Effective MILP and matheuristic for multi-echelon green supply chain operations and financing considering carbon emission reduction investment

Abstract

Carbon emission trading schemes have been widely implemented in many countries to achieve carbon peaking and carbon neutrality goals, significantly encouraging manufacturers to proactively invest in carbon emission reduction. The core manufacturer in green supply chain needs to comprehensively determine carbon emission reduction investments, raw material procurement, product production, transportation, distribution, and financing decisions by considering consumers’ green preferences and financial constraints. To effectively tackle this newly emerged practical decision-making problem, a mixed-integer linear programming (MILP) model with the objective of profit maximization is formulated. To address large-scale problems efficiently, a two-stage matheuristic algorithm (TSMA) is developed. Numerous test results indicate that TSMA significantly enhances solution efficiency and achieves high-quality solutions with gaps of less than 1.07%. The results confirm that carbon emission reduction investments and carbon pledge financing can simultaneously decrease manufacturers’ carbon emissions and improve the profitability of supply chains. Sensitivity analysis demonstrates that carbon quota prices and consumers’ green preferences positively impact profit and carbon emission reduction in green supply chains.