Fishery management researchers use Artelys Knitro to develop age-structured fishing models incorporating complex factors and integrating ecological and economic aspects. The outcomes yield a high-performing nonlinear stochastic model, facilitating the identification of the optimal balance between economic profitability and ecological considerations.
Fishery ecologists have found that fishing acts as a powerful selective force causing age class truncation effects that destabilize fish stocks and increase the risk of population collapse. It has been shown that harvesting activities cause age class truncation effects with potentially adverse consequences on population recruitment, growth, and stability. This leads to the question of how to control the catch pressure over the population age classes.
The choice of fishing gear is the prime method for achieving this, especially the mesh size in gillnet and trawl fisheries. Including these factors into economic analyses is particularly complex. Age-structured fishery models which include endogenous harvesting selectivity and stochastic recruitment capture both economic (harvesting costs) and ecological (age-specific survival rates) aspects of fishery.
The resulting challenging nonlinear stochastic model can be solved efficiently with Artelys Knitro, allowing to maximize harvesting profitability while also including a large number of ecological considerations.
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 been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.
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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.
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