Multi-Objective Sustainable Supply Chain Network Planning Based on Proximity Optimization With Hybrid Genetic Algorithm Variable Neighborhood Search Strategy

With the continuous expansion of the global market and the change of consumer preferences, the market demand presents a trend of complexity and diversity, which requires the supply chain network to have a high degree of flexibility and adaptability to quickly adjust the allocation of resources, optimize the production process, shorten the delivery cycle, and continue to innovate to meet the ever-upgrading needs of consumers. By simulating natural selection and genetic mechanism, genetic algorithm can search effectively in the solution space and improve the solution efficiency. At the same time, combined with the variable neighborhood search strategy, we can change the search neighborhood in the iterative process of genetic algorithm to avoid falling into the local optimal solution, and further improve the quality of the solution. Therefore, this paper proposes a multi-objective supply chain network planning based on proximity optimization and hybrid genetic algorithm (GA) variable neighborhood search strategy. In order to verify the effectiveness of the proposed strategy, the study conducts multiple sets of arithmetic tests. The results revealed that the gap between the maximum and minimum values of the optimal solutions of the studied algorithms can be controlled within 1.5% in the medium-scale examples. In the large-scale example, the optimal solution of the research algorithm was controlled within 0.41%. In addition, the study also tested the optimization effect of the proposed model on different dimensions. The results revealed that in the economic cost dimension and social impact dimension, the model achieves the highest optimization effect at example 11, while the performance is relatively weak at example 2. In the environmental pollution dimension, the research model improved more than 0.5% on average over the triple bottom line optimization model. In summary, the strategy offers a new and useful tool for supply chain network planning and performs well in terms of both solution efficiency and solution quality.