Bidder optimal assignments for general utilities

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Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

As computational thinking (CT) becomes increasingly acknowledged as an important skill in education, self-directed learning (SDL) emerges as a key strategy for developing this capability. The advent of generative AI (GenAI) conversational agents has disrup ...

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Smartphones enable understanding human behavior with activity recognition to support peoples daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous ...

Existing activity-based modeling predominantly focus on out-of-home activities in order to un- derstand transport demand. In this research, we extend the state of practice in activity-based modelling by determining both in- and out-of-home activities in a ...

We study a canonical model of decentralized exchange for a durable good or asset, where agents are assumed to have time-varying, heterogeneous utility types. Whereas the existing literature has focused on the special case of two types, we allow agents' uti ...

Social Engagement is a novel business model transforming final users of a service from passive into active components. In this framework, people are contacted by a company and they are asked to perform tasks in exchange for a reward. This arises the compli ...

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

The equilibrium of the archetypal morning commute problem with alpha -,B - gamma preferences has been shown to be unstable with various rational adjustment mechanisms, under both continuous and discrete day-to-day dynamics. It is not clear however whether ...

We propose a framework to find optimal price-based policies to regulate markets characterized by oligopolistic competition and in which consumers make a discrete choice among a finite set of alternatives. The framework accommodates general discrete choice ...

In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, b ...

The objective of this thesis is to develop a general methodology to incorporate a disaggregate demand representation in supply-oriented optimization problems that allows to capture the interplay between the behavior of individuals and the decisions to be o ...

The aggregators are intermediary players at the distribution system level. They manage the financial transactions of resources such as micro-turbines (MTs), photo-voltaic (PV) production systems, flexible demands (FDs), and must-run demands (MDs) in the en ...

This paper investigates the stability of departure time choice within a population of commuters that need to pass through a bottleneck of constant capacity. It is shown that the corresponding utility function is monotonic if and only if the marginal utilit ...

Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of geo-located data r ...

Discrete Choice Models (DCMs) have a distinct advantage over Machine Learning (ML) classification algorithms, in that they employ a highly interpretable linear structure. However, a key drawback of DCMs compared to ML is the need to specify the utility fun ...

Fatigue safety verification of existing bridges that uses ‘‘re-calculation’’ based on codes, usually results in insufficient fatigue safety, triggering invasive interventions. Instead of “re-calculation”, Structural Health Monitoring (SHM) should be used f ...

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

For system identification, most sensor-placement strategies are based on the minimization of the model-parameter uncertainty. However, reducing the uncertainty in remaining-life prognosis of structures is often more relevant. This paper proposes an optimiz ...

In a supply chain, the importance of information elicitation from the supply chain players is vital to design supply chain network. The rationality and self-centredness of these players causes the information asymmetry in the supply chain and thus situatio ...

Bidder optimal assignments for general utilities

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OASIS: An integrated optimisation framework for activity scheduling

Generative AI-Enabled Conversational Interaction to Support Self-Directed Learning Experiences in Transversal Computational Thinking

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

A utility optimization-based framework for joint in- and out-of-home scheduling

Heterogeneity in decentralized asset markets

A chance-constraint approach for optimizing Social Engagement-based services

Capturing trade-offs between daily scheduling choices

Monotonicity in the trip scheduling problem

Price-based regulation of oligopolistic markets under discrete choice models of demand

Integrating advanced discrete choice models in mixed integer linear optimization

A general framework for the integration of complex choice models into mixed integer optimization

Balancing management of strategic aggregators using non-cooperative game theory

Scheduling trips through a bottleneck: monotonicity and stability

On (The Lack Of) Location Privacy in Crowdsourcing Applications

Weak teachers: assisted specification of discrete choice models using ensemble learning algorithms

Quantification of the conditional value of SHM data for the fatigue safety evaluation of a road viaduct

Privacy in Recommender Systems

Measurement system design for civil infrastructure using expected utility

Equilibrium analysis in multi-echelon supply chain with multi-dimensional utilities of inertial players