Green Supply Chain Optimization Based on Two-Stage Heuristic Algorithm

Abstract

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits.