Genetic algorithm based multi-objective optimization of wheat flour supply chain considering raw material substitution

The aim of this study was to develop multi-objective optimization model for wheat flour supply chain. The model was developed by considering raw material substitution with local flour. The local flour such as mocaf, tapioca, sweet potato, modified corn flour etc. can substitute a part or whole of wheat flour for wheat flour-based product application. However, raw material substitution can impact supply chain network, raw material supply policy, and product quality so that it is important to optimize supply chain for that case. In this work, we used mocaf as flour substitution for wheat flour in wheat flour mill. We developed multi-objective supply chain model that minimized total cost and maximized product quality. Genetic algorithm approach was used to solve the optimization problem. For numerical experiment, we used supply chain configuration consisting of three wheat suppliers, three mocaf suppliers, three wheat flour mills, four distribution centers, and two food factories.