Open Access
Development of optimisation model for black soldier fly-based aquaculture feed supply chains in Malaysia
Author(s) -
Chilton Ng,
Claire Chan,
Viknesh Andiappan,
Law Yong Ng,
Denny K. S. Ng
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1195/1/012049
Subject(s) - aquaculture , supply chain , biomass (ecology) , agriculture , raw material , palm oil , environmental science , agricultural engineering , palm , business , pulp and paper industry , agricultural science , fish <actinopterygii> , fishery , agronomy , engineering , biology , ecology , physics , marketing , quantum mechanics
Aquaculture is identified as one of the critical food supplies in Malaysia. Due to the increasing demand for aquaculture products, the demand for protein sources for fish feed is also increased accordingly. Black soldier fly larvae is identified as one of the main protein sources that can be used in fish feed. Such larvae can be grown using different types of organic materials, such as food waste, agriculture waste, etc. As Malaysia is the second-largest palm oil producer in the world, therefore, a large number of agricultural wastes, also known as palm-based biomass (e.g., empty fruit bunches, mesocarp fibre, decanter cake, etc.) are generated annually. Based on the current industry practise, palm-based biomass can be converted into value-added products. However, using palm-based biomass as feedback to grow black soldier fly larvae is a relatively recent discovery. Thus, a viable supply chain model has yet to be established. In this work, a mathematical optimisation model is developed via commercial optimisation software (Lingo v. 16) to synthesise an optimum black soldier fly-based aquaculture feed supply chain that utilised palm–based biomass as the feedstock. Based on the optimised result, the annual operating cost of the aquaculture feed supply chain is estimated as RM 5.2 million.