arXiv subjects

Marco Caserta

Publications and source records attributed to Marco Caserta.

4 recordsLinked to original sources

arXiv2026

Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design

We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory optimization model. The design partitions hundreds of sites into feasible clusters and selects a central replenishment site for each cluster to reduce costs while maintaining service levels. Because the optimizer favors candidates with high predicted savings, it can exploit optimistic surrogate errors. We develop a conservative framework combining a graph neural network ensemble, variable neighborhood search, and set-partitioning recombination. The surrogate is trained on exact cluster evaluations, while a lower quantile of ensemble-predicted savings guides the search to limit optimism. Clusters found during the search are recombined through set partitioning using surrogate-based objective coefficients. The resulting network is evaluated with the exact inventory model, and only this evaluation is used to report performance. In a case study of 246 fulfillment centers in Amazon's North American network, the framework improves combined savings by 30.5% over an optimization baseline based entirely on exact cluster evaluations, while maintaining approximately 99.8% service across six independent replications. Under equal computational budgets, graph-surrogate-guided search achieves higher mean exact savings than a tabular alternative under both scoring schemes. Conservative scoring improves mean savings for both surrogate classes and reduces the share of final-network clusters overestimated by the graph surrogate from 68% to 28%. Predictive and ranking accuracy deteriorate among search-generated candidates with high surrogate scores, indicating that random holdout performance can incompletely characterize surrogate quality during optimization.

math.OC↗

arXiv2026

A Robust Chance Constrained Approach to Surgery Scheduling

We study elective surgery scheduling under uncertain procedure durations. Schedules based on mean durations or fixed buffering rules may appear efficient ex ante but become fragile in execution, as early overruns propagate through the day and expose later surgeries to accumulated delay. We propose a robust chance-constrained framework that separates uncertainty quantification from schedule optimization. A buffer engine converts distributional information into reliability-dependent buffered durations, while the scheduling model jointly selects assignments, sequences, start times, and surgery-level reliability levels from a discrete menu. Reliability therefore becomes an endogenous scheduling decision rather than a fixed service-level parameter. The framework accommodates average-reliability, worst-day, and hard-target risk postures. Comparisons with common-reliability and uniform proportional-buffer benchmarks show that the menu derives its value from exploiting surgery-level heterogeneity, allocating protection where it has the greatest operational value while avoiding unnecessary conservatism. In a rolling-origin case study at HLA Moncloa Hospital in Madrid, covering 10 instances with 45 to 227 surgeries, the approach reduces delays exceeding 90 minutes by 97%, lowers the 95th-percentile delay by approximately 620 minutes, and reduces total overtime by 42% relative to a deterministic mean-based baseline. By allocating buffers according to uncertainty and operational exposure, the framework translates heterogeneous duration data and risk preferences into schedules that are more reliable in execution and less conservatively buffered than one-size-fits-all rules.

math.OC↗

arXiv2026

Output-Constrained Decision Trees

Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This paper introduces new methods for training Output-Constrained Regression Trees (OCRT), addressing the limitations of traditional decision trees in constrained multi-target regression tasks. We propose three approaches: M-OCRT, which uses split-based mixed integer programming to enforce constraints; E-OCRT, which employs an exhaustive search for optimal splits and solves constrained prediction problems at each decision node; and EP-OCRT, which applies post-hoc constrained optimization to tree predictions. To illustrate their potential uses in ensemble learning, we also introduce a random forest framework working under convex feasible sets. We validate the proposed methods through a computational study both on synthetic and industry-driven hierarchical time series datasets. Our results demonstrate that imposing constraints on decision tree training results in accurate and feasible predictions.

cs.LG↗

arXiv2024

Large Scale Constrained Clustering With Reinforcement Learning

Given a network, allocating resources at clusters level, rather than at each node, enhances efficiency in resource allocation and usage. In this paper, we study the problem of finding fully connected disjoint clusters to minimize the intra-cluster distances and maximize the number of nodes assigned to the clusters, while also ensuring that no two nodes within a cluster exceed a threshold distance. While the problem can easily be formulated using a binary linear model, traditional combinatorial optimization solvers struggle when dealing with large-scale instances. We propose an approach to solve this constrained clustering problem via reinforcement learning. Our method involves training an agent to generate both feasible and (near) optimal solutions. The agent learns problem-specific heuristics, tailored to the instances encountered in this task. In the results section, we show that our algorithm finds near optimal solutions, even for large scale instances.

cs.LG↗