Network parameter setting.

For the optimal design of the sustainable supply chain network, considering the comprehensiveness of the problem factors, considering the three aspects of economy, environment and society, the goal is to minimize the establishment cost, minimize the emission of environ-mental pollution and maximize the number of labor. A mixed integer programming model is established to maximize the efficiency of the supply chain network. The innovation of this paper, first, is to consider the impact of economic, environmental and social benefits in a continuous supply chain, where the environmental benefits not only consider carbon emissions but also include the emissions of plant wastewater, waste and solid waste as influencing factors. Second, a multi-objective fuzzy affiliation function is constructed to measure the quality of the model solution in terms of the overall satisfaction value. Finally, the chaotic particle ant colony algorithm is proposed, and the problem of premature convergence in the operation of the particle swarm algorithm is solved. Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.针对可持续供应链网络的优化设计问题，为充分考量问题各影响因素的全面性，本文从经济、环境与社会三个维度出发，以最小化建设成本、降低环境污染排放量以及最大化就业岗位数量为优化目标，构建混合整数规划模型以最大化供应链网络运行效率。本文的创新点主要包括：其一，在连续型供应链场景中兼顾经济、环境与社会效益的影响，其中环境效益不仅考量碳排放，还将工厂废水、一般废弃物及固体废物的排放纳入影响因素范畴；其二，构建多目标模糊隶属度函数，通过整体满意度值衡量模型求解结果的优劣；其三，提出混沌粒子蚁群算法，解决了粒子群算法运行过程中易出现的早熟收敛问题。实验结果表明，将本文提出的PSCACO算法与MOPSO、CACO及NSGA-II算法进行对比后，可证实该算法的收敛效果更优，由此验证了混沌粒子蚁群算法求解多目标优化问题的有效性与可行性，为供应链管理领域提供了一种全新的可行解决方案。