Artelys Kalis 13: Boost the resolution of your allocation and scheduling problems!
This new version of Artelys Kalis returns first feasible solutions of allocation instances that are of better quality, in a lower amount of time. As an example, on Berth assignment problems, for which an operator allocates berth space for vessels in container terminals, the average time to find a first feasible solution has drastically decreased. Also, the quality of the initial solution, i.e. the time required to perform all port activities in our example, is better than in previous versions. This enhancement derives from a solver internal reformulation facilitating the modeling of allocation problems for the user!
The average performance improvement reaches 25% on a set of customer scheduling problems. This enhancement derives from a refactoring of the Timetable algorithm (computing the constraint propagation for available resources and scheduled tasks) in the solution tree search. The solver is more efficient and computes up to 30% more nodes within a given time limit. The optimal scheduling is found faster.
Other Artelys Kalis 13 features:
Improved Python interface to facilitate nonlinear constraint manipulation. Increased performances on nonlinear problems thanks to a more efficient standard function propagation. General improvements on memory usage in particular for “implies constraints” with gains of up to 50%.
Artelys Introduces Future Sight: a Visualisation Tool Supporting the Energy Transition
As partner in the European Climate + Energy Modeling Forum (ECEMF) – a Horizon 2020 Europe project uniting research institutes and leading energy modellers in Europe – Artelys has contributed to modeling activities powered by Artelys Crystal Super Grid modelisation tool, and has led the development of a fully-fledged visualisation tool.
Supercharging Optimisation: How Artelys, FICO and NVIDIA cuOpt Join Efforts to Scale Up Energy System Optimisation
As energy system models continue to scale—reaching up to hundreds of millions of variables and constraints—traditional CPU-based optimisation solvers are hitting performance and memory bottlenecks. These increasingly complex models are essential for planning Europe’s energy transition, yet solving them within realistic timeframes has become a pressing challenge.
Artelys is powering SEleNe CC’s Common Grid Model (CGM) Service
Artelys provides maintenance and support services of the calculation module used to perform the European Merging Function (EMF) at SEleNe CC
Improving electrical transformer production planning with artificial intelligence solutions
Improving high-voltage transformer production planning at the Hitachi Energy factory in Varennes with advanced artificial intelligence solutions.
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