Live reoptimization of operating room schedules is required to cope with disruptions such as emergency arrivals and deviations in surgery durations under strict time limits. The resulting problem can be formulated as a large-scale Resource Constrained Project Scheduling Problem (RCPSP). While exact optimization methods are attractive in this context due to their ability to provide … Read more
We study cutting-plane generation for binary polynomial optimization (BPO), whose feasible region is the multilinear set of a hypergraph. Strong inequalities for this set—such as two-links, flowers, and odd $\beta$-cycles—are classically hand-derived for fixed support patterns. Instead, we propose a decision-diagram (DD) approach: for any chosen support, it separates a facet-defining cut in the local … Read more
Classical optimization methods determine a single optimal or near-optimal solution for a decision problem. In many applications, however, the decision maker is interested in evaluating a pool of high-quality solutions, to encode fairness-oriented criteria or to obtain a portfolio of alternatives to use in case of unexpected scenarios. In this paper, we consider the problem … Read more
The length-constrained cycle partition problem (LCCP) is a graph optimization problem in which a set of nodes must be partitioned into a minimum number of cycles. Every node is associated with a critical time and the length of every cycle must not exceed the critical time of any node in the cycle. We formulate LCCP … Read more
We study energy-efficient operation of underground train networks, where energy from regenerative braking is usable only if another train in the same electrically isolated subnetwork accelerates simultaneously. Timetabling models for this setting typically fix one velocity profile per leg and running time, which limits the matching of braking and accelerating phases. We drop this assumption … Read more
Imagine that you are in front of a cockpit with several on–off buttons. If you were to thoroughly test it, you would need to try a prohibitive number of configurations. But since most bugs in practice can be isolated to interactions among few components, having tests that cover every possible pairwise configuration is a good … Read more
Primal heuristics for finding high-quality feasible solutions are an important component in mixed-integer optimization (MIO) solvers. Recent advances in GPU-accelerated optimization algorithms show the potential of GPU acceleration for continuous optimization. In this paper, we introduce the Parallel Node Generation and Exploration Algorithm (PaNGEA), a GPU-friendly MIO primal heuristic. PaNGEA explores restricted subproblems by combining … Read more
Tracking targets in undersea warfare requires successful detection by an active search asset. Maximizing detection likelihood requires strategic placement and routing of the search assets in the search region over the planning horizon. We develop a two-stage stochastic integer programming model that maximizes the expected total reward for target detections under uncertainty in target motion … Read more
This research introduces the Stochastic Sequential Queens Elimination Problem, where on the \(n\)-queens board, each activated queen simultaneously attempts to eliminate all queens in her unblocked neighborhood, each independently succeeding with probability \(p\). The objective is to minimize the expected cumulative conflict count over the trajectory. This research proposes a Markov decision process for this … Read more
We study the convex hull obtained after deleting prescribed vertices from the binary cube. The analysis separates three regimes according to the number of deleted vertices. When this number is fixed, both the original-space facet count and the linear extension complexity remain linear in the ambient dimension, up to constants depending only on the number … Read more