arXiv subjects

Anne Meyer

Publications and source records attributed to Anne Meyer.

17 recordsLinked to original sources

arXiv2026

Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization

Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing. While integrated models capture interactions between these decisions, practical warehouse systems often require decomposed approaches due to organizational boundaries, differing responsibilities, or limited data availability. Existing studies primarily evaluate algorithms for isolated subproblems or fixed subproblem combinations for specific warehouse settings, but lack a general mechanism to determine applicable algorithm configurations, compose them into valid solution pipelines, and assess their performance. With Context-Aware Synthesis of Optimization Pipelines (CASOP), we propose a framework for constructing and evaluating context-specific optimization pipelines and apply these to order fulfillment. The framework comprises: (1) a modular repository of algorithms for common order fulfillment problems; (2) semantic data and algorithm cards to describe warehouse context and algorithm requirements; (3) a taxonomy that structures order fulfillment problems into relevant subproblems; (4) a pipeline synthesizer that identifies applicable algorithms for a given warehouse context and composes all valid optimization pipelines; and (5) a pipeline evaluator that assesses all resulting pipelines. We demonstrate the framework on 7 benchmark instance sets covering four problem classes, resulting in 1,063,044 valid pipelines. The framework supports researchers and practitioners in designing, automatically synthesizing, and selecting valid, high-performing algorithmic pipelines for warehouse operations. The software is open-source and available at https://github.com/kit-dsm/ware_ops_pipes and https://github.com/kit-dsm/ware_ops_algos. Keywords: Warehouse optimization, Algorithm selection, Pipeline synthesis, Order fulfillment

cs.AI↗

arXiv2026

CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models

Many real-world optimization problems consist of multiple tightly coupled subproblems whose solutions must be coordinated to achieve high overall performance. However, existing large language model driven automated heuristic design approaches are limited to single-problem settings. In this paper, we propose CoupleEvo. CoupleEvo proposes three evolutionary coordination strategies to evolve heuristics for coupled optimization problems: the sequential strategy evolves heuristics for one subproblem after the other; the iterative strategy alternates the evolution of heuristics for different subproblems over successive generations; and the integrated strategy evolves heuristics for all problems simultaneously. The approach is evaluated on two representative coupled optimization problems. Experimental results show that decomposition-based strategies (sequential and iterative) provide more stable convergence and higher solution quality, while the integrated evolution strategy suffers from increased search complexity and variability. These findings highlight the importance of coordinating evolutionary search across interdependent subproblems and demonstrate the potential of LLM-driven heuristic design for complex coupled optimization problems. The code is available: https://github.com/tb-git-kit-research/CoupleEvo.

cs.NE↗

arXiv2026

The Multi-AMR Buffer Storage, Retrieval, and Reshuffling Problem: Exact and Heuristic Approaches

Buffer zones are essential in production systems to decouple sequential processes. In dense floor storage environments, such as space-constrained brownfield facilities, manual operation is increasingly challenged by severe labor shortages and rising operational costs. Automating these zones requires solving the Buffer Storage, Retrieval, and Reshuffling Problem (BSRRP). While previous work has addressed scenarios where the focus is limited to reshuffling and retrieving a fixed set of items, real-world manufacturing necessitates an adaptive approach that also incorporates arriving unit loads. This paper introduces the Multi-AMR BSRRP, coordinating a robot fleet to manage concurrent reshuffling, alongside time-windowed storage and retrieval tasks, within a shared floor area. We formulate a Binary Integer Programming (IP) model to obtain exact solutions for benchmarking purposes. As the problem is NP-hard, rendering exact methods computationally intractable for industrial scales, we propose a hierarchical heuristic. This approach decomposes the problem into an A* search for task-level sequence planning of unit load placements, and a Constraint Programming (CP) approach for multi-robot coordination and scheduling. Experiments demonstrate orders-of-magnitude computation time reductions compared to the exact formulation. These results confirm the heuristic's viability as responsive control logic for high-density production environments.

cs.RO↗

arXiv2026

Algorithmic Prompt-Augmentation for Efficient LLM-Based Heuristic Design for A* Search

Heuristic functions are essential to the performance of tree search algorithms such as A*, where their accuracy and efficiency directly impact search outcomes. Traditionally, such heuristics are handcrafted, requiring significant expertise. Recent advances in large language models (LLMs) and evolutionary frameworks have opened the door to automating heuristic design. In this paper, we extend the Evolution of Heuristics (EoH) framework to investigate the automated generation of guiding heuristics for A* search. We introduce a novel domain-agnostic prompt augmentation strategy that includes the A* code into the prompt to leverage in-context learning, named Algorithmic - Contextual EoH (A-CEoH). To evaluate the effectiveness of A-CeoH, we study two problem domains: the Unit-Load Pre-Marshalling Problem (UPMP), a niche problem from warehouse logistics, and the classical sliding puzzle problem (SPP). Our computational experiments show that A-CEoH can significantly improve the quality of the generated heuristics and even outperform expert-designed heuristics.

