjective integer linear programs using tools from algebraic geometry. We introduce the concept of partial Gr\"obner basis for a family of multiobjective programs where the right-hand side varies. This new structure extends the notion of Gr\"obner basis for the single objective case, to the case of multiple objectives, i.e., a partial ordering instead of a total ordering over the feasible vectors. The main property of these bases is that the partial reduction of the integer elements in the kernel of the constraint matrix by the different blocks of the basis is zero. It allows us to prove that this new construction is a test family for a family of multiobjective programs. An algorithm '\`a la Buchberger' is developed to compute partial Gr\"obner bases and two different approaches are derived, using this methodology, for computing the entire set of efficient solutions of any multiobjective integer linear problem (MOILP). Some examples illustrate the application of the algorithms and computational experiments are reported on several families of problems.