The Same-Day Delivery Problem for Online Purchases

Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incor...

Full description

Saved in:
Bibliographic Details
Published in:Transportation science 2019-01, Vol.53 (1), p.167-184
Main Authors: Voccia, Stacy A., Campbell, Ann Melissa, Thomas, Barrett W.
Format: Article
Language:English
Subjects:
Decision analysis
Decision making
Decisions
dynamic vehicle routing
Electric vehicles
Flexibility
Heuristic
Logistics
Routing
same-day delivery
time windows
Transportation
Windows
Windows (intervals)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3
cites cdi_FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3
container_end_page 184
container_issue 1
container_start_page 167
container_title Transportation science
container_volume 53
creator Voccia, Stacy A.
Campbell, Ann Melissa
Thomas, Barrett W.
description Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incorporates information about future requests into routing decisions. We also introduce an analytical result that identifies when it is beneficial for vehicles to wait at the depot. We present a wide range of computational experiments that demonstrate the value of our approach. The results show that more requests can be filled when time windows are evenly spread throughout the day compared to when many requests' time windows occur late in the day. However, the anticipation of future requests is most valuable when many requests' time windows occur late in the day. As a result of increased flexibility, experiments also demonstrate that the value of anticipating the future decreases when the number of vehicles or the arrival rate of requests increases. The online appendix is available at https://doi.org/10.1287/trsc.2016.0732 .
doi_str_mv 10.1287/trsc.2016.0732
format article
fullrecord <record><control><sourceid>jstor_infor</sourceid><recordid>TN_cdi_informs_primary_10_1287_trsc_2016_0732</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>27296501</jstor_id><sourcerecordid>27296501</sourcerecordid><originalsourceid>FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3</originalsourceid><addsrcrecordid>eNqFkM9LwzAUx4MoOKdXb0LBc-p7aZM0R9n8BYMN3D00aco6-mMmrbD_3paKV0_v8P3F-xByjxAjy-RT74ONGaCIQSbsgiyQM0F5mspLsgBIkaLg_JrchHAEQC6RLwjbH1z0mTeOrvNztHZ19e38Odr5ztSuicrOR9u2rloX7QZvD3lw4ZZclXkd3N3vXZL968t-9U4327eP1fOG2pTxnuYlpCxxxhamQAlFriCRKQcJyFRqlSqUlSXDhCF3IIwwBZgiyVypjOQ2WZLHufbku6_BhV4fu8G346JmmIkMMq746Ipnl_VdCN6V-uSrJvdnjaAnLHrCoicsesIyBh7mwDH0nf9zM8mU4ICjTme9asfnm_Bf3w-u92y6</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2186808595</pqid></control><display><type>article</type><title>The Same-Day Delivery Problem for Online Purchases</title><source>International Bibliography of the Social Sciences (IBSS)</source><source>JSTOR Archival Journals and Primary Sources Collection【Remote access available】</source><source>BSC - Ebsco (Business Source Ultimate)</source><creator>Voccia, Stacy A. ; Campbell, Ann Melissa ; Thomas, Barrett W.</creator><creatorcontrib>Voccia, Stacy A. ; Campbell, Ann Melissa ; Thomas, Barrett W.</creatorcontrib><description>Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incorporates information about future requests into routing decisions. We also introduce an analytical result that identifies when it is beneficial for vehicles to wait at the depot. We present a wide range of computational experiments that demonstrate the value of our approach. The results show that more requests can be filled when time windows are evenly spread throughout the day compared to when many requests' time windows occur late in the day. However, the anticipation of future requests is most valuable when many requests' time windows occur late in the day. As a result of increased flexibility, experiments also demonstrate that the value of anticipating the future decreases when the number of vehicles or the arrival rate of requests increases. The online appendix is available at https://doi.org/10.1287/trsc.2016.0732 .</description><identifier>ISSN: 0041-1655</identifier><identifier>EISSN: 1526-5447</identifier><identifier>DOI: 10.1287/trsc.2016.0732</identifier><language>eng</language><publisher>Baltimore: INFORMS</publisher><subject>Decision analysis ; Decision making ; Decisions ; dynamic vehicle routing ; Electric vehicles ; Flexibility ; Heuristic ; Logistics ; Routing ; same-day delivery ; time windows ; Transportation ; Windows ; Windows (intervals)</subject><ispartof>Transportation science, 2019-01, Vol.53 (1), p.167-184</ispartof><rights>2017 INFORMS</rights><rights>Copyright Institute for Operations Research and the Management Sciences Jan/Feb 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3</citedby><cites>FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3</cites><orcidid>0000-0002-6080-6191 ; 0000-0002-9927-687X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/27296501$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/27296501$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,33200,58213,58446</link.rule.ids></links><search><creatorcontrib>Voccia, Stacy A.</creatorcontrib><creatorcontrib>Campbell, Ann Melissa</creatorcontrib><creatorcontrib>Thomas, Barrett W.