arXiv subjects

Elizabeth Stojanovski

Publications and source records attributed to Elizabeth Stojanovski.

9 recordsLinked to original sources

arXiv2026

Deterministic Structure of Vertical Configurations in Minimal Picker Tours for Rectangular Warehouses

The picker routing problem seeks the shortest tour through a warehouse that visits every item in a given pick-list and returns to the depot. For rectangular warehouses, dynamic programming algorithms solve this problem by sequentially evaluating combinations of vertical edge configurations within subaisles and horizontal edge configurations between aisles. These methods proceed through stages one after another, but how those stages relate to each other has received limited structural analysis. Building on our recent structural result for rectangular warehouses, which shows that connecting double traversals are not required to maintain tour connectivity, we prove that for rectangular warehouses of any size, the horizontal edge structure of a minimal tour subgraph uniquely determines the required vertical edge configurations. The proof uses a case analysis on horizontal degree along each aisle and at merged-segment endpoints, showing that the admissible vertical pattern in each regime is uniquely determined by Eulerian parity and by minimizing traversal length. This deterministic relationship implies that vertical configuration stages in existing dynamic programming algorithms can be replaced by a direct inference step, reducing the combinatorial complexity of the problem and providing a structural foundation for developing more efficient exact methods for warehouse layouts of any size.

math.OC↗

arXiv2026

Double Traversals in Boundary Subaisles: Implications for Two-Block Layouts

The order picking problem seeks the shortest warehouse route that visits all required item locations. Strict conditions are known for single-block rectangular layouts under which optimal routes never require double traversals, while broader results show that double traversals serving cross-aisle connectivity can always be avoided. We strengthen these findings by proving that no double traversals are needed in the boundary subaisles, the uppermost and lowermost subaisle segments, of warehouses with at least two non-empty aisles. This yields a unified strict condition for all single-block layouts and for two-block layouts with more than one aisle. For these widely used layouts, exact methods such as dynamic programming and mathematical programming can therefore exclude the double-traversal configuration from every boundary subaisle, reducing the number of admissible edge configurations without loss of optimality.

math.OC↗

arXiv2026

Modified Dynamic Programming Algorithms for Order Picking in Single-Block and Two-Block Rectangular Warehouses

Recent research has shown that optimal picker tours in rectangular warehouses exhibit deterministic travel patterns within each aisle, and that certain previously considered traversals are unnecessary. Using these insights, this paper proposes modifications to dynamic programming algorithms that improve computational efficiency without affecting optimality. For layouts with and without a central cross-aisle, the modifications preserve linear-time complexity in the number of aisles while reducing the number of state-action evaluations per stage. The proposed modifications reduce computational effort by factors up to 1.81, confirmed by numerical experiments. These findings are encouraging and highlight how structural refinements can yield significant improvements in practical performance of algorithms.

math.OC↗

arXiv2025

Benchmarking of Clustering Validity Measures Revisited

Validation plays a crucial role in the clustering process. Many different internal validity indexes exist for the purpose of determining the best clustering solution(s) from a given collection of candidates, e.g., as produced by different algorithms or different algorithm hyper-parameters. In this study, we present a comprehensive benchmark study of 26 internal validity indexes, which includes highly popular classic indexes as well as more recently developed ones. We adopted an enhanced revision of the methodology presented in Vendramin et al. (2010), developed here to address several shortcomings of this previous work. This overall new approach consists of three complementary custom-tailored evaluation sub-methodologies, each of which has been designed to assess specific aspects of an index's behaviour while preventing potential biases of the other sub-methodologies. Each sub-methodology features two complementary measures of performance, alongside mechanisms that allow for an in-depth investigation of more complex behaviours of the internal validity indexes under study. Additionally, a new collection of 16177 datasets has been produced, paired with eight widely-used clustering algorithms, for a wider applicability scope and representation of more diverse clustering scenarios.

stat.ML↗

arXiv2025

Double Traversals in Optimal Picker Routes for Warehouses with Multiple Blocks

Order picking is a process that involves collecting items from their respective locations within a warehouse. There exist dynamic programming algorithms for finding the minimal picker route by considering only a limited number of options for possible travel within a subaisle. Although one such action, traversing an aisle twice, has been shown to never be required for a rectangular warehouse with two cross-aisles, this is not the case when there are more than two cross-aisles. In this work, we demonstrate that double traversals within a subaisle are not required to connect cross-aisle travel regardless of the number of cross-aisles. This result simplifies the structure of feasible tours, enabling more efficient algorithms.

math.OC↗

arXiv2024

Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data

The Minimum Volume Covering Ellipsoid (MVCE) problem, characterised by $n$ observations in $d$ dimensions where $n \gg d$, can be computationally very expensive in the big data regime. We apply methods from randomised numerical linear algebra to develop a data-driven leverage score sampling algorithm for solving MVCE, and establish theoretical error bounds and a convergence guarantee. Assuming the leverage scores follow a power law decay, we show that the computational complexity of computing the approximation for MVCE is reduced from $\mathcal{O}(nd^2)$ to $\mathcal{O}(nd + \text{poly}(d))$, which is a significant improvement in big data problems. Numerical experiments demonstrate the efficacy of our new algorithm, showing that it substantially reduces computation time and yields near-optimal solutions.

math.OC↗

arXiv2024

A Modified Algorithm for Optimal Picker Routing in a Single Block Warehouse

The order picker routing problem involves finding the optimal tour of a warehouse that collects all the required items on a given pick list. Ratliff and Rosenthal introduced a dynamic programming algorithm for solving this problem in polynomial time by sequentially adding edges inside and between each aisle to construct a tour. We provide a method where only transitions from one aisle to the next are considered, significantly reducing the number of stages in the algorithm.

math.OC↗

arXiv2024

Deep Reinforcement Learning for Picker Routing Problem in Warehousing

Order Picker Routing is a critical issue in Warehouse Operations Management. Due to the complexity of the problem and the need for quick solutions, suboptimal algorithms are frequently employed in practice. However, Reinforcement Learning offers an appealing alternative to traditional heuristics, potentially outperforming existing methods in terms of speed and accuracy. We introduce an attention based neural network for modeling picker tours, which is trained using Reinforcement Learning. Our method is evaluated against existing heuristics across a range of problem parameters to demonstrate its efficacy. A key advantage of our proposed method is its ability to offer an option to reduce the perceived complexity of routes.

cs.LG↗

arXiv2020

Modeling the Dynamics of the COVID-19 Population in Australia: A Probabilistic Analysis

The novel Corona Virus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.

stat.AP↗