Industrial information integration can help companies develop supply chain systems and is a good framework for vehicle routing problem. To address the problems of excessive energy consumption, environmental pollution caused by carbon dioxide emission, and timeliness in the transportation process, a multi-objective vehicle routing optimization model was proposed to minimise transportation, carbon emission, and time window penalty costs. In the proposed model, an elitist nondominated sorting genetic algorithm was used to obtain the Pareto optimal solution. Furthermore, the optimal distribution path was selected by using multi-objective grey target decision-making according to entropy value. Finally, a real distribution case was analysed, and the calculated optimal path was compared with the actual path of the case to verify the feasibility of the proposed model and algorithm. The results showed that the proposed model and algorithm can be effectively used to reduce the target cost.

工业信息集成可以帮助企业开发供应链系统，是解决车辆路径问题的一个很好的框架。针对交通运输过程中能源消耗过大、二氧化碳排放造成环境污染和时效性问题，提出了一种多目标车辆路径优化模型，以最小化运输、碳排放和时间窗惩罚成本。在所提出的模型中，使用精英非支配排序遗传算法获得帕累托最优解。进而根据熵值采用多目标灰色目标决策选择最优分布路径。最后，对一个真实的分布案例进行分析，并将计算得到的最优路径与案例的实际路径进行对比，验证了所提模型和算法的可行性。结果表明，所提出的模型和算法可以有效地降低目标成本。