Wouter van Heeswijk
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View article: Anatomy of Peer-to-Peer (P2P) Lending Fraud: A Review with Managerial Implications
Anatomy of Peer-to-Peer (P2P) Lending Fraud: A Review with Managerial Implications Open
Peer-to-peer (P2P) lending platforms have transformed the financial technology sector by facilitating direct interactions between borrowers and lenders, eliminating the need for traditional financial intermediaries. While this innovation e…
View article: Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling
Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling Open
Accurately estimating the impact of road maintenance schedules on traffic conditions is important because maintenance operations can substantially worsen congestion if not carefully planned. Reliable estimates allow planners to avoid exces…
View article: The Stochastic Dynamic Postdisaster Inventory Allocation Problem with Trucks and UAVs
The Stochastic Dynamic Postdisaster Inventory Allocation Problem with Trucks and UAVs Open
Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It intro…
View article: Towards self‐organizing logistics in transportation: a literature review and typology
Towards self‐organizing logistics in transportation: a literature review and typology Open
Deploying self‐organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision‐making and decentralized or distributed control structures are combined. Such contr…
View article: The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs
The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs Open
Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It intro…
View article: Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty
Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty Open
Effective humanitarian relief operations are challenging in the aftermath of disasters, as trucks are often faced with considerable travel time uncertainties due to damaged transportation networks. Efficient deployment of Unmanned Aerial V…
View article: Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces Open
Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning. Existing solution approaches can handle unstructured LDAS with up to a few million actions. However, many real-world applications in logistics, produ…
View article: Predicting truck parking occupancy using machine learning
Predicting truck parking occupancy using machine learning Open
The logistics industry faces an increasing shortage of truck parking spots. This results in illegal parking or fatigued driving with hazardous consequences for traffic safety, as truck drivers have no insight into future availability of pa…
View article: Strategic bidding in freight transport using deep reinforcement learning
Strategic bidding in freight transport using deep reinforcement learning Open
This paper presents a multi-agent reinforcement learning algorithm to represent strategic bidding behavior in freight transport markets. Using this algorithm, we investigate whether feasible market equilibriums arise without any central co…
View article: Deep Reinforcement Learning in Linear Discrete Action Spaces
Deep Reinforcement Learning in Linear Discrete Action Spaces Open
Problems in operations research are typically combinatorial and high-dimensional. To a degree, linear programs may efficiently solve such large decision problems. For stochastic multi-period problems, decomposition into a sequence of one-s…
View article: Why would we get attacked? An analysis of attacker's aims behind DDoS attacks
Why would we get attacked? An analysis of attacker's aims behind DDoS attacks Open
Reliable availability to the internet and internet-based services is crucial in today’s world. DDoS attacks pose a severe threat to the availability of such online resources – especially owing to booters – virtually everyone can execute th…
View article: Smart Containers With Bidding Capacity: A Policy Gradient Algorithm for\n Semi-Cooperative Learning
Smart Containers With Bidding Capacity: A Policy Gradient Algorithm for\n Semi-Cooperative Learning Open
Smart modular freight containers -- as propagated in the Physical Internet\nparadigm -- are equipped with sensors, data storage capability and intelligence\nthat enable them to route themselves from origin to destination without manual\nin…
View article: Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces
Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces Open
Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decision problems. However, in multi-period decision problems, w…
View article: An urban consolidation center in the city of Copenhagen: A simulation study
An urban consolidation center in the city of Copenhagen: A simulation study Open
Urban consolidation centers (UCCs) have a key role in many initiatives in urban logistics, yet few of them are successful in the long run. The high costs often prevent attracting a sufficient number of UCC users. In this paper, we study su…
View article: Scalability and Performance of Decentralized Planning in Flexible Transport Networks
Scalability and Performance of Decentralized Planning in Flexible Transport Networks Open
This paper addresses the planning of freight dispatch in flexible transport networks featuring multiple carriers. To deal with the computational challenges of the planning problem, we develop an Approximate Dynamic Programming (ADP) algori…