Anna Konovalenko, PhD

Anna Konovalenko, PhD

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Predicting Last-Mile Delivery Route Deviations Using Machine Learning

Published in Expert Systems with Applications, 2025

This paper presents a machine learning approach to predict deviations in last-mile delivery routes by analyzing 20,000+ historical routes and evaluating neural architectures (LSTM, CNN, Transformers). The approach achieves up to 19% improvement in prediction accuracy over baseline methods.

Recommended citation: Konovalenko, A., Hvattum, L.M., & Iversen, K.A.H. (2025). "Predicting Last-Mile Delivery Route Deviations Using Machine Learning." Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2025.129921
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Optimizing Tabu Search Parameters with Machine Learning

Published in Logistics, 2024

Develops adaptive parameter tuning methods for tabu search algorithms using machine learning. The ML-based tuning system dynamically adjusts parameters during execution, achieving 5% improvement over static configurations validated through statistical significance testing.

Recommended citation: Konovalenko, A., & Hvattum, L.M. (2024). "Optimizing Tabu Search Parameters with Machine Learning." Logistics.
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Using Machine Learning to Identify Hidden Constraints in Vehicle Routing Problems

Published in Computers and Operations Research, 2024

This paper explores machine learning methods for identifying hidden constraints in vehicle routing problems. Develops an LSTM-based classification system to detect constraint violations (time windows, capacity, precedence) in VRP solutions and pinpoint violation locations.

Recommended citation: Konovalenko, A., Hvattum, L.M., & Msakni, M.K. (2024). "Using Machine Learning to Identify Hidden Constraints in Vehicle Routing Problems." Computers and Operations Research. https://doi.org/10.1016/j.cor.2025.107029
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Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State Space Components

Published in Logistics, 2023

Develops a reinforcement learning algorithm for real-time vehicle routing. Conducts systematic ablation studies on state space components, achieving 15% efficiency improvement over greedy heuristics with better performance under disruptions.

Recommended citation: Konovalenko, A., & Hvattum, L.M. (2023). "Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State Space Components." Logistics.
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Conference Papers

Comparative Analysis of MaxSAT Solvers for Discrete Optimization

Published in Conference on Satisfiability Testing, 2023

Benchmark study demonstrating that specialized MaxSAT solvers achieve 22% faster solving times compared to cross-domain optimization approaches on discrete optimization problems.

Recommended citation: Konovalenko, A. (2023). "Comparative Analysis of MaxSAT Solvers for Discrete Optimization." Conference on Satisfiability Testing.
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