考虑拥堵路况下碳排放的选址-配送集成优化问题

doi:10.12005/orms.2019.0154

摘要/Abstract

摘要： 供应链网络的运输过程是碳排放的主要来源之一,道路拥堵、配送距离和车辆载重等因素会影响碳排放量,本文研究考虑拥堵路况下碳排放的选址-配送集成优化问题。根据车辆行驶状态定义道路拥堵情况,以不同时段下拥堵概率和预期拥堵距离作为路况决定因素,构建碳排放量和经济成本都最小的两目标模型。设计改进的非支配排序遗传算法(NSGA-II)求解模型获得Pareto解集。以环境问题较重的北京和天津为中心构建供应链网络作为算例,验证了模型和算法的有效性和可行性。实验结果给出了不同偏好下供应链网络的构建方案,并对不同时段下决定路况的拥堵概率和预期拥堵距离以及车辆载重进行灵敏度分析。实验结果表明,相对于高速公路,城市道路不同时段对碳排放量影响更敏感。

关键词: 拥堵概率, 预期拥堵距离, 车流量, 车辆载重, 改进的非支配排序遗传算法

Abstract: The transport process of the supply chain network is one of the main sources of carbon emissions. Road congestion, distribution distance, vehicle load and other factors will affect the carbon emissions. This paper studies the location-distribution integrated optimization considering carbon emissions under congested road conditions. The road congestion is defined according to the vehicle running state. Taking the congestion probability and the expected congestion distance at different time periods as the determinants of road conditions, we construct a two objective model with minimum economic cost and carbon emissions. Non-dominated sorting genetic algorithm (NSGA-II) is designed to obtain the Pareto solution set. We build a supply chain network centered on Beijing and Tianjin with heavy environmental problems to verify the effectiveness and feasibility of the model and algorithm. The results of the experiment give the construction scheme of the supply chain network under different preferences. The sensitivity analysis is made for the congestion probability and the expected congestion distance under different time periods as well as the vehicle load. The experiment results show that the impact of urban road on carbon emissions is more sensitive than that of expressway at different time periods.

Key words: congestion probability, expected congestion distance, traffic flow, vehicle load, NSGA-II

中图分类号:

C931

任慧, 王东宇. 考虑拥堵路况下碳排放的选址-配送集成优化问题[J]. 运筹与管理, 2019, 28(7): 81-90.

REN Hui, WANG Dong-yu. Location-distribution Integrated Optimization considering CarbonEmissions under the Congested Road Condition[J]. Operations Research and Management Science, 2019, 28(7): 81-90.

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