P-Graph methodology for bi-objective optimization of bioenergy supply chains: economic and safety perspectives

Abstract

Widespread adoption of bioenergy for electricity generation could lead to a globally greener environment, with significant climatic and waste management benefits. Numerous methodologies have been developed to optimize the performance of bioenergy supply chains with different objective functions (e.g., profitability, carbon footprint, etc.) usually based on conventional optimization techniques. Such techniques are usually based on single-objective formulations, and are able to determine unique optimal solutions. Process graph (P-graph) is a graph-theoretic methodology which has been applied towards various network optimization problems in different domains. It offers advantages of computational efficiency for large-scale combinatorial problems, as well as the capability to identify both optimal and near-optimal solutions; the latter feature is especially good for decision-makers in practical applications. This paper presents an approach to the planning of bioenergy supply chains, taking into account both total cost minimization and supply chain risk reduction. The supply chain risk is accounted for in terms of transportation fatalities computed in an actuarial manner. An illustrative example based on Malaysian palm-based bioenergy supply chain is solved to illustrate the proposed approach.