EURO 2025 Leeds
Abstract Submission

1539. Incorporating Uncertainty in the Maritime Inventory Routing Problem: A Sample Average Approximation Approach for Antarctic Logistics

Invited abstract in session WC-17: Combinatorial Optimization and Data Processing, stream Combinatorial Optimization.

Wednesday, 12:30-14:00
Room: Esther Simpson 2.08

Authors (first author is the speaker)

1. Dagoberto Cifuentes-Lobos
Ingeniería Industrial, Universidad Católica de la Santísima Concepción
2. Lorena Pradenas
Ingeniería Industrial, Universidad de Concepción
3. Víctor Parada
Universidad de Santiago de Chile

Abstract

Maritime logistics in Antarctica are critical for sustaining scientific research, yet they face significant challenges due to uncertainties in weather conditions, supply availability, and demand fluctuations. These uncertainties complicate inventory and routing decisions, making the Maritime Inventory Routing Problem (MIRP) particularly complex. Existing optimization models either adopt deterministic formulations—ignoring uncertainty—or focus on short time horizons, limiting their applicability to real-world Antarctic logistics.
To address this gap, we propose a stochastic optimization formulation for MIRP using the Sample Average Approximation (SAA) method, extending a previously developed deterministic Mixed-Integer Linear Programming (MILP) model. We apply the SAA-based model to instances constructed from real-world data of Chilean Antarctic scientific bases and compare the solutions against those obtained from the deterministic counterpart, which has previously delivered optimal results for small-scale instances. Our analysis evaluates computational performance, scalability, and the impact of incorporating uncertainty on solution quality and robustness. The results contribute valuable insights into combinatorial optimization techniques for inventory routing under uncertainty, with potential applications extending beyond Antarctic logistics.

Keywords

Status: accepted

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