Wild and semi-domesticated reindeer are one of the key species in Arctic and subarctic areas, and reindeer husbandry is an integral part of the social and economic life of many northern indigenous and local communities. Reindeer population dynamics are closely tied to winter conditions.
By combining a state-of-the-art bioeconomic model and practitioner knowledge, the authors aim at studying how the variation of winter conditions and potential increase of this variation due to climate change affect the economic sustainability of reindeer husbandry.
Artelys Knitro is leveraged to conduct the analysis, allowing to conclude that the increase of variation in winter conditions will negatively affect the reindeer herding economy, even if the harshness of winter conditions during difficult winters does not change. In addition, the study shows that scientific and practical knowledge can be combined and mutual validate each other, offering compatible insight, ideas, and understanding that can be accepted by researchers and local communities.
Start with a tutorial!
You’re not familiar with nonlinear optimization? This tutorial will present some examples of nonlinear problems for various applications. You will discover nonlinear programming methods using the Artelys Knitro solver in a Python notebook, through different examples.
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Artelys Knitro has unmatched performance
Artelys Knitro has been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.
The Artelys technical support team comprises Artelys’consultants (PhD-level) who are used to solving the most difficult problems and deploying enterprise-wide optimization solutions. They can advise on algorithmic or software features that may result in enhanced performance in your usage of Artelys Knitro.
The development team works continuously to provide two releases of Artelys Knitro every year. Based on feedback, we always improve our solver to meet users’ requirements and need to solve larger models faster.