Md
M.B.J. de Goede
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Robust Optimization of Heavy Goods Electric Vehicle Fleet Planning
CertERoute: A Framework for Routing and Charging Scheduling under Time and Energy Consumption Uncertainty
The shift towards zero-emission transport has driven rapid adoption of Heavy Goods Electric Vehicles (HGEVs) across Europe. This trend is also evident in the Netherlands, where their numbers have grown exponentially over the past six years. The integration of HGEVs into fleet operations introduces new challenges for fleet planning due to limited battery ranges and significant operational uncertainties in both the time and energy domains.
To address these challenges, this thesis introduces CertERoute, an Adaptive Robust Optimization (ARO) framework that allows for joint optimization of routing and charging scheduling under time and energy consumption uncertainty. It incorporates both depot and en-route charging while accounting for charger availability and other realistic operational constraints. Time-related uncertainties in service, waiting, travel, and charging durations are modelled using uncertainty sets, which require minimal assumptions about the underlying probability distributions. Energy consumption is modelled as a function of time-domain uncertainty, with environmental and vehicle-specific energy uncertainty factors accounted for through Monte Carlo Simulation (MCS). The adaptive design of the framework supports a two-stage decision process where routing and charger visits are planned in advance to hedge against worst-case scenarios, while charging amount and timing decisions remain flexible during route execution to avoid overly conservative and costly solutions.
To ensure computational tractability, the framework employs a Column and Constraint Generation (CCG) approximation method enhanced by a novel One-Step Look-ahead Pessimization (OSLP) algorithm, which selectively integrates only provably infeasible scenarios into the optimization problem. Despite its theoretical vulnerability to premature convergence, this algorithm empirically produces highly robust solutions. To improve scalability to larger instances, a multi-scenario Adaptive Large Neighbourhood Search (ALNS) metaheuristic is developed, integrating charging and timing decisions into neighbourhood generation to enable more informed solution exploration.
The framework is evaluated using representative yet synthetic European HGEV planning scenarios. The results highlight the scalability of the proposed solution methods and demonstrate that the resulting plans are highly robust under operational uncertainty. As a result, this work offers a practical foundation for integrated fleet and energy management systems and supports Shell eMobility’s strategic goal of enabling sustainable, cost-efficient transport through offering intelligent charging solutions. ...
To address these challenges, this thesis introduces CertERoute, an Adaptive Robust Optimization (ARO) framework that allows for joint optimization of routing and charging scheduling under time and energy consumption uncertainty. It incorporates both depot and en-route charging while accounting for charger availability and other realistic operational constraints. Time-related uncertainties in service, waiting, travel, and charging durations are modelled using uncertainty sets, which require minimal assumptions about the underlying probability distributions. Energy consumption is modelled as a function of time-domain uncertainty, with environmental and vehicle-specific energy uncertainty factors accounted for through Monte Carlo Simulation (MCS). The adaptive design of the framework supports a two-stage decision process where routing and charger visits are planned in advance to hedge against worst-case scenarios, while charging amount and timing decisions remain flexible during route execution to avoid overly conservative and costly solutions.
To ensure computational tractability, the framework employs a Column and Constraint Generation (CCG) approximation method enhanced by a novel One-Step Look-ahead Pessimization (OSLP) algorithm, which selectively integrates only provably infeasible scenarios into the optimization problem. Despite its theoretical vulnerability to premature convergence, this algorithm empirically produces highly robust solutions. To improve scalability to larger instances, a multi-scenario Adaptive Large Neighbourhood Search (ALNS) metaheuristic is developed, integrating charging and timing decisions into neighbourhood generation to enable more informed solution exploration.
The framework is evaluated using representative yet synthetic European HGEV planning scenarios. The results highlight the scalability of the proposed solution methods and demonstrate that the resulting plans are highly robust under operational uncertainty. As a result, this work offers a practical foundation for integrated fleet and energy management systems and supports Shell eMobility’s strategic goal of enabling sustainable, cost-efficient transport through offering intelligent charging solutions. ...
