Data on supply chains is often sparse due to reluctance among actors to share their data, making supply chain simulation modeling difficult. As a result, supply chain simulation models suffer from parametric and structural uncertainties, and there is a large variety of plausible simulation models that would align with the sparse observations about the real-world supply chain. Constructing a diverse set of models that fit sparse data is not an easy task. A relatively unknown approach to generating this diverse set of plausible models is the Quality Diversity (QD) algorithm. This study evaluates the feasibility of using QD to generate a diverse ensemble of supply chain simulation models for a varying degree of data sparseness. The results show that QD is able to generate a diverse ensemble of supply chain models, including the ground truth. As expected, QD successfully identifies the structure of the ground truth most frequently for a low level of data sparseness. When the sparseness of the data increases, QD is prone to overfitting, identifying supply chain structures that are more complex than the ground truth. Further research should focus on reviewing the calibration metric for sparse data, to reduce the overfitting of complex network structures.

One of the tasks of police is catching fleeing suspects, where the police interception positions depend on the fleeing suspect’s route choices. Various conceptualizations of route choice decision-making of fleeing suspects exist. However, we do not know the effects of these different models of fugitive behavior on the calculated police interception strategy. Therefore, we operationalize two models of route choice and implement these in a simulation. Police interception strategies are obtained by optimization. The resulting sets of routes and the calculated police interception positions are subsequently compared and interpreted. The experiments show that the different route-choice models result in different escape routes and, therefore, different calculated police interception positions. The differences are larger when the road network is complex and contains non-uniform obstacles. In other words, the robustness of the calculated police interception positions for each model largely depends on the network topology.

The police control room determines where to send available police units to intercept a fleeing fugitive. Models can support the police with decision-making for fugitive interception. The police have, at most, a few minutes to determine an interception strategy. Therefore, a timely calculation of the interception positions is essential to support police interception operations. The number of nodes in the network, each being a crossing where routes of the fleeing suspect can split, greatly contributes to the computation time. Graph coarsening is a promising approach to reduce the complexity of the network, and therefore the computation time. We compare four graph coarsening algorithms on five road networks and assess their impact on computation time and solution quality for the fugitive interception problem. Based on the comparison, we propose and test a new method specifically for fugitive interception. This method, Search Space Representation, improves the quality of the best solutions obtained by the optimization algorithm with up to 12%, improves the reliability of the optimization to find high-quality solutions, and decreases the number of function evaluations required to obtain high-quality solutions to 5000–10,000 depending on the size and complexity of the road network, which is feasible for real-time decision-making. Search Space Representation can be applied to reduce the computation time of other network-based optimization problems.

Urban vulnerability is affected by changing patterns of hazards due to climate change, increasing inequalities, rapid urban growth and inadequate infrastructure. While we have a relatively good understanding of how urban vulnerability changes in space, we know relatively little about the temporal dynamics of urban vulnerability. This paper presents a framework to assess urban vulnerability over time and space to address this gap. We apply the framework to Amsterdam, Rotterdam, and The Hague, the Netherlands. Using high-resolution, anonymised ambulance calls and socio-economic, built environment, and proximity data, we identify three temporal patterns: ’Midday Peaks’, ’Early Birds’, and ’All-Day All-Night’. Each pattern represents a unique rhythm of risk arising from the interaction of people with diverse demographic and socio-economic backgrounds and the temporal flow of their daily activities within various urban environments. Our findings also highlight the polycentric nature of modern Dutch cities, where similar rhythms emerge in areas with varying population densities. Through these case studies, we demonstrate that our framework uncovers the spatio-temporal dynamics of urban vulnerability. These insights suggest that a more nuanced approach is necessary for assessing urban vulnerability and enhancing preparedness efforts.

Schedule design in the transportation and logistics sector is a widely studied problem. Transport service providers, such as the train industry and aviation, aim for schedules to be on-time according to the planning (i.e., on-time performance or OTP) in order to increase the service level by ensuring that passengers actually make their connections and to reduce costs. Transportation services also aim for schedules that serve a high variety of destinations and frequency of connections (i.e., connectivity). OTP and connectivity are both highly dependent on buffer time: more lucrative connections can often be offered by reducing the buffer time in the schedule, while more delay can be absorbed by more buffer time. Given strict constraints on the minimum turnaround time of aircraft and minimum (and maximum acceptable) transfer times of passengers, assigning buffer time in an already tightly planned schedule to optimize OTP and connectivity simultaneously is a big challenge. This research presents a novel multi-objective formulation of a daily flight schedule where buffer scheduling is used to ensure the optimal balance between OTP of the schedule and the passenger connections as connectivity, given the tight restrictions. This problem formulation is solved using a simulation–optimization framework. Specifically, we use the Multi-Objective Evolutionary Algorithm (MOEA) BORG. As a proof of concept, a daily European flight schedule of a large international airline is optimized on both OTP and connectivity. The results demonstrate that the presented multi-objective formulation and associated solving through simulation–optimization can result in candidate schedules with both better on-time performance and a higher connectivity.

Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception. The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time. We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.