cs.AI↗

arXiv2026

From Mice to Trains: Amortized Bayesian Inference on Graph Data

Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data requires methods that are permutation-invariant, scalable across varying sizes and sparsities, and capable of capturing complex long-range dependencies, making posterior estimation on graph parameters particularly challenging. Amortized Bayesian Inference (ABI) is a simulation-based framework that employs generative neural networks to enable fast, likelihood-free posterior inference. We adapt ABI to graph data to address these challenges to perform inference on node-, edge-, and graph-level parameters. Our approach couples permutation-invariant graph encoders with flexible neural posterior estimators in a two-module pipeline: a summary network maps attributed graphs to fixed-length representations, and an inference network approximates the posterior over parameters. In this setting, several neural architectures can serve as the summary network. In this work we evaluate multiple architectures and assess their performance on controlled synthetic settings and two real-world domains - biology and logistics - in terms of recovery and calibration.

stat.ML↗

arXiv2025

Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design

We propose a novel reinforcement learning (RL) design to optimize the charging strategy for autonomous mobile robots in large-scale block stacking warehouses. RL design involves a wide array of choices that can mostly only be evaluated through lengthy experimentation. Our study focuses on how different reward and action space configurations, ranging from flexible setups to more guided, domain-informed design configurations, affect the agent performance. Using heuristic charging strategies as a baseline, we demonstrate the superiority of flexible, RL-based approaches in terms of service times. Furthermore, our findings highlight a trade-off: While more open-ended designs are able to discover well-performing strategies on their own, they may require longer convergence times and are less stable, whereas guided configurations lead to a more stable learning process but display a more limited generalization potential. Our contributions are threefold. First, we extend SLAPStack, an open-source, RL-compatible simulation-framework to accommodate charging strategies. Second, we introduce a novel RL design for tackling the charging strategy problem. Finally, we introduce several novel adaptive baseline heuristics and reproducibly evaluate the design using a Proximal Policy Optimization agent and varying different design configurations, with a focus on reward.

cs.AI↗

arXiv2025

Efficient Uncertainty Propagation in Bayesian Two-Step Procedures

Bayesian inference provides a principled framework for probabilistic reasoning. If inference is performed in two steps, uncertainty propagation plays a crucial role in accounting for all sources of uncertainty and variability. This becomes particularly important when both aleatoric uncertainty, caused by data variability, and epistemic uncertainty, arising from incomplete knowledge or missing data, are present. Examples include surrogate models and missing data problems. In surrogate modeling, the surrogate is used as a simplified approximation of a resource-heavy and costly simulation. The uncertainty from the surrogate-fitting process can be propagated using a two-step procedure. For modeling with missing data, methods like Multivariate Imputation by Chained Equations (MICE) generate multiple datasets to account for imputation uncertainty. These approaches, however, are computationally expensive, as multiple models must be fitted separately to surrogate parameters respectively imputed datasets. To address these challenges, we propose an efficient two-step approach that reduces computational overhead while maintaining accuracy. By selecting a representative subset of draws or imputations, we construct a mixture distribution to approximate the desired posteriors using Pareto smoothed importance sampling. For more complex scenarios, this is further refined with importance weighted moment matching and an iterative procedure that broadens the mixture distribution to better capture diverse posterior distributions.

stat.ME↗

arXiv2025

Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

Combinatorial optimization problems often rely on heuristic algorithms to generate efficient solutions. However, the manual design of heuristics is resource-intensive and constrained by the designer's expertise. Recent advances in artificial intelligence, particularly large language models (LLMs), have demonstrated the potential to automate heuristic generation through evolutionary frameworks. Recent works focus only on well-known combinatorial optimization problems like the traveling salesman problem and online bin packing problem when designing constructive heuristics. This study investigates whether LLMs can effectively generate heuristics for niche, not yet broadly researched optimization problems, using the unit-load pre-marshalling problem as an example case. We propose the Contextual Evolution of Heuristics (CEoH) framework, an extension of the Evolution of Heuristics (EoH) framework, which incorporates problem-specific descriptions to enhance in-context learning during heuristic generation. Through computational experiments, we evaluate CEoH and EoH and compare the results. Results indicate that CEoH enables smaller LLMs to generate high-quality heuristics more consistently and even outperform larger models. Larger models demonstrate robust performance with or without contextualized prompts. The generated heuristics exhibit scalability to diverse instance configurations.

