Crew costs make up the second largest expense for airlines, behind only fuel costs. This motivates a potential gain in improving crew efficiency within the bounds set by the law and collective labour agreements. Doing so requires to take into account aircraft routes and crew pairings, and the specifics of the airline’s network. This work presents an integrated model for obtaining efficient crew pairings for airlines operating point-to-point networks, while also allowing for flight retiming. By considering simultaneously both crew pairing and constrained aircraft routing, better-performing solutions can be obtained. The greater complexity of the integrated model is addressed by means of a custom branch-and-price approach with a shortest path pricing sub-problem, in order to obtain exact solutions. The results of the integrated model are evaluated on a real-world case of an European low-cost carrier that operates a short-haul point-to-point network. Results show a reduction in crew duties of 10% and an increase in crew efficiency metrics by up to 1.5%, optimising the carrier’s complete network of 926 flights over a full week.

As a tool serving other disciplines of enquiry, artificial intelligence (AI) offers the potential of a potent discovery, a design and analysis paradigm to address (new) questions in urban planning. This thematic issue raises a forum for cross-disciplinary dialogues at the intersection of urban planning and AI. Nine articles discuss both emerging use cases in urban planning practice and the relevant AI techniques being used and developed, as well as articulate the challenges associated. Future development of AI in urban planning shall address the ethical, inclusive, and just implications of AI applications for urban planning while navigating human and AI agents’ interactions and intra-actions to facilitate a better understanding of the intentions of AI development and use, and the impacts on the behaviour of designers and users in complex urban planning practices.

This article aimed to assess the potential impact of policy actions to support mass timber construction through an ex ante policy analysis in Amsterdam. Through a combination of policy coherence analysis and agent-based simulation, the study evaluates 130 policy actions, including 80 specific instruments, for the transition from traditional masonry to mass timber construction. The coherence analysis reveals a predominance of regulatory instruments (62%) and a lack of active economic measures (16%), which limits their impact on circular city development. The simulation tested three instruments - demolition notification, a mass timber subsidy proxy and a carbon tax proxy - to assess their individual and combined effectiveness. Isolated measures, such as material price adjustments, were found to be insufficient due to systemic inertia. However, the combination of subsidies and carbon taxes proves more effective, significantly increasing the uptake of mass timber construction as its cost is reduced and construction companies develop expertise. A key finding highlights the complementary role of recycled concrete in supporting mass timber construction, highlighting the need for integrated policies targeting both mass timber and secondary materials. Improving industry knowledge and expertise is identified as a transformative approach to reducing costs and overcoming barriers to adoption. This research is the first contribution to demonstrate the value of ex ante policy evaluation and agent-based simulation in formulating coherent and effective policies for circular city transitions. Policy makers in Amsterdam and other Dutch cities are advised to implement synergistic instruments, support local material reuse and invest in capacity building to achieve carbon neutrality and resource circularity in urban construction. The findings provide actionable guidance for Amsterdam and similar cities seeking to promote sustainable and circular urban environments.

Uncertainty quantification remains a difficult challenge in reinforcement learning. Several algorithms exist that successfully quantify uncertainty in a practical setting. However it is unclear whether these algorithms are theoretically sound and can be expected to converge. Furthermore, they seem to treat the uncertainty in the target parameters in different ways. In this work, we unify several practical algorithms into one theoretical framework by defining a new Bellman operator on distributions, and show that this Bellman operator is a contraction. We highlight use cases of our framework by analyzing an existing Bayesian Q-learning algorithm, and also introduce a novel uncertainty-aware variant of PPO that adaptively sets its clipping hyperparameter.

The Dutch housing market comprises three sectors: social-rented, private-rented, and owner-occupied. The contemporary market is marked by a shortage of supply and a large subsidised social sector. Waiting lists for social housing are growing, whereas households with incomes above the limit do not or cannot leave the social sector. Government policy and market regulations change frequently, not least for political reasons. In view of commonly recognised problems in the housing market, this article considers the ‘internal demand’ of those households that are dissatisfied with their current residence. We examine the effects of regulatory policy by means of an exploratory agent-based simulation. The results provide perspectives on how internal demand is impacted by regulations in a housing market that is suffering from a shortage, and allow decision makers to weigh the pros and cons of policy measures.

