Stochastic Approach to Optimize the Supply Chain Network of Microalga-Derived Biodiesel under Uncertain Diesel Demand

Abstract

To investigate the effect of uncertainty in biodiesel production from microalgae, a supply chain network (SCN) of the process from cultivation of microalgae to distribution of biodiesel is developed using mixed-integer linear programming. Biodiesel demandis the crucial factor in the design of SCN, but is quite uncertain, so a stochastic approach that considers uncertain scenarios is developed, and its recommendations are compared to those of a deterministic model. Also, three different scenario-generation methodologies are employed to illustrate the applicability of the approach. The proposed model determines: (1) the numbers, locations, and sizes of a carbon capture and storage system; (2) the numbers, locations, sizes, and types of bio-refineries; (3) the transportation paths of CO, and water from feedstock fields to bio-refineries; and (4) the transportation paths of biodiesel from bio-refinery to demand cities, while minimizing the expected total cost considering several constraints such as locations of power plants as carbon sources, potential locations of bio-refineries, and demands for biodiesel at each site. The proposed model is validated by applying it to a case study based on the predicted biodiesel demand of Korea in the year 2030. A numerical example illustrates that the unit production cost of alga-derived biodiesel by the stochastic model (US$ 2.84 per liter) is at least 5% more economical than that of the deterministic model (US$ 2.99 per liter). The proposed approach is able to respond to uncertain demand situations in SCN design.