Sustainable design and operation of cellulosic bioelectricity supply chain networks with life cycle economic, environmental, and social optimization

In this work, we propose a novel multiobjective optimization model for the sustainable design and operation of bioelectricity supply chain networks, which simultaneously accounts for the associated economic, environmental, and social impacts. The proposed model covers the cradle-to-gate life cycle of bioelectricity including biomass cultivation and harvesting, feedstock pretreatment, energy conversion, and biopower generation, as well as transportation and storage. We formulate the problem as a multiobjective mixed-integer linear fractional programming (MILFP) problem following the functional-unit-based life cycle optimization approach. The geographical dispersion and seasonality of biomass supply are captured and handled by the spatially explicit and multiperiod features of the model. The multiobjective optimization is accomplished via the ε-constraint method to obtain the approximate Pareto frontiers, which reveal the trade-off between economic performance and concerns about environmental and social impacts. Tailored solution methods are proposed for the effective global optimization of the resulting MILFP problem. An illustrative example and a county-level case study on the potential bioelectricity supply chain in the state of Illinois are provided to demonstrate the application of both the modeling framework and solution methods.