Carbon capture and sequestration initiatives make new demands on modern reservoir simulators. To find optimal locations and volumes of CO2 to inject into a subsurface to maximize CO2 storage, we must simulate a large ensemble of injection cases. One possible solution to the computational complexity of this task is to employ machine-learning models which, after a one-off overhead cost of training, can infer and predict future states of a reservoir several orders of magnitude faster than traditional methods. Most previous work in the literature has primarily focused on either convolution-based methods or, more recently, neural operator-based methods, to predict the evolution of state variables. These architectures have shown promise in predicting on structured reservoir grids but lack the capability to extend the same level of accuracy to unstructured grids. Graph neural networks (GNNs) overcome this bottleneck by incorporating inductive biases arising from local message-passing mechanisms, facilitating convolution operations over complex graphs and meshes. In this work, we present a novel autoregressive GNN autoencoder to predict time-varying state variables for an ensemble of CO2 injection cases. We implement a graph convolution network for the message-passing protocol and incorporate physics-informed edge weights between cell connections to guide flow. An exhaustive set of node features are used to train the model on the hyperbolic evolution of phase saturations while preserving the ellipticity in the pressure. We test the performance of the GNN model for (1) its ability to predict state variables for varying injection rates of CO2, (2) for the post-injection phase, and (3) under different unseen geological configurations. Training and testing are performed by constructing ensembles of 2D, 3D, and real field cases that best represent these scenarios. For the 2D regular grid case, we observe that the model can capture pressure and saturation values accurately, even for highly varying injection rates and with only a limited amount of data. This performance is maintained in the post-injection phase. A key advantage of GNNs is that they show a distinct ability for transfer learning on ensembles of unseen geological configurations. We observe that the model can predict the shape and intensity of wavefronts of certain cases with no prior exposure to the specific static properties during training. Similar results are produced for 3D grids and real field cases.

The transportation industry is a significant source of greenhouse gas emissions, with freight transport emerging as one of the main contributors owing to its extensive mileage and substantial weight. As a result, electrification of road transportation has become a vital step in reducing direct CO₂ emissions. While the adoption of passenger electric vehicles has gained notable traction, the landscape for Heavy-Duty Electric Vehicles (HDEVs) is still in its early stages of development. Accelerating the advancement and adoption of HDEVs hinges on prioritizing the installation of their charging infrastructure. This requires a deep understanding of HDEVs' energy and power requirements while also considering grid limitations. Meeting the high demand for charging necessitates exploring on-site renewable energy generation and stationary batteries as viable solutions. Recognizing this imperative, a multiobjective sizing model has been developed, tailored specifically to address the requirements of HDEV charging stations. These objectives include minimizing investment costs, penalizing undercharged or rejected HDEVs' charging demand, reducing idle charger time, and managing expenditures within a charging station. The key outcomes of the model encompass various critical factors essential for designing and implementing charging infrastructure for HDEVs. These factors include determining the optimal number of PV panels and wind turbines to harness renewable energy, specifying the capacity of the battery energy storage system, and identifying the necessary number and rated power of chargers in alignment with the grid contract limit.

Exploration in reinforcement learning remains a difficult challenge. In order to drive exploration, ensembles with randomized prior functions have recently been popularized to quantify uncertainty in the value model. There is no theoretical reason for these ensembles to resemble the actual posterior, however. In this work, we view training ensembles from the perspective of Sequential Monte Carlo, a Monte Carlo method that approximates a sequence of distributions with a set of particles. In particular, we propose an algorithm that exploits both the practical flexibility of ensembles and theory of the Bayesian paradigm. We incorporate this method into a standard Deep Q-learning agent (DQN) and experimentally show qualitatively good uncertainty quantification and improved exploration capabilities over a regular ensemble.

Metaheuristics are known to be effective in finding good solutions in combinatorial optimization, but solving stochastic problems is costly due to the need for evaluation of multiple scenarios. We propose a general method to reduce the number of scenario evaluations per solution and thus improve metaheuristic efficiency. We use a sequential sampling procedure exploiting estimates of the solutions’ expected objective values. These values are obtained with a predictive model, which is founded on an estimated discrete probability distribution linearly related to all solutions’ objective distributions; the probability distribution is continuously refined based on incoming solution evaluation. The proposed method is tested using simulated annealing, but in general applicable to single solution metaheuristics. The method’s performance is compared to descriptive sampling and an adaptation of a sequential sampling method assuming noisy evaluations. Experimental results on three problems indicate the proposed method is robust overall, and performs better on average than the baselines on two of the problems.