cs.AI↗

arXiv2024

Sorting multibay block stacking storage systems

Autonomous mobile robots (AMRs) are increasingly used to automate operations in intralogistics. One crucial feature of AMRs is their availability, allowing them to operate 24/7. This work addresses the multibay unit load pre-marshalling problem, which extends pre-marshalling from a single bay to larger warehouse configurations with multiple bays. Pre-marshalling leverages off-peak time intervals to sort a block stacking warehouse in anticipation of future orders. These larger warehouse configurations require not only the minimization of the number of moves but also the consideration of distance or time when making sorting decisions. Our proposed solution for the multibay unit load pre-marshalling problem is based on our two-step approach that first determines the access direction for each stack and then finds a sequence of moves to sort the warehouse. In addition to adapting the existing approach that integrates a network flow model and an extended A* algorithm, we additionally present an exact constraint programming approach for the second stage of the problem-solving process. The results demonstrate that the presented solution approach effectively enhances the access time of unit loads and reduces the sorting effort for block stacking warehouses with multiple bays.

cs.DS↗

arXiv2023

Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems

Recent years have seen a rise in interest in terms of using machine learning, particularly reinforcement learning (RL), for production scheduling problems of varying degrees of complexity. The general approach is to break down the scheduling problem into a Markov Decision Process (MDP), whereupon a simulation implementing the MDP is used to train an RL agent. Since existing studies rely on (sometimes) complex simulations for which the code is unavailable, the experiments presented are hard, or, in the case of stochastic environments, impossible to reproduce accurately. Furthermore, there is a vast array of RL designs to choose from. To make RL methods widely applicable in production scheduling and work out their strength for the industry, the standardisation of model descriptions - both production setup and RL design - and validation scheme are a prerequisite. Our contribution is threefold: First, we standardize the description of production setups used in RL studies based on established nomenclature. Secondly, we classify RL design choices from existing publications. Lastly, we propose recommendations for a validation scheme focusing on reproducibility and sufficient benchmarking.

cs.LG↗

arXiv2022

Solving the unit-load pre-marshalling problem in block stacking storage systems with multiple access directions

Block stacking storage systems are highly adaptable warehouse systems with low investment costs. With multiple, deep lanes they can achieve high storage densities, but accessing some unit loads can be time-consuming. The unit-load pre-marshalling problem sorts the unit loads in a block stacking storage system in off-peak time periods to prepare for upcoming orders. The goal is to find a minimum number of unit-load moves needed to sequence a storage bay in ascending order based on the retrieval priority group of each unit load. In this paper, we present two solution approaches for determining the minimum number of unit-load moves. We show that for storage bays with one access direction, it is possible to adapt existing, optimal tree search procedures and lower bound heuristics from the container pre-marshalling problem. For multiple access directions, we develop a novel, two-step solution approach based on a network flow model and an A* algorithm with an adapted lower bound that is applicable in all scenarios. We further analyze the performance of the presented solutions in computational experiments for randomly generated problem instances and show that multiple access directions greatly reduce both the total access time of unit loads and the required sorting effort.

cs.DS↗

arXiv2022

Mission planning for emergency rapid mapping with drones

We introduce a mission planning concept for routing unmanned aerial vehicles (UAVs) through a set of sampling locations in the immediate aftermath of an incident such as a fire or chemical accident. Using interpolation methods that account for the spatial interdependencies inherent in the surveyed phenomenon, these samples allow predicting the distribution of hazardous substances across the affected area. We define the generalized correlated team orienteering problem (GCorTOP) for selecting {informative} samples considering spatial correlations between observed and unobserved locations as well as priorities in the surveyed area. To quickly provide high-quality solutions in time-sensitive situations, we propose a two-phase multi-start adaptive large neighborhood search (2MLS). We show the competitiveness of the solution approach using benchmark instances for the team orienteering problem and investigate the performance of the proposed models and solution approach in an extensive study based on newly introduced benchmark instances for the mission planning problem.

cs.RO↗

arXiv2021

Unsupervised Movement Detection in Indoor Positioning Systems of Production Halls

Consider indoor positioning systems (IPS) in production halls where objects equipped with sensors send their current position. Beside its large volume, the analyzation of the resulting raw data is challenging due to the susceptibility towards noise. Reasons are accuracy issues and undesired awakenings of sensors that occur due to the dynamics of logistic processes (e.g.~vibrations of passing forklifts). We propose a tailor-made statistical procedure for these challenges and combine visual analytics with movement detection. Contrary to common stay-point algorithms, we do not only distinguish between stops and moves, but also consider undesired awakenings. This leads to a more detailed interpretation scheme offering usages for online (e.g.~monitoring of orders) and offline applications (e.g.~detection of problematic areas). The approach does not require other information than the raw IPS output and enables an ad-hoc analysis. We underline our findings in an extensive case study with real IPS data of our industry partner.

