arXiv subjects

Wout Dullaert

Publications and source records attributed to Wout Dullaert.

4 recordsLinked to original sources

arXiv2025

Pricing, bundling, and driver behavior in crowdsourced delivery

Challenges in last-mile delivery have encouraged solutions like crowdsourced delivery, in which occasional drivers undertake delivery tasks along their pre-planned trips in exchange for compensation. A key challenge is that drivers' acceptance behavior towards offered tasks is uncertain and influenced by task properties and compensation. The current literature lacks formulations that address this challenge with a joint and exact approach. Hence, we formulate an integrated optimization problem that maximizes total expected cost savings by offering bundles of tasks to occasional drivers. We simultaneously determine the optimal set of bundles, their assignment to drivers, and personalized compensations for each bundle-driver pair while considering bundle- and compensation-dependent acceptance probabilities, captured via generic logistic functions. The vast number of potential bundles, combined with incorporating acceptance probabilities leads to a mixed-integer nonlinear program (MINLP) with exponentially many variables. We address these complexities by exploiting properties of the problem, leading to an exact linearization of the MINLP tackled via a tailored exact column generation algorithm. Our algorithm uses a variant of the elementary shortest path problem with resource constraints that features a non-linear and non-additive objective function as subproblem, for which we develop tailored dominance and pruning strategies. We introduce several heuristic and exact variants, and perform an extensive experiments evaluating the performance and the solution structures. The results demonstrate the efficiency of these algorithms for instances with up to 120 tasks and 60 drivers, and highlight the advantages of integrated decision-making over sequential approaches. The sensitivity analysis indicates that sensitivity to compensation is the most influential factor in shaping the bundle structure.

math.OC↗

arXiv2024

Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems

This study develops a framework based on reinforcement learning to dynamically manage a large portfolio of search operators within meta-heuristics. Using the idea of tabu search, the framework allows for continuous adaptation by temporarily excluding less efficient operators and updating the portfolio composition during the search. A Q-learning-based adaptive operator selection mechanism is used to select the most suitable operator from the dynamically updated portfolio at each stage. Unlike traditional approaches, the proposed framework requires no input from the experts regarding the search operators, allowing domain-specific non-experts to effectively use the framework. The performance of the proposed framework is analyzed through an application to the permutation flowshop scheduling problem. The results demonstrate the superior performance of the proposed framework against state-of-the-art algorithms in terms of optimality gap and convergence speed.

cs.LG↗

arXiv2024

Task-splitting in home healthcare routing and scheduling

This paper introduces the concept of task-splitting into home healthcare (HHC) routing and scheduling. It focuses on the design of routes and timetables for caregivers providing services at patients' homes. Task-splitting is the division of a (lengthy) patient visit into separate visits that can be performed by different caregivers at different times. The resulting split parts may have reduced caregiver qualification requirements, relaxed visiting time windows, or a shorter/longer combined duration. However, additional temporal dependencies can arise between them. To incorporate task-splitting decisions into the planning process, we introduce two different mixed integer linear programming formulations, a Miller-Tucker-Zemlin and a time-indexed variant. These formulations aim to minimize operational costs while simultaneously deciding which visits to split and imposing a potentially wide range of temporal dependencies. We also propose pre-processing routines for the time-indexed formulation and two heuristic procedures. These methods are embedded into the branch-and-bound approach as primal and improvement heuristics. The results of our computational study demonstrate the additional computational difficulty introduced by task-splitting possibilities and the associated additional synchronization, and the usefulness of the proposed heuristic procedures. From a planning perspective, our results indicate that integrating task-splitting decisions into the planning process reduces staff requirements, decreases HHC operational costs, and allows caregivers to spend relatively more time on tasks aligned with their qualifications.

math.OC↗

arXiv2023

The role of individual compensation and acceptance decisions in crowdsourced delivery

One of the recent innovations in urban distribution is crowdsourced delivery, where deliveries are made by occasional drivers who wish to utilize their surplus resources (unused transport capacity) by making deliveries in exchange for some compensation. The potential benefits of crowdsourced delivery include reduced delivery costs and increased flexibility (by scaling delivery capacity up and down as needed). The use of occasional drivers poses new challenges because (unlike traditional couriers) neither their availability nor their behavior in accepting delivery offers is certain. The relationship between the compensation offered to occasional drivers and the probability that they will accept a task has been largely neglected in the scientific literature. Therefore, we consider a setting in which compensation-dependent acceptance probabilities are explicitly considered in the process of assigning delivery tasks to occasional drivers. We propose a mixed-integer nonlinear model that minimizes the expected delivery costs while identifying optimal assignments of tasks to a mix of professional and occasional drivers and their compensation. We propose an exact two-stage solution algorithm that allows to decompose compensation and assignment decisions for generic acceptance probability functions and show that the runtime of this algorithm is polynomial under mild conditions. Finally, we also study a more general case of the considered problem setting, show that it is NP-hard and propose an approximate linearization scheme of our mixed-integer nonlinear model. The results of our computational study show clear advantages of our new approach over existing ones. They also indicate that these advantages remain in dynamic settings when tasks and drivers are revealed over time and in which case our method constitutes a fast, yet powerful heuristic.

math.OC↗