随着我国电商行业的迅猛发展以及国家对相关减排政策的大力倡导与推行,针对物流企业使用电动冷藏车开展生鲜冷链运输的具体情境,构建了一个能够同时考虑企业在配送过程中所产生的综合运输成本、碳排放量及配送水平的多目标优化模型。在NSGA-II算法中引入了佳点集生成初始种群、自适应交叉变异概率和模拟退火辅助局部搜索的改进策略。实验结果显示,改进后的算法有效克服了传统NSGA-II算法对初始种群敏感、局部搜索能力有限、收敛速度较慢等问题,获得了更优质的Pareto解集,从而验证了该改进算法的有效性。With the rapid development of China’s e-commerce industry and the country’s strong advocacy and implementation of relevant emission reduction policies, a multi-objective optimization model is constructed to consider the comprehensive transportation cost, carbon emissions and distribution level generated by enterprises in the distribution process, aiming at the specific situation of logistics enterprises using electric refrigerated trucks to carry out fresh cold chain transportation. In NSGA-II algorithm, the improved strategies of generating initial population with good point set, adaptive cross-mutation probability and simulated annealing assisted local search are introduced. Experimental results show that the improved algorithm effectively overcomes the problems of the traditional NSGA-II algorithm, such as sensitivity to the initial population, limited local search ability and slow convergence speed, and obtains a better Pareto solution set, thus verifying the effectiveness of the improved algorithm.