cs.LG↗

arXiv2020

Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimization

Field sales forces play an important role in direct marketing, especially for companies offering complex products, services, or solutions in the business-to-business context. A key task of sales representatives in operational planning is to select the most promising customers to visit within the next days. On an operative horizon, a key task for sales representatives is to select the most promising customers to visit within the next days. A strongly varying set of scoring methods predicting or approximating the expected response exists for this customer selection phase. However, in the case of field sales forces, the final customer selection is strongly interrelated to the tour planning decisions. To this end, we formalize variants of the profitable sales representatives tour problem as a multi-period team orienteering problem, thereby providing a unified view on the customer scoring and the tour planning phase. In an extensive computational study on real-world instances from the retail industry, we systematically examine the impact of the aggregation level and the content of information provided by a scoring method and the sensitivity of the proposed models concerning prediction errors. We show that the selection of a customer scoring and tour planning variant depends on the available data. Furthermore, we work out where to put effort in the data acquisition and scoring phase to get better operational tours.

math.OC↗

arXiv2020

Celestial calendar-paintings and culture-based digital storytelling: cross-cultural, interdisciplinary, STEM/STEAM resources for authentic astronomy education engagement

In D(L)akota star knowledge, the Sun is known as Wi and the Moon is Han-Wi. They have an important relationship, husband and wife. The pattern of their ever-changing relationship is mirrored in the motions of Sun and Moon as seen from our backyards, also called the lunar phases. The framework of the cultural teaching is storytelling and relationships. Cultural perspectives in astronomy such as this remind us of how indigenous ways of knowing are rooted in inclusion, engagement, and relevancy. Designed by A. Lee in 2007, the Native Skywatchers initiative seeks to remember and revitalize indigenous star and earth knowledge, promoting the native voice as the lead voice. The overarching goal of Native Skywatchers is to communicate the knowledge that indigenous people traditionally practiced a sustainable way of living and sustainable engineering through a living and participatory relationship with the above and below, sky and earth. In 2012 two indigenous star maps were created: the Ojibwe Giizhig Anung Masinaaigan-Ojibwe Sky Star Map (A. Lee, W. Wilson, C. Gawboy), and the D(L)akota star map, Makoce Wicanhpi Wowapi (A. Lee, J. Rock). In 2016, a collaboration with W. Buck of the Manitoba First Nations Resource Centre (MFNRC), produced a third star map: Ininew Achakos Masinikan-Cree Star Map Book. We aim to improve current inequities in education for native young people especially through STEM engagement, to inspire increased cultural pride, and promote community wellness. Presented here will be recently created resources such as: astronomical calendar-paintings and short videos that exist at the intersection of art-science-culture. As we look for sustainable ways to widen participation in STEM, particularly in astronomy education, part of the conversation needs to consider the place for art and culture in STEM.

physics.ed-ph↗

arXiv2020

Idle vehicle repositioning for dynamic ride-sharing

In dynamic ride-sharing systems, intelligent repositioning of idle vehicles enables service providers to maximize vehicle utilization and minimize request rejection rates as well as customer waiting times. In current practice, this task is often performed decentrally by individual drivers. We present a centralized approach to idle vehicle repositioning in the form of a forecast-driven repositioning algorithm. The core part of our approach is a novel mixed-integer programming model that aims to maximize coverage of forecasted demand while minimizing travel times for repositioning movements. This model is embedded into a planning service also encompassing other relevant tasks such as vehicle dispatching. We evaluate our approach through extensive simulation studies on real-world datasets from Hamburg, New York City, and Manhattan. We test our forecast-driven repositioning approach under a perfect demand forecast as well as a naive forecast and compare it to a reactive strategy. The results show that our algorithm is suitable for real-time usage even in large-scale scenarios. Compared to the reactive algorithm, rejection rates of trip requests are decreased by an average of 2.5 percentage points and customer waiting times see an average reduction of 13.2%.

math.OC↗

arXiv2020

Travel Time Prediction using Tree-Based Ensembles

In this paper, we consider the task of predicting travel times between two arbitrary points in an urban scenario. We view this problem from two temporal perspectives: long-term forecasting with a horizon of several days and short-term forecasting with a horizon of one hour. Both of these perspectives are relevant for planning tasks in the context of urban mobility and transportation services. We utilize tree-based ensemble methods that we train and evaluate on a dataset of taxi trip records from New York City. Through extensive data analysis, we identify relevant temporal and spatial features. We also engineer additional features based on weather and routing data. The latter is obtained via a routing solver operating on the road network. The computational results show that the addition of this routing data can be beneficial to the model performance. Moreover, employing different models for short and long-term prediction is useful as short-term models are better suited to mirror current traffic conditions. In fact, we show that accurate short-term predictions may be obtained with only little training data.

stat.ML↗