The existing supply chains for plant protein food will be redesigned for decentralization of processing operations, side stream valorization and closed loops formation. As plant-protein market is new and rapidly growing, the investments decisions under various market growth scenarios in size and in geography will be investigated. On top of that, the possible effects of seasonal differences in harvesting times and storage conditions of geographical dispersion in supply chains will be incorporated into making strategical and tactical decisions in network design. At last, the effects of climate change will be incorporated in the supply chain design for long-term robustness. Partner collaborations within VALPRO Path -which funds this PhD project- will the main source of data, but other databases will be utilized if necessary. Multi Objective Mixed Integer Programming will be the main modeling approach, but it will be extended to stochastic two-stage or multi-stage multi-period models as uncertainties and length of the planning horizon play more role. Pareto Efficiency will be observed for the economic objective -profits- and environmental objective -weighted sum of several environmental impact factors. The solutions of the models will be used to make not only decision making for the partner supply chains used in the case study but also inferences on the real-life system behavior.