Robust Optimal Control (ROC) with adjustable uncertainties has proven to be effective in addressing critical challenges within modern energy networks, especially the reserve and provision problem. However, prior research on ROC with adjustable uncertainties has predominantly focused on the scenario of uncertainties modeled as continuous variables. In this paper, we explore ROC with binary adjustable uncertainties, where the uncertainties are modeled by binary decision variables, marking the first investigation of its kind. To tackle this new challenge, firstly we introduce a metric designed to quantitatively measure the extent of binary adjustable uncertainties. Then, to balance computational tractability and adaptability, we restrict control policies to be affine functions with respect to uncertainties, and propose a general design framework for ROC with binary adjustable uncertainties. To address the inherent computational demands of the original ROC problem, especially in large-scale applications, we employ strong duality (SD) and big-M-based reformulations to create a scalable and computationally efficient Mixed-Integer Linear Programming (MILP) formulation. Numerical simulations are conducted to showcase the performance of our proposed approach, demonstrating its applicability and effectiveness in handling binary adjustable uncertainties within the context of modern energy networks.

The way how the uncertainties are represented by sets plays a vital role in the performance of robust optimization (RO). This paper presents a novel approach leveraging machine learning (ML) techniques to construct data-driven uncertainty sets from historical uncertainty data for RO problems. The proposed method integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), and Principle Component Analysis (PCA) systematically to eliminate the influence of uncertainty scenarios with low occurrence probability and generate a nonconvex uncertainty set that is a union of multiple basic subsets (box or ellipsoid) without sacrificing its computational tractability. In addition to presenting a comprehensive algorithm for uncertainty set development, this paper offers detailed guidelines for parameter tuning and performance analysis. By harnessing the well-established ML packages scikit-learn, a Python-based toolkit for implementing the proposed approach is also provided. Furthermore, a computationally efficient solution for a two-stage linear RO problem with the proposed data-driven uncertainty set is derived, alongside establishing a probabilistic guarantee of constraint satisfaction for out-of-sample uncertainties. Extensive numerical experiments, conducted on both synthetic and real-world datasets as well as an optimization-based control problem, are performed to demonstrate the efficacy of the proposed methodology.

Optimization models used to make discrete decisions often contain uncertain parameters that are context-dependent and estimated through prediction. To account for the quality of the decision made based on the prediction, decision-focused learning (end-to-end predict-then-optimize) aims at training the predictive model to minimize regret, i.e., the loss incurred by making a suboptimal decision. Despite the challenge of the gradient of this loss w.r.t. the predictive model parameters being zero almost everywhere for optimization problems with a linear objective, effective gradient-based learning approaches have been proposed to minimize the expected loss, using the empirical loss as a surrogate. However, empirical regret can be an ineffective surrogate because empirical optimal decisions can vary substantially from expected optimal decisions. To understand the impact of this deficiency, we evaluate the effect of aleatoric and epistemic uncertainty on the accuracy of empirical regret as a surrogate. Next, we propose three novel loss functions that approximate expected regret more robustly. Experimental results show that training two state-of-the-art decision-focused learning approaches using robust regret losses improves test–sample empirical regret in general while keeping computational time equivalent relative to the number of training epochs.

ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are mostly limited to MLPs. Graph neural networks (GNNs) can learn from non-euclidean data structures such as molecular structures efficiently and are thus highly relevant to computer-aided molecular design (CAMD). We propose a bilinear formulation for ReLU Graph Convolutional Neural Networks and a MILP formulation for ReLU GraphSAGE models. These formulations enable solving optimisation problems with trained GNNs embedded to global optimality. We apply our optimisation approach to an illustrative CAMD case study where the formulations of the trained GNNs are used to design molecules with optimal boiling points.

Delftse Foundations of Computation is a textbook for a one quarter introductory course in theoretical computer science. It includes topics from propositional and predicate logic, proof techniques, set theory and the theory of computation, along with practical applications to computer science. It has no prerequisites other than a general familiarity with computer programming.

Towards integrating renewable electricity generation sources into the grid, an important facilitator is the energy flexibility provided by buildings' thermal inertia. Most of the existing research follows a single-step price- or incentive-based scheme for unlocking the flexibility potential of buildings. In contrast, this paper proposes a novel two-step design approach for better harnessing buildings' energy flexibility. In a first step, a robust optimization model is formulated for assessing the energy flexibility of buildings in the presence of uncertain predictions of external conditions, such as ambient temperature, solar irradiation, etc. In a second step, energy flexibility is activated in response to a feasible demand response (DR) request from grid operators without violating indoor temperature constraints, even in the presence of uncertain external conditions. The proposed approach is tested on a high-fidelity Modelica simulator to evaluate its effectiveness. Simulation results show that, compared with price-based demand-side management, the proposed approach achieves greater energy reduction during peak hours.

