A multi-stage multi-criteria dynamic decision-making framework for fishing route planning and optimization

Abstract

Fishing is a main activity in the Barents Sea. Improving fishing vessel trip planning is a feasible way to improve fishing efficiency, profitability, sustainability and at the same time reducing accidental risk from hazardous weather conditions and reducing environmental impact by reducing fuel consumption. Today, fishing trip planning and route optimization is all up to skippers`s experiences. Determine where and when to go for fishing is a challenge. This paper presents a multi-stage multicriteria dynamic route planning and optimization framework for pelagic fishing in the arctic. Developing such algorithms will allow using available data in decision-making for more efficient fishing. At the same time, the algorithms can be used to transmit the empiric knowledge of the skippers to the new generations. Users can customize their objectives when using the proposed fishing route planning and optimization model. The route planning and optimization model uses graph theory, paretooptimal and genetic algorithms to find a set of optimal routes which contains a set of sequential fishing grounds to visit and fishing factory to deliver the catch, a combination of speed to sail between locations and a combination of duration to stop at each location. Dynamic routing is used to determine next action after finding the optimal routes so that options are open to opportunities and information in future. Skippers can use this as a support tool for their fishing trip planning and route optimization. At the same time, the algorithms can be tuned and improved through daily application and feedback to form a rich body of knowledge.