Hybrid Fuzzy AHP and Fuzzy TOPSIS Decision Model for Aquaculture Species Selection

Worldwide demand for fish products is increasing continuously. Literature evidence indicates that there is a persistent decrease in ocean fisheries' supply. Aquaculture bridges the gap between the reduced ocean fish supply and increased world fish food demand. Sustainable and profitable aquaculture is firmly facilitated by selective fish species. Species selection has been achieved through deeply analyzing the manifold and complex interrelationships between the numerous subjective risk categories intricate in aquaculture. Apparently an analytical system able to analyze massive subjective stakes in terms of its quantifiable equivalent that aids selecting an optimal fish species is nontrivial. This research provides quantifiable metrics that eases the analytical struggle towards the subjective aspect of species selection. The novelty involves providing hybrid multi-criteria-based viable decision support methodology that analyses extensive aquaculture domain knowledge and inference and emulates the logic and reasoning process so as to choose an optimal fish species for aqua farming. The methodology is based on the assessment of several species in accordance with analyzing numerous associated criteria and sub-criteria of risk factors. This research consists of nineteen sub-criteria which were classified under five comprehensive heads of evaluation criteria such as environmental, nutritional, disease outbreaks, biotic and physiological risk categories. The weight scores for each criteria and sub-criteria were determined using the Fuzzy Analytical Hierarchy Processing (FAHP) method. Consequently using the derived priority weights, the best fish species to choose from several alternative species is identified based on the relative closeness values and ranks assigned to them by applying the fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. A case study is performed on five varieties of most favored fish species for consumption to exemplify the effectiveness of the proposed model. The sturdiness of the suggested model is validated against the two standing multiple criteria decision-making approaches such as fuzzy ordered weighted average and fuzzy extent analysis-fuzzy weighted average methods. The research outcome strongly aids aqua farmers to identify an optimal fish species.

Automated summary

Global demand for fish products continues to increase, while the supply of ocean fisheries is decreasing. Aquaculture bridges this gap by selectively breeding fish species. This study provides a decision support method for selecting the optimal fish species based on comprehensive evaluation criteria and analyzing various risk factors. The method utilizes fuzzy analytic hierarchy processing to determine weight scores and applies fuzzy technique for order preference by similarity to the ideal solution to identify the best species. A case study validates the effectiveness of the proposed model. Overall, this research greatly aids aquaculture farmers in identifying the optimal fish species.