Social commitments are recognized as an abstraction that enables flexible coordination between autonomous agents. We make these contributions. First, we introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by a maintenance condition whose truthhood an agent commits to maintain. This concept of maintenance commitments enables us to capture a richer variety of real-world scenarios than possible using achievement commitments with a temporal condition. Second, we develop a rule-based operational semantics, by which we study the relationship between agents' achievement and maintenance goals, achievement commitments, and maintenance commitments. Third, we motivate a notion of coherence between an agents' achievement and maintenance cognitive and social constructs, and prove that, under specified conditions, the goals and commitments of both rational agents individually and of a multiagent system altogether are coherent. Fourth, we illustrate our approach with a detailed real-world scenario from an aerospace aftermarket domain.

Industrial and academic interest converge on scheduling flow shops with sequence- and time-dependent maintenance. We posit that anticipatory, integrated scheduling of operational and maintenance tasks leads to superior performance to purely 'wait-then-fix' handling of the maintenance tasks. Motivated by an industrial problem with (sequence dependent) setup times, maximum separation constraints, and a combination of sequence- and time- dependent maintenance tasks, this paper introduces an integer programming solution, a constraint programming solution and a heuristic solution based on list scheduling. The motivating use case provides a unique combination of concerns that is to the best of our knowledge, not yet studied in the literature. We build on existing work where we can by extending models for sequence-dependent maintenance scheduling to accommodate sequence- and time-dependent maintenance scheduling and also propose other new models. We show the relative performances of our methods through empirical evaluations and also show significant improvements - up to 25% reduction in makespan - when compared to a reactive scheduling approach that does not consider maintenance in its planning. Based on our evaluations on exact methods, constraint programming models scale better than mixed integer programming models for this problem.

Surrogate modelling techniques such as Kriging are a popular means for cheaply emulating the response of expensive Computational Fluid Dynamics (CFD) simulations. These surrogate models are often used for exploring a parameterised design space and identifying optimal designs. Multi-fidelity Kriging extends the methodology to incorporate data of variable accuracy and costs to create a more effective surrogate. This work recognises that the grid convergence property of CFD solvers is currently an unused source of information and presents a novel method that, by leveraging the data structure implied by grid convergence, could further improve the performance of the surrogate model and the corresponding optimisation process. Grid convergence states that the simulation solution converges to the true simulation solution as the numerical grid is refined. The proposed method is tested with realistic multi-fidelity data acquired with CFD simulations. The performance of the surrogate model is comparable to an existing method, and likely more robust. More research is needed to explore the full potential of the proposed method. Code has been made available online at https://github.com/robertwenink/MFK-Extrapolation.

Current state-of-the-art airline planning models face computational limitations, restricting the operational applicability to problems of representative sizes. This is particularly the case when considering the uncertainty necessarily associated with the long-term plan of an aircraft fleet. Considering the growing interest in the application of machine learning techniques to operations research problems, this article investigates the applicability of these techniques for airline planning. Specifically, an Advantage Actor–Critic (A2C) reinforcement learning algorithm is developed for the airline fleet planning problem. The increased computational efficiency of using an A2C agent allows us to consider real-world-sized problems and account for highly-volatile uncertainty in demand and fuel price. The result is a multi-stage probabilistic fleet plan describing the evolution of the fleet according to a large set of future scenarios. The A2C algorithm is found to outperform a deterministic model and a deep Q-network algorithm. The relative performance of the A2C increases as more complexity is added to the problem. Further, the A2C algorithm can compute a multi-stage fleet planning solution within a few seconds

The efficacy of robust optimal control with adjustable uncertainty sets is verified in several domains under the perfect state information setting. This paper investigates constrained robust optimal control for linear systems with linear cost functions subject to uncertain disturbances and state measurement errors that are both residing in adjustable uncertainty sets. We first show that the class of affine feedback policies of state measurements are equivalent to the class of affine feedback policies of estimated disturbances in terms of their conservativeness. Then, we formulate and solve a robust optimal control problem with adjustable uncertainty sets by considering the disturbance feedback policies. In contrast to the conventional robust optimal control, where uncertainty sets are fixed and known a priori, the uncertainty sets themselves are regarded as decision variables in our design. In particular, given the metrics for evaluating the optimal size/shape of the polyhedral uncertainty sets, a bilinear optimization problem is formulated to decide the optimal size/shape of uncertainty sets and a corresponding optimal control policy to robustly guarantee that the system will respect its constraints for all admissible uncertainties. In addition, we introduce a convex approximation for the proposed scheme to provide a computationally efficient inner approximation of the original problem. The proposed scheme is illustrated by numerical simulation of a building temperature control problem to demonstrate its effectiveness.