Aquaculture production optimisation in multi-cage farms subject to commercial and operational constraints - Luna, Manuel; Llorente, Ignacio; Cobo, Angel (Biosystems engineering, 2020)
The new advances in production methods have led to an increase in aquaculture production to the extent that the industry can now aid traditional fishing in meeting the growing global demand for fish within the context of the depletion of fisheries' resources. In this new context, market competition has increased and the complexity of managing industrial-scale production processes involving biological systems is still a growing problem. This has also led, in many cases, to a lack of management capacity that increases when it comes to setting long-term strategic plans. This study presents a methodology that aims to help aquaculture managers in decision making. It integrates a multi-criteria model and a Particle Swarm Optimisation (PSO) technique in order to provide a production strategy that optimises the value of multiple objectives at a fish farm with multiple cages, batches, feeding alternatives and products. This multi-criteria approach takes into account not only the effect of biological performance on economic profitability, but also the effect on environmental sustainability and aspects of product quality. In addition, it enables consideration of new operational and commercial constraints, such as the maximum volume of fish harvested per week, based on labour and marketing constraints, or the minimum necessary volume of fish harvested on specific dates to comply with commercial agreements. Results obtained demonstrate the utility of this novel approach to decision-making optimisation in aquaculture both when establishing overall strategic planning and when adopting new ways of producing. •Producer decisions and biological processes are highly related in aquaculture.•Decisions must be based on economic, environmental and product quality factors.•Sequencing the strategy of every cage and cycle is mandatory to increase efficiency.•New optimisation techniques have to be capable of handling large volumes of data.•Commercial and operational constraints can be integrated with good results.