Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed–Centralized Processing Networks

TLDR

The BTL supply chain comprises multisite distributed–centralized processing networks for biomass conversion and liquid fuel production. The study proposes a multiobjective, multiperiod mixed‑integer linear programming model to optimally design and plan biomass‑to‑liquids supply chains under economic and environmental criteria. The authors formulate a bicriterion mixed‑integer linear programming model that minimizes annualized cost while reducing life‑cycle GHG emissions, simultaneously determining network design, facility locations, technology choices, capital investment, production planning, inventory control, and logistics, and solve it with the ε‑constraint method, illustrated by an Iowa case study. The Pareto‑optimal curve shows how annualized cost and network structure vary with environmental performance, illustrating trade‑offs in the supply chain.

Abstract

This paper addresses the optimal design and planning of biomass-to-liquids (BTL) supply chains under economic and environmental criteria. The supply chain consists of multisite distributed–centralized processing networks for biomass conversion and liquid transportation fuel production. The economic objective is measured by the total annualized cost, and the measure of environmental performance is the life cycle greenhouse gas emissions. A multiobjective, multiperiod, mixed-integer linear programming model is proposed that takes into account diverse conversion pathways and technologies, feedstock seasonality, geographical diversity, biomass degradation, infrastructure compatibility, demand distribution, and government incentives. The model simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The problem is formulated as a bicriterion optimization model and solved with the ε-constraint method. The resulting Pareto-optimal curve reveals how the optimal annualized cost and the BTL processing network structure change with different environmental performances of the supply chain. The proposed approach is illustrated through a county-level case study for the state of Iowa.