Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks
The Autonomous Transfer Hub Network (ATHN) is one of the most promising ways to adapt self-driving trucks for the freight industry. These networks use autonomous trucks for the middle mile, while human drivers perform the first and last miles. This paper extends previous work on optimizing ATHN oper...
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| creator | Lee, Chungjae Boonbandansook, Wirattawut Akhlaghi, Vahid Eghbal Dalmeijer, Kevin Van Hentenryck, Pascal |
| description | The Autonomous Transfer Hub Network (ATHN) is one of the most promising ways
to adapt self-driving trucks for the freight industry. These networks use
autonomous trucks for the middle mile, while human drivers perform the first
and last miles. This paper extends previous work on optimizing ATHN operations
by including transfer hub capacities, which are crucial for labor planning and
policy design. It presents a Constraint Programming (CP) model that shifts an
initial schedule produced by a Mixed Integer Program to minimize the hub
capacities. The scalability of the CP model is demonstrated on a case study at
the scale of the United States, based on data provided by Ryder System, Inc.
The CP model efficiently finds optimal solutions and lowers the necessary total
hub capacity by 42%, saving $15.2M in annual labor costs. The results also show
that the reduced capacity is close to a theoretical (optimistic) lower bound. |
| doi_str_mv | 10.48550/arxiv.2305.03191 |
| format | Article |
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to adapt self-driving trucks for the freight industry. These networks use
autonomous trucks for the middle mile, while human drivers perform the first
and last miles. This paper extends previous work on optimizing ATHN operations
by including transfer hub capacities, which are crucial for labor planning and
policy design. It presents a Constraint Programming (CP) model that shifts an
initial schedule produced by a Mixed Integer Program to minimize the hub
capacities. The scalability of the CP model is demonstrated on a case study at
the scale of the United States, based on data provided by Ryder System, Inc.
The CP model efficiently finds optimal solutions and lowers the necessary total
hub capacity by 42%, saving $15.2M in annual labor costs. The results also show
that the reduced capacity is close to a theoretical (optimistic) lower bound.</description><identifier>DOI: 10.48550/arxiv.2305.03191</identifier><language>eng</language><subject>Mathematics - Optimization and Control</subject><creationdate>2023-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2305.03191$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2305.03191$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.4230/LIPIcs.CP.2023.46$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Chungjae</creatorcontrib><creatorcontrib>Boonbandansook, Wirattawut</creatorcontrib><creatorcontrib>Akhlaghi, Vahid Eghbal</creatorcontrib><creatorcontrib>Dalmeijer, Kevin</creatorcontrib><creatorcontrib>Van Hentenryck, Pascal</creatorcontrib><title>Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks</title><description>The Autonomous Transfer Hub Network (ATHN) is one of the most promising ways
to adapt self-driving trucks for the freight industry. These networks use
autonomous trucks for the middle mile, while human drivers perform the first
and last miles. This paper extends previous work on optimizing ATHN operations
by including transfer hub capacities, which are crucial for labor planning and
policy design. It presents a Constraint Programming (CP) model that shifts an
initial schedule produced by a Mixed Integer Program to minimize the hub
capacities. The scalability of the CP model is demonstrated on a case study at
the scale of the United States, based on data provided by Ryder System, Inc.
The CP model efficiently finds optimal solutions and lowers the necessary total
hub capacity by 42%, saving $15.2M in annual labor costs. The results also show
that the reduced capacity is close to a theoretical (optimistic) lower bound.</description><subject>Mathematics - Optimization and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAYhWEvDKhwAUz4BhL8n2SsIqCVKmBI5-hrYlcWjV19dsrP1SMC01leHekh5I6zUtVaswfAT38phWS6ZJI3_Jrs2xhSRvAh0zeMR4Rp8uFIc6Tb6YzxYulmPtB99if_DdnHQH2g6znHEKc4J9ohhOQsLtmLzR8R39MNuXJwSvb2f1eke3rs2k2xe33etutdAabihRaD0eNhsKbRilUDE9JJO2jLAEC5ikkzytoOo3CNqGuhRi6s5pVRHOrRMLki93-3i6s_o58Av_pfX7_45A90ekvO</recordid><startdate>20230504</startdate><enddate>20230504</enddate><creator>Lee, Chungjae</creator><creator>Boonbandansook, Wirattawut</creator><creator>Akhlaghi, Vahid Eghbal</creator><creator>Dalmeijer, Kevin</creator><creator>Van Hentenryck, Pascal</creator><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20230504</creationdate><title>Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks</title><author>Lee, Chungjae ; Boonbandansook, Wirattawut ; Akhlaghi, Vahid Eghbal ; Dalmeijer, Kevin ; Van Hentenryck, Pascal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-52c65dbce695407c023f3ec5e0aaa4f7036d38ecd2f928824d12e517641a8d603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Mathematics - Optimization and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Lee, Chungjae</creatorcontrib><creatorcontrib>Boonbandansook, Wirattawut</creatorcontrib><creatorcontrib>Akhlaghi, Vahid Eghbal</creatorcontrib><creatorcontrib>Dalmeijer, Kevin</creatorcontrib><creatorcontrib>Van Hentenryck, Pascal</creatorcontrib><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lee, Chungjae</au><au>Boonbandansook, Wirattawut</au><au>Akhlaghi, Vahid Eghbal</au><au>Dalmeijer, Kevin</au><au>Van Hentenryck, Pascal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks</atitle><date>2023-05-04</date><risdate>2023</risdate><abstract>The Autonomous Transfer Hub Network (ATHN) is one of the most promising ways
to adapt self-driving trucks for the freight industry. These networks use
autonomous trucks for the middle mile, while human drivers perform the first
and last miles. This paper extends previous work on optimizing ATHN operations
by including transfer hub capacities, which are crucial for labor planning and
policy design. It presents a Constraint Programming (CP) model that shifts an
initial schedule produced by a Mixed Integer Program to minimize the hub
capacities. The scalability of the CP model is demonstrated on a case study at
the scale of the United States, based on data provided by Ryder System, Inc.
The CP model efficiently finds optimal solutions and lowers the necessary total
hub capacity by 42%, saving $15.2M in annual labor costs. The results also show
that the reduced capacity is close to a theoretical (optimistic) lower bound.</abstract><doi>10.48550/arxiv.2305.03191</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Mathematics - Optimization and Control |
| title | Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks |
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