Approximate dynamic programming for pickup and delivery problem with crowd-shipping

IF 5.8 1区 工程技术 Q1 ECONOMICS Transportation Research Part B-Methodological Pub Date : 2024-07-29 DOI:10.1016/j.trb.2024.103027
Kianoush Mousavi , Merve Bodur , Mucahit Cevik , Matthew J. Roorda
{"title":"Approximate dynamic programming for pickup and delivery problem with crowd-shipping","authors":"Kianoush Mousavi ,&nbsp;Merve Bodur ,&nbsp;Mucahit Cevik ,&nbsp;Matthew J. Roorda","doi":"10.1016/j.trb.2024.103027","DOIUrl":null,"url":null,"abstract":"<div><p>We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd-shippers that impose several challenges for efficient matching of orders to crowd-shippers. We formulate the problem as a Markov decision process and develop an Approximate Dynamic Programming (ADP) policy using value function approximation for obtaining a highly scalable and real-time matching strategy while considering temporal and spatial uncertainty in arrivals of online orders and crowd-shippers. We incorporate several algorithmic enhancements to the ADP algorithm, which significantly improve the convergence. We compare the ADP policy with an optimization-based myopic policy using various performance measures. Our numerical analysis with varying parameter settings shows that ADP policies can lead to up to 25.2% cost savings and a 9.8% increase in the number of served orders. Overall, we find that our proposed framework can guide crowd-shipping platforms for efficient real-time matching decisions and enhance the platform delivery capacity.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"187 ","pages":"Article 103027"},"PeriodicalIF":5.8000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0191261524001516/pdfft?md5=a403f465827064c17e360b684a0c494e&pid=1-s2.0-S0191261524001516-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part B-Methodological","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0191261524001516","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd-shippers that impose several challenges for efficient matching of orders to crowd-shippers. We formulate the problem as a Markov decision process and develop an Approximate Dynamic Programming (ADP) policy using value function approximation for obtaining a highly scalable and real-time matching strategy while considering temporal and spatial uncertainty in arrivals of online orders and crowd-shippers. We incorporate several algorithmic enhancements to the ADP algorithm, which significantly improve the convergence. We compare the ADP policy with an optimization-based myopic policy using various performance measures. Our numerical analysis with varying parameter settings shows that ADP policies can lead to up to 25.2% cost savings and a 9.8% increase in the number of served orders. Overall, we find that our proposed framework can guide crowd-shipping platforms for efficient real-time matching decisions and enhance the platform delivery capacity.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
有人群运输的取货和送货问题的近似动态程序设计
我们研究了一种动态取货和送货众包操作的变体,用于在几小时内从实体店交付在线订单。由于在线订单和人群配送员的随机到达,这种人群配送操作具有高度的不确定性,这给高效匹配订单和人群配送员带来了诸多挑战。我们将这一问题表述为马尔可夫决策过程,并开发了一种近似动态编程(ADP)策略,该策略采用值函数近似法,可在考虑在线订单和人群发货人到达的时间和空间不确定性的同时,获得高度可扩展的实时匹配策略。我们对 ADP 算法进行了多项算法改进,显著提高了收敛性。我们使用各种性能指标对 ADP 策略和基于优化的近视策略进行了比较。我们在不同参数设置下进行的数值分析表明,ADP 政策可节省高达 25.2% 的成本,并使服务订单数量增加 9.8%。总之,我们发现我们提出的框架可以指导众包平台做出高效的实时匹配决策,并提高平台的交付能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Transportation Research Part B-Methodological
Transportation Research Part B-Methodological 工程技术-工程:土木
CiteScore
12.40
自引率
8.80%
发文量
143
审稿时长
14.1 weeks
期刊介绍: Transportation Research: Part B publishes papers on all methodological aspects of the subject, particularly those that require mathematical analysis. The general theme of the journal is the development and solution of problems that are adequately motivated to deal with important aspects of the design and/or analysis of transportation systems. Areas covered include: traffic flow; design and analysis of transportation networks; control and scheduling; optimization; queuing theory; logistics; supply chains; development and application of statistical, econometric and mathematical models to address transportation problems; cost models; pricing and/or investment; traveler or shipper behavior; cost-benefit methodologies.
期刊最新文献
Modelling the impacts of en-route ride-pooling service in a mixed pooling and non-pooling market Amachine learning technique embedded reference-dependent choice model for explanatory power improvement: Shifting of reference point as a key factor in vehicle purchase decision-making Making the most of your private parking slot: Strategy-proof double auctions-enabled staggered sharing schemes Editorial Board Safety, liability, and insurance markets in the age of automated driving
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1