The shift towards zero-emission transport has driven rapid adoption of Heavy Goods Electric Vehicles (HGEVs) across Europe. This trend is also evident in the Netherlands, where their numbers have grown exponentially over the past six years. The integration of HGEVs into fleet operations introduces new challenges for fleet planning due to limited battery ranges and significant operational uncertainties in both the time and energy domains.
To address these challenges, this thesis introduces CertERoute, an Adaptive Robust Optimization (ARO) framework that allows for joint optimization of routing and charging scheduling under time and energy consumption uncertainty. It incorporates both depot and en-route charging while accounting for charger availability and other realistic operational constraints. Time-related uncertainties in service, waiting, travel, and charging durations are modelled using uncertainty sets, which require minimal assumptions about the underlying probability distributions. Energy consumption is modelled as a function of time-domain uncertainty, with environmental and vehicle-specific energy uncertainty factors accounted for through Monte Carlo Simulation (MCS). The adaptive design of the framework supports a two-stage decision process where routing and charger visits are planned in advance to hedge against worst-case scenarios, while charging amount and timing decisions remain flexible during route execution to avoid overly conservative and costly solutions.
To ensure computational tractability, the framework employs a Column and Constraint Generation (CCG) approximation method enhanced by a novel One-Step Look-ahead Pessimization (OSLP) algorithm, which selectively integrates only provably infeasible scenarios into the optimization problem. Despite its theoretical vulnerability to premature convergence, this algorithm empirically produces highly robust solutions. To improve scalability to larger instances, a multi-scenario Adaptive Large Neighbourhood Search (ALNS) metaheuristic is developed, integrating charging and timing decisions into neighbourhood generation to enable more informed solution exploration.
The framework is evaluated using representative yet synthetic European HGEV planning scenarios. The results highlight the scalability of the proposed solution methods and demonstrate that the resulting plans are highly robust under operational uncertainty. As a result, this work offers a practical foundation for integrated fleet and energy management systems and supports Shell eMobility’s strategic goal of enabling sustainable, cost-efficient transport through offering intelligent charging solutions.
To address these challenges, this thesis introduces CertERoute, an Adaptive Robust Optimization (ARO) framework that allows for joint optimization of routing and charging scheduling under time and energy consumption uncertainty. It incorporates both depot and en-route charging while accounting for charger availability and other realistic operational constraints. Time-related uncertainties in service, waiting, travel, and charging durations are modelled using uncertainty sets, which require minimal assumptions about the underlying probability distributions. Energy consumption is modelled as a function of time-domain uncertainty, with environmental and vehicle-specific energy uncertainty factors accounted for through Monte Carlo Simulation (MCS). The adaptive design of the framework supports a two-stage decision process where routing and charger visits are planned in advance to hedge against worst-case scenarios, while charging amount and timing decisions remain flexible during route execution to avoid overly conservative and costly solutions.
To ensure computational tractability, the framework employs a Column and Constraint Generation (CCG) approximation method enhanced by a novel One-Step Look-ahead Pessimization (OSLP) algorithm, which selectively integrates only provably infeasible scenarios into the optimization problem. Despite its theoretical vulnerability to premature convergence, this algorithm empirically produces highly robust solutions. To improve scalability to larger instances, a multi-scenario Adaptive Large Neighbourhood Search (ALNS) metaheuristic is developed, integrating charging and timing decisions into neighbourhood generation to enable more informed solution exploration.
The framework is evaluated using representative yet synthetic European HGEV planning scenarios. The results highlight the scalability of the proposed solution methods and demonstrate that the resulting plans are highly robust under operational uncertainty. As a result, this work offers a practical foundation for integrated fleet and energy management systems and supports Shell eMobility’s strategic goal of enabling sustainable, cost-efficient transport through offering intelligent charging solutions.
Training diffusion models with federated learning
A communication-efficient model for cross-silo federated image generation
The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Probabilistic Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training, compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score.
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The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to the lack of transparency regarding training data. Hence, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Probabilistic Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training, compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score.