The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain o...The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming solvers.In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.展开更多
The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for i...The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet.In practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power con-sumption.Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available.In this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging algorithm.We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models.Each charging process includes the time series of charging power.After pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in total.We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of 0.06.Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge profiles.Furthermore,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions.However,charging features are required including the number of phases used for charging.展开更多
文摘The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming solvers.In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.
文摘The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet.In practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power con-sumption.Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available.In this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging algorithm.We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models.Each charging process includes the time series of charging power.After pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in total.We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of 0.06.Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge profiles.Furthermore,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions.However,charging features are required including the number of phases used for charging.
