大连海事大学学报 >
2022 , Vol. 48 >Issue 3: 39 - 45
DOI: https://doi.org/10. 16411 / j. cnki. issn1006-7736. 2022. 03. 005
一种数据驱动的港口外集卡预约配额设计方法
收稿日期: 2022-05-14
修回日期: 2022-07-16
网络出版日期: 2022-07-16
基金资助
国家自然科学基金资助项目 (72202025);中央高校基本科研业务费专项资金资助项目(3132022188)
A data-driven design method for appointment quota of container trucks outside ports
Received date: 2022-05-14
Revised date: 2022-07-16
Online published: 2022-07-16
针对集装箱卡车预约配额优化问题,提出一种数据驱动的集装箱卡车预约配额设计方法。 该方法通过分析码头闸口数据(车辆进出闸口信息),揭示车辆到达时间分布与车辆总周转时间之间的因果关系,并以此建立和求解车辆到达时间分布优化模型,实现集装箱卡车预约系统的预约配额设计。 以盐田港的数据为例,对该方法的可行性进行实例分析。 数据挖掘的结果表明,在ÿ一个预约窗口内,作业类型为“提进口空箱作业” 以及“一交一提作业”的外部集装箱卡车,其总周转时间与车辆到达数量呈二次函数关系;而作业类型为“交出口重箱作业” 的外部集装箱卡车则表现出线性函数关系。 数例分析结果表明,基于上述函数关系所构建的集装箱卡车预约配额优化方法,可以有效降低码头车辆的总周转时间以及因交通拥堵产生的温室气体排放。 此外,该方法是基于实证数据分析得到的,因此具有较强的泛化性和实用性,可为各个码头优化集装箱卡车预约份额提供参考。
孙世超 , 董曜 , 郑勇 . 一种数据驱动的港口外集卡预约配额设计方法[J]. 大连海事大学学报, 2022 , 48(3) : 39 -45 . DOI: 10. 16411 / j. cnki. issn1006-7736. 2022. 03. 005
a , a data-driven design met hod of c ontainer t ruck appoin tment quota was pro pose d. This metho d revea led the causal relationship between the distribution of vehicle arrival time an d t he tot al tur nover time o f v ehicles by analy zing the data of t he sm art gate (in form ation o f ve hicle entranc e and e x-it), an d then establ ished and sol ved the opt imiz ation model of vehic le arriv al tim e distribu tion t o realize th e appoin tment quo ta d esign o f the con taine r tr uck appointm ent system. Taking the data of Y antian por t as an exam pl e, the feasibi lity of this m ethod was analyze d. The re sult s of data mini ng sh ow tha t in each appointm ent wi ndow , t he total tu rnove r t ime of the exter nal con tainer trucks wit h the operation types of “pi ck imported empty c ontainers” an d “ one-delivery-one-pick” is a quadra tic fun ctio n relation ship with t he nu mber of v ehicles ar-rivin g, while the external conta iner truck w ith the op eration type of “ delivery expo rted full conta iners” s hows a linear func tion al relationshi p. The ex amples an alysis results show that t he optimiza tion m eth od of container truc k appointm ent quota con struc ted based o n the ab ove- menti oned func tional re-latio nship can eff ectiv ely reduce the total turno ver time of dock vehicles and t he greenh ous e gas e mi ssions ca used by tra ffic c ongestion. In a dditi on, th e method is ba sed o n empiri-cal da ta anal ysis, so it h as strong g ene raliza tion and pra ctica-bility , which can provid e reference for each terminal to optimize the container truck appoin tment share.
Key words: ?truck appointment system; appointment; data-driven; smart gate data
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