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Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading

机译:基于碳交易的带时间窗的多站点开放式车辆路径问题

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摘要

In order to cut the costs of third-party logistics companies and respond to the Chinese government’s low-carbon economy plans, this paper studies the more practical and complex open vehicle routing problem, which considers low-carbon trading policies. A low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW) model is constructed with minimum total costs, which include the driver’s salary, penalty costs, fuel costs and carbon emissions trading costs. Then, a two-phase algorithm is proposed to handle the model. In the first phase, the initial local solution is obtained with particle swarm optimization (PSO); in the second phase, we can obtain a global optimal solution through a further tabu search (TS). Experiments proved that the proposed algorithm is more suitable for small-scale cases. Furthermore, a series of experiments with different values of carbon prices and carbon quotas are conducted. The results of the study indicate that, as carbon trading prices and carbon quotas change, total costs, carbon emission trading costs and carbon emissions are affected accordingly. Based on these academic results, this paper presents some effective proposals for the government’s carbon trading policy-making and also for logistics companies to have better route planning under carbon emission constraints.
机译:为了削减第三方物流公司的成本并响应中国政府的低碳经济计划,本文研究了考虑低碳贸易政策的更实际,更复杂的开放式车辆路径问题。建立了具有时间窗的低碳多站点开放式车辆路线问题(MDOVRPTW)模型,该模型具有最低的总成本,其中包括驾驶员的薪水,罚款成本,燃料成本和碳排放交易成本。然后,提出了一种两阶段算法来处理该模型。在第一阶段,通过粒子群优化(PSO)获得初始局部解。在第二阶段,我们可以通过进一步的禁忌搜索(TS)获得全局最优解。实验证明,该算法更适合小规模案例。此外,还进行了一系列具有不同碳价格和碳配额值的实验。研究结果表明,随着碳交易价格和碳配额的变化,总成本,碳排放交易成本和碳排放也受到相应的影响。基于这些学术成果,本文为政府的碳交易政策制定以及物流公司在碳排放限制下制定更好的路线规划提供了一些有效的建议。

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