</creatorcontrib><title>The Same-Day Delivery Problem for Online Purchases</title><title>Transportation science</title><description>Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incorporates information about future requests into routing decisions. We also introduce an analytical result that identifies when it is beneficial for vehicles to wait at the depot. We present a wide range of computational experiments that demonstrate the value of our approach. The results show that more requests can be filled when time windows are evenly spread throughout the day compared to when many requests' time windows occur late in the day. However, the anticipation of future requests is most valuable when many requests' time windows occur late in the day. As a result of increased flexibility, experiments also demonstrate that the value of anticipating the future decreases when the number of vehicles or the arrival rate of requests increases. The online appendix is available at https://doi.org/10.1287/trsc.2016.0732 .</description><subject>Decision analysis</subject><subject>Decision making</subject><subject>Decisions</subject><subject>dynamic vehicle routing</subject><subject>Electric vehicles</subject><subject>Flexibility</subject><subject>Heuristic</subject><subject>Logistics</subject><subject>Routing</subject><subject>same-day delivery</subject><subject>time windows</subject><subject>Transportation</subject><subject>Windows</subject><subject>Windows (intervals)</subject><issn>0041-1655</issn><issn>1526-5447</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><recordid>eNqFkM9LwzAUx4MoOKdXb0LBc-p7aZM0R9n8BYMN3D00aco6-mMmrbD_3paKV0_v8P3F-xByjxAjy-RT74ONGaCIQSbsgiyQM0F5mspLsgBIkaLg_JrchHAEQC6RLwjbH1z0mTeOrvNztHZ19e38Odr5ztSuicrOR9u2rloX7QZvD3lw4ZZclXkd3N3vXZL968t-9U4327eP1fOG2pTxnuYlpCxxxhamQAlFriCRKQcJyFRqlSqUlSXDhCF3IIwwBZgiyVypjOQ2WZLHufbku6_BhV4fu8G346JmmIkMMq746Ipnl_VdCN6V-uSrJvdnjaAnLHrCoicsesIyBh7mwDH0nf9zM8mU4ICjTme9asfnm_Bf3w-u92y6</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Voccia, Stacy A.</creator><creator>Campbell, Ann Melissa</creator><creator>Thomas, Barrett W.</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><orcidid>https://orcid.org/0000-0002-6080-6191</orcidid><orcidid>https://orcid.org/0000-0002-9927-687X</orcidid></search><sort><creationdate>20190101</creationdate><title>The Same-Day Delivery Problem for Online Purchases</title><author>Voccia, Stacy A. ; Campbell, Ann Melissa ; Thomas, Barrett W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Decision analysis</topic><topic>Decision making</topic><topic>Decisions</topic><topic>dynamic vehicle routing</topic><topic>Electric vehicles</topic><topic>Flexibility</topic><topic>Heuristic</topic><topic>Logistics</topic><topic>Routing</topic><topic>same-day delivery</topic><topic>time windows</topic><topic>Transportation</topic><topic>Windows</topic><topic>Windows (intervals)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Voccia, Stacy A.</creatorcontrib><creatorcontrib>Campbell, Ann Melissa</creatorcontrib><creatorcontrib>Thomas, Barrett W.</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Transportation science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Voccia, Stacy A.</au><au>Campbell, Ann Melissa</au><au>Thomas, Barrett W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Same-Day Delivery Problem for Online Purchases</atitle><jtitle>Transportation science</jtitle><date>2019-01-01</date><risdate>2019</risdate><volume>53</volume><issue>1</issue><spage>167</spage><epage>184</epage><pages>167-184</pages><issn>0041-1655</issn><eissn>1526-5447</eissn><abstract>Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incorporates information about future requests into routing decisions. We also introduce an analytical result that identifies when it is beneficial for vehicles to wait at the depot. We present a wide range of computational experiments that demonstrate the value of our approach. The results show that more requests can be filled when time windows are evenly spread throughout the day compared to when many requests' time windows occur late in the day. However, the anticipation of future requests is most valuable when many requests' time windows occur late in the day. As a result of increased flexibility, experiments also demonstrate that the value of anticipating the future decreases when the number of vehicles or the arrival rate of requests increases. The online appendix is available at https://doi.org/10.1287/trsc.2016.0732 .</abstract><cop>Baltimore</cop><pub>INFORMS</pub><doi>10.1287/trsc.2016.0732</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-6080-6191</orcidid><orcidid>https://orcid.org/0000-0002-9927-687X</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0041-1655
ispartof Transportation science, 2019-01, Vol.53 (1), p.167-184
issn 0041-1655
1526-5447
language eng
recordid cdi_informs_primary_10_1287_trsc_2016_0732
source International Bibliography of the Social Sciences (IBSS); JSTOR Archival Journals and Primary Sources Collection【Remote access available】; BSC - Ebsco (Business Source Ultimate)
subjects Decision analysis
Decision making
Decisions
dynamic vehicle routing
Electric vehicles
Flexibility
Heuristic
Logistics
Routing
same-day delivery
time windows
Transportation
Windows
Windows (intervals)
title The Same-Day Delivery Problem for Online Purchases
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T01%3A10%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_infor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Same-Day%20Delivery%20Problem%20for%20Online%20Purchases&rft.jtitle=Transportation%20science&rft.au=Voccia,%20Stacy%20A.&rft.date=2019-01-01&rft.volume=53&rft.issue=1&rft.spage=167&rft.epage=184&rft.pages=167-184&rft.issn=0041-1655&rft.eissn=1526-5447&rft_id=info:doi/10.1287/trsc.2016.0732&rft_dat=%3Cjstor_infor%3E27296501%3C/jstor_infor%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c425t-af0423ebcdbd170da9037450701294c99d9c7f213215e06b6bd0bd38ef9b75c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2186808595&rft_id=info:pmid/&rft_jstor_id=27296501&rfr_iscdi=true