Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand

Abstract

Biomass electricity supply chains are prone to a range of uncertainties that emanate internally or externally. Therefore, design of robust supply chains that remain resilient in the face of disruptions becomes of important interest. This study presents a two-stage stochastic mixed integer non-linear programming (MINLP) model combined with chance constraint to minimize the total cost of producing electricity from woody biomass in a four-level integrated bioenergy supply chain. The strategic decisions such as location and capacity of infrastructure are incorporated into the first stage, and decisions for sourcing the materials, inventory policy and are included in the second-stage. The proposed model considers detailed climate factors such as temperature and dew point, which are highly influential on moisture content, higher heating value, energy values, and ultimately, the performance of bioenergy supply chain. Given the NP-hard nature of the problem, hybrid metaheuristic approach is proposed that benefits from genetic algorithm (GA) operators and chaos theory in the structure of an improved cross entropy (CE) algorithm. Compared to the results of exact solution, the proposed ICE performs better compared to conventional CE by 5.6%.