Large-scale hydrogen production and transportation for power-to-power supply chain has gained in interest in the context of energy decarbonisation. Hydrogen can be carried in vessels, in various forms: liquefied hydrogen, ammonia, liquid organic carrier, among others. The production and the transport impact each other, and choices of technologies, sizes and control have to be optimized together. In this work, we present a MILP model of batch transportation included within a global MILP model which encompasses several appliances functioning together: a renewable electricity production, an electrolyser, energy and gas storages, ammonia as hydrogen carrier and a gas turbine. The MILP model optimizes sizing and control of the complete hydrogen supply chain, however it is costly to compute. We propose and combine two methods to reduce the number of integer variables for batch transportation: the template method and the time aggregation applied to transportation. The combination of these two methods reduces the computation time by 330 without losing results quality (gap lower than 0.7%). This computation time reduction enables to perform bi-objective optimization. A parametric study on carbon emissions and bi-objective optimization with an external loop to the MILP model are performed and compared in terms of total costs of the supply chain, carbon emissions and computation time.