Illicit supply chains for products like counterfeit Personal Protective Equipment (PPE) are characterized by sparse data and great uncertainty about the operational and logistical structure, making criminal activities largely invisible to law enforcement and challenging to intervene in. Simulation is a way to get insight into the behavior of complex systems, using calibration to tune model parameters to match its real-world counterpart. Calibration methods for simulation models of illicit supply chains should work with sparse data, while also tuning the structure of the simulation model. Thus, this study addresses the question: “To what extent can various model calibration techniques reconstruct the underlying structure of an illicit supply chain when varying the degree of data sparseness?” We evaluate the quality-of-fit of a reference technique, Powell's Method, and three model calibration techniques that have shown promise for sparse data: Approximate Bayesian Computing, Bayesian Optimization, and Genetic Algorithms. For this, we use a simulation model of a stylized counterfeit PPE supply chain as ground truth. We extract data from this ground truth and systematically vary its sparseness. We parameterize structural uncertainty using System Entity Structure. The results demonstrate that Bayesian Optimization and Genetic Algorithms are suitable for reconstructing the underlying structure of an illicit supply chain for a varying degree of data sparseness. Both techniques identify a diverse set of optimal solutions that fit with the sparse data. For a comprehensive understanding of illicit supply chain structures, we propose to combine the results of the two techniques. Future research should focus on developing a combined algorithm and incorporating solution diversity.

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review of supply chain visibility and data quality literature, this study proposes to characterize data sparseness as a lack of data quality across the entire supply chain, where data sparseness can be classified into three dimensions: noise, bias, and missing values. The quantitative analysis relies on a stylized simulation model of a moderately complex illicit supply chain. Scenarios are used to evaluate the combined effect of the individual dimensions from actors with different perspectives in the supply chain, either supply or demand-oriented. Results show that when a data sparseness of 90% is applied, supply chain visibility reduces to 52% for noise, to 65% for bias, and to 32% for missing values. The scenarios also show that companies with a supply-oriented view typically have a higher supply chain visibility than those with a demand-oriented view. The classification and assessment offer valuable insights for improving data quality and for enhancing supply chain visibility.

Integrated Assessment Models (IAMs) vary widely in complexity and underlying assumptions. There have been considerable efforts to increase the complexity of IAMs for improved representation of socioeconomic and environmental outcomes. However, less attention has been given to the foundational assumptions of these models and their distributional consequences. These assumptions are fraught with deep and normative uncertainty and can significantly impact IAM projections. If these assumptions are not explicit, IAMs can perpetuate existing mistakes and exacerbate inequalities due to their black-box nature. This paper introduces a novel IAM called JUSTICE (Justice Universality Spatial Temporal Integrated Climate Economy) to explore the influence on distributive justice outcomes due to underlying modelling assumptions across model components and functions: the economy and climate components, and the damage and social welfare functions. JUSTICE is a simple IAM inspired by the long-established RICE and is designed to be a surrogate for more complex IAMs for eliciting normative insights. As illustrated in Figure 1, JUSTICE contains two distinct economic and climate sub-models, three damage functions, and four social welfare functions (SWFs), each based on fundamentally different assumptions. This modular structure enables JUSTICE to uncover assumptions with nontrivial normative and distributional consequences. Also, the simplicity of JUSTICE makes it suitable for assessing the consequences of these modelling assumptions under deep and normative uncertainty using MS-MORDM and EMODPS frameworks, promoting a more equitable approach to decision-making. Using JUSTICE, we investigate the effects of three SWFs—Utilitarianism, Egalitarianism, and Prioritarianism—on global temperature rise, with two levels of aggregation. We also explore the sensitivity of distributional outcomes for two different climate models. Our findings reveal that different assumptions lead to significantly distinct optimal abatement pathways, underscoring the importance of explicating assumptions and exploring their uncertainties to facilitate deliberation and identify common ground among policymakers with diverse perspectives.

Model-based predictive signal control is a popular method to pro-actively control traffic and to reduce the effects of congestion in urban networks. In combination with structure-free controllers, which adapt signal settings in arbitrary order and combination (no imposed cycles), these predictive control methods have a high potential to increase system performance by adapting to individual vehicle patterns, which are increasingly available due to new technology. However, most of these control methods assume perfect predictions, while in practice there are prediction errors due to various reasons. In this paper, the sensitivity of the system performance to these prediction errors is analyzed, for an urban corridor with spillback. In a microscopic simulator, first the ideal world is created for the structure-free model-based predictive signal controller, in which perfect predictions are made and the controller can reach its optimal performance. Then prediction errors are introduced in this perfect world, distinguished in aggregation errors that arise using a macroscopic prediction model and biases that represent structural errors in the prediction model or in its demand and state input. The effects of these prediction errors on the system performance are analyzed, as a function of the prediction horizon and update frequency of the control system. The results show that, even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length. A high update frequency dampens the influence of prediction errors, enabling the structure-free controller to correct mistakes faster. However, there remains a significant performance loss due to aggregation errors and biases in the prediction model, indicating a promising performance gain of more reliable predictions and the incorporation of information on individual vehicles in future control applications. Moreover, for all model quantities one direction of the bias has more impact on the system performance than the other direction, indicating guidelines towards a more robust control system that suffers less from erroneous predictions.