arXiv subjects

Patrick Stokkink

Publications and source records attributed to Patrick Stokkink.

4 recordsLinked to original sources

arXiv2026

Dominance-Based Feasibility Inference for Packing-Constrained Pickup and Delivery Problems

Routing and packing are intrinsically coupled in transport problems, requiring joint planning for cost-efficient and physically realizable solutions. We study a pickup and delivery problem with two-dimensional packing constraints (2P-PDP). Unlike vehicle routing variants where items are loaded before vehicles leave the depot and packing is validated only once, the 2P-PDP induces non-monotonic free-space evolution, substantially increasing feasibility-checking complexity. To address this bottleneck, we propose a generic dominance-based feasibility framework that is embeddable in a broad class of exact and heuristic routing algorithms. Under no-relocation constraints, inferring feasibility from a previously verified packing state requires preserving the pickup and delivery order of onboard items. To this end, we introduce an order-preserving mapping that jointly captures geometric containment and sequence compatibility, enabling dominance-based inference by embedding the new packing state into a verified reference plan. To further reduce dominance-screening overhead, we design three search rules to guide candidate exploration and tailored strategies to store, retrieve, and prioritize verified states. Computational experiments show that the proposed approach reduces feasibility-checking time by up to 42% compared to a benchmark without dominance. The improvement stems from reducing exact packing-procedure calls, shifting verification effort away from the most computationally expensive stage.

math.OC↗

arXiv2025

The Impact of AI on Educational Assessment: A Framework for Constructive Alignment

The influence of Artificial Intelligence (AI), and specifically Large Language Models (LLM), on education is continuously increasing. These models are frequently used by students, giving rise to the question whether current forms of assessment are still a valid way to evaluate student performance and comprehension. The theoretical framework developed in this paper is grounded in Constructive Alignment (CA) theory and Bloom's taxonomy for defining learning objectives. We argue that AI influences learning objectives of different Bloom levels in a different way, and assessment has to be adopted accordingly. Furthermore, in line with Bloom's vision, formative and summative assessment should be aligned on whether the use of AI is permitted or not. Although lecturers tend to agree that education and assessment need to be adapted to the presence of AI, a strong bias exists on the extent to which lecturers want to allow for AI in assessment. This bias is caused by a lecturer's familiarity with AI and specifically whether they use it themselves. To avoid this bias, we propose structured guidelines on a university or faculty level, to foster alignment among the staff. Besides that, we argue that teaching staff should be trained on the capabilities and limitations of AI tools. In this way, they are better able to adapt their assessment methods.

cs.HC↗

arXiv2022

A Continuum Approximation Approach to the Hub Location Problem in a Crowd-Shipping System

Last-mile delivery in the logistics chain contributes to emissions and increased congestion. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery, but relies heavily on the availability of occasional couriers. In this work, we propose a hub-based crowd-shipping system that aims to attract sufficient potential crowd-shippers to serve a large portion of the demand for small parcels. While small-scale versions of this problem have been recently addressed, a scaling to larger instances significantly complexifies the problem. A heuristic approach based on continuum approximation is designed to evaluate the quality of a potential set of hub locations. By combining an efficient and accurate approximation method with a large neighborhood search heuristic, we are able to efficiently find a good set of hub locations, even for large scale networks. Furthermore, on top of determining good hub locations, our methods allow to identify the expected number of delivered parcels in every region, which can be used to design a smart dynamic assignment strategy. A case study on the Washington DC network shows that hubs are built at locations that are both geographically central, but most importantly are popular origins for crowd-shippers. The optimal number of hubs is mainly dependent on the marginal number of parcels that can be served by crowd-shippers from a specific hub, relative to the costs involved in opening that hub. The performance of our algorithm is close to that of a simulation-optimization algorithm, yet being up to 25 times faster. Thereby, the results show a dynamic assignment policy based on continuum approximation estimates outperforms existing assignment strategies.

math.OC↗

arXiv2021

Incentive-Based Electric Vehicle Charging for Managing Bottleneck Congestion

We propose an incentive-based traffic demand management policy to alleviate traffic congestion on a road stretch that creates a bottleneck for the commuters. The incentive targets electric vehicles owners by proposing a discount on the energy price they use to charge their vehicles if they are flexible in their departure time. We show that, with a sufficient monetary budget, it is possible to completely eliminate the traffic congestion and we compute the optimal discount. We analyse also the case of limited budget, when the congestion cannot be completely eliminated. We compute analytically the policy minimising the congestion and estimate the level of inefficiency for different budgets. We corroborate our theoretical findings with numerical simulations that allow us to highlight the power of the proposed method in providing practical advice for the design of policies.

math.OC↗