Data-driven stochastic programming for energy storage system planning in high PV-penetrated distribution networkData-drivenDistributionally robust optimizationEnergy storage systemPlanningWasserstein distanceEnergy storage systems (ESSs) facilitate the reliable and economic operation of distribution systems with ...
Manage preferencesfor further information and to change your choices. Accept all cookies Abstract In this paper, we study data-driven chance constrained stochastic programs, or more specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a data-driven setting to provid...
Asymptotic consistency:AsNtends to infinity, the certificate\(\widehat{J}_N\)and the data-driven solution\(\widehat{x}_N\)converge—in a sense to be made precise below—to the optimal value\(J^\star \)and an optimizer\(x^\star \)of the stochastic program (1), respectively. (iii) ...
Data‐driven rolling horizon approachDynamic supply chain network designMulti‐stage stochastic programmingRisk managementWe address the dynamic design of supply chain networks in which the moments of demand distribution function are uncertain and facilities' availability is stochastic because of possible ...
Data-driven Chance Constrained Stochastic Program Ruiwei Jiang and Yongpei Guan Department of Industrial and Systems Engineering University of Florida, Gainesville, FL 32611, USA Email: rwjiang@ufl.edu; guan@ise.ufl.edu July 5, 2012 Abstract Chance constrained programming is an effective and...
Abundant ecosystem: There are vast collections of libraries and frameworks that address almost every aspect of programming in Python, like data (NumPy, Pandas), machine learning (TensorFlow, scikit-learn), and web development (Django, Flask). Community-driven support: A huge number of developers ...
Still, adding non-stationary dynamics to SELSP necessitates a control theory approach where the production planning and control decisions, e.g., lot size determination, cannot solely be handled within OR framework, e.g., through stochastic programming. Show abstract A supervised learning-driven ...
Comparison of key considerations for the major categories of device modeling approaches. The proposed two-tier Kriging approach can be classified as a data-driven device modeling approach. Full size image Since data-based models are generated directly from measured data without knowledge of the device...
You want to gain expertise in data-driven decision-making using data science and predictive analytics and use your spreadsheet skills. You want to easily work with data in SQL databases, data warehouses, public datasets, or Excel spreadsheets. You want to take the initiative and empower yourself...
The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to