# Adaptive Operator Selection in Bilevel Large Neighborhood Search for Electric Autonomous Dial-a-Ride Problem under Uncertainty

arXiv: https://arxiv.org/abs/2610.04219 · HTML: https://mlsanity.com/paper/2610.04219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 3 Oct 2026.

## Builds on (most cited first, 11 of 11)

- [ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution](https://mlsanity.com/paper/2402.01145/) (2024)
- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms](https://mlsanity.com/paper/2006.11444/) (2020)
- [Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables](https://mlsanity.com/paper/2109.05799/) (2021)
- [3-Objective Pareto Optimization for Problems with Chance Constraints](https://mlsanity.com/paper/2304.08774/) (2023)
- [Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints](https://mlsanity.com/paper/2102.05303/) (2021)
- [Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem](https://mlsanity.com/paper/2404.06014/) (2024)
- [Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting](https://mlsanity.com/paper/2305.17957/) (2023)
- [On the Use of Bi-Objective Evolutionary Algorithms for the Stochastic MKP under Dynamic Constraints](https://mlsanity.com/paper/2604.10930/) (2026)
- [A Deterministic Annealing Local Search for the Electric Autonomous Dial-A-Ride Problem](https://mlsanity.com/paper/2212.04167/) (2022)
- [On the use of evolutionary optimization for the dynamic chance constrained open-pit mine scheduling problem](https://mlsanity.com/paper/2604.13385/) (2026)

## Abstract (arXiv)

The electric autonomous dial-a-ride problem (EADARP) extends the classical dial-a-ride problem by incorporating battery and charging constraints for electric vehicles. In practice, travel-time uncertainty can cause violations of time-window constraints. Large neighborhood search is effective for solving the EADARP, but its performance can depend on the choice of insertion operator during the repair phase. This paper investigates insertion-operator selection within a bilevel large neighborhood search framework for deterministic and chance-constrained variants of the EADARP. In the chance-constrained variant, arc travel times are modeled as independent normally distributed random variables, and upper time-window constraints are enforced probabilistically. We consider six selection methods, namely fixed greedy insertion, fixed regret-based insertion, random selection, a deterministic state-based rule, performance-adaptive ALNS selection, and LLM-based state-aware selection. Experimental results show comparable performance on smaller instances, while differences become more evident on larger and more constrained instances. There is no single strategy that performs best across all instances, and the relative performance of the LLM-based, rule-based, and ALNS strategies varies with the problem instance and experimental setting.