当前位置: X-MOL 学术Eng. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach
Engineering Computations ( IF 1.6 ) Pub Date : 2021-07-12 , DOI: 10.1108/ec-10-2020-0587
Yong Peng 1 , Yi Juan Luo 1 , Pei Jiang 2 , Peng Cheng Yong 1
Affiliation  

Purpose

Distribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.

Design/methodology/approach

Monte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.

Findings

Numerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.

Originality/value

The impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.



中文翻译:

带时刻表的多式联运路线问题:随机多目标优化模型和数据驱动的模拟启发式方法

目的

长途货物的配送可以通过多式联运网络进行管理,决策者必须考虑这些因素,包括运输时间和成本的不确定性、所选模式的时间表限制以及货物提前或延迟到达的存储成本. 综合考虑上述因素,本文建立了以最小化总运输时间和成本为目标的多式联运多目标路线优化模型。本研究可为决策者制定交通计划提供参考。

设计/方法/方法

引入蒙特卡洛(MC)模拟来处理运输不确定性,并设计了具有外部档案精英保留策略的NSGA-II算法。一种基于数据驱动的高效变换方法,克服了MC仿真带来的高耗时问题。本研究的其他贡献是针对通过 NSGA-II 算法获得的非绝对最优 Pareto 前沿解集开发了一种方案风险评估方法。

发现

数值实例验证了所提算法的有效性,因为它能够找到高质量的解决方案,并且本文提出的风险评估方法可以为路径决策提供支持。

原创性/价值

分析了时刻表对运输时长的影响,并在数学模型中进行了详细描述。不确定的运输持续时间和成本用服从一定分布的随机数表示,并设计了带有MC模拟的NSGA-II来解决所提出的问题。采用数据驱动策略,减少进化算法与MC仿真相结合带来的计算时间。具有外部归档的精英保留策略旨在提高解决方案的质量。针对求解方案和数值模拟实验提出了一种风险评估方法。

更新日期:2021-07-12
down
wechat
bug