An innovative approach to the supply-chain network optimization of biorefineries using metaheuristic techniques

Abstract

A novel formulation that can be used with metaheuristic techniques to optimize the biorefinery supply-chain network involving nonlinear cost functions is proposed. A repair operator is introduced to improve potential solutions that violate the flow rates and capacity constraints. It employs an implicit constraint handling technique to ensure the satisfaction of constraints involving mass balances. The proposed framework is demonstrated using six metaheuristic techniques in a biorefinery case study. It provides better solutions than the standard penalty function approach to handle constraints. Particle Swarm Optimization (PSO) was able to determine a solution with the least violation using the standard penalty approach. With the help of the proposed strategy, PSO determines a solution with 45% improvement in the total lifecycle cost. Among the six techniques, single-phase multi-group teaching–learning optimization was able to determine the best solution, with 18.4% improvement compared to Yin-Yang Pair Optimization.