Optimal operation policy for a sustainable recirculation aquaculture system for ornamental fish: Simulation and response surface methodology

Hadas Elalouf, Moshe Kaspi, Amir Elalouf, Ilan Halachmi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Recirculating aquaculture systems (RAS) require a large investment in construction, equipment and energy. To ensure sufficient return on these investments, RAS operations must be managed meticulously. RAS managers must consider numerous factors related to the biological traits of the fish, logistics, seasonal market demand, and livestock management. RAS that specialize in ornamental fish are faced with particular challenges in that a given species of fish may actually yield several different “products,” distinguished, for example, by color, which can be sold at different sizes for different prices. RAS managers need to consider the market prices of different-sized fish in the light of their production costs (cost of food and space, dependent on time in the system). The current study aims to develop an optimization model for the operations management of RAS specializing in ornamental fish. The objective of the model is to maximize annual profit. The methods used include: a general simulation model, built in Arena 11.0®, that seeks to mimic the studied system; an optimization procedure based on response surface methodology (RSM), including design of simulation experiments, stepwise regression (in SPSS® 11.0), and a nonlinear objective function and constraints solved with MATLAB®. The method is demonstrated in a case study—a RAS on Kibbutz Hazorea, Israel, raising ornamental koi fish (Cyprinus carpio).

Original languageEnglish
Pages (from-to)230-240
Number of pages11
JournalComputers and Operations Research
Volume89
DOIs
StatePublished - 1 Jan 2018

Keywords

  • Metamodel
  • Optimization
  • Recirculating aquaculture system (RAS)
  • Response surface methodology (RSM)
  • Simulation

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research

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