We present an exact method for separating Chvátal-Gomory cuts from binary knapsack sets, consisting of two steps: i) enumerating a finite set of possible optimal multipliers for the knapsack constraint; ii) for each candidate, adjusting optimally the remaining multipliers. We prove that ii) can be formulated as a binary knapsack problem, leading to a pseudopolynomial-time … Read more
In this paper, we address the Capacitated Vehicle Routing Problem with Stochastic Demands (CVRPSD), in which routes are planned a priori and recourse actions are performed to ensure demand fulfillment. These recourse actions are defined through policies and may include replenishment trips or demand backlogging subject to penalties. We develop the first family of pseudo-compact … Read more
The paper presents a three-phase algorithm to compute the set of nondominated points for the binary version of the uncapacitated facility location problem with two objectives. The first phase constructs a paving in objective space which is a collection of boxes that covers all nondominated points. The paving procedure is a branch and bound algorithm … Read more
Given a simple graph G = (V, E) with edge cost c ∈ ℝ^|E|, a positive integer h, source s ∈ V and terminal t ∈ V, the hop-constrained cheapest path problem (HCCP) seeks to find an s–t path of length at most h hops with the cheapest cost. This paper proposes a cut-based mixed … Read more
We present a machine learning (ML) component designed to enhance the well-known branch-and-cut (B&C) framework for the symmetric traveling salesman problem (TSP) in which only the subtour elimination constraints (SECs) violated by previously found integer solutions are considered. The objective of the ML component is to identify which SECs, from a pool of candidates, will … Read more
Scenario-based optimization problems can be solved via Benders decomposition, which separates first-stage (master problem) decisions from second-stage (subproblem) recourse actions and iteratively refines the master problem with Benders cuts. In conventional Benders decomposition, all subproblems are solved at each iteration. For problems with many scenarios, solving only a selected subset can reduce computation. We quantify … Read more
Machine learning is increasingly used to improve decisions within branch-and-bound algorithms for mixed-integer programming. Many existing approaches rely on deep learning, which often requires very large training datasets and substantial computational resources for both training and deployment, typically with GPU parallelization. In this work, we take a different path by developing interpretable models that are … Read more
Two-Stage Vehicle Routing Problems with Stochastic Demands (VRPSDs) form a class of stochastic combinatorial optimization problems where routes are planned in advance, demands are revealed upon vehicle arrival, and recourse actions are triggered whenever capacity is exceeded. Following recent works, we consider VRPSDs where demands are given by an empirical probability distribution of scenarios. Existing … Read more
In this work, we revisit binary polynomial optimization (BPO) problems with limited treewidth of the associated graph. We provide alternate proofs of the exactness of a reformulated linear program (LP) with O(n.2^d) variables, n being the number of variables and d being the treewidth of the associated graph. The first proof relies on expressing any … Read more
We prove the NP-hardness, using Karp reductions, of some problems related to the correlation polytope and its corresponding cone, spanned by all of the n×n rank-one matrices over {0, 1}. The problems are: membership, rank of the decomposition, and a “relaxed rank” obtained from relaxing the zero-norm expression for the rank to an ℓ1 norm. … Read more