Multi-car paint shop optimization with quantum annealing
We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the optimization is to minimize the number of color switches between cars in a paint shop queue during manufacturing, a known NP...
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| creator | Yarkoni, Sheir Alekseyenko, Alex Streif, Michael Von Dollen, David Neukart, Florian Bäck, Thomas |
| description | We present a generalization of the binary paint shop problem (BPSP) to tackle
an automotive industry application, the multi-car paint shop (MCPS) problem.
The objective of the optimization is to minimize the number of color switches
between cars in a paint shop queue during manufacturing, a known NP-hard
problem. We distinguish between different sub-classes of paint shop problems,
and show how to formulate the basic MCPS problem as an Ising model. The problem
instances used in this study are generated using real-world data from a factory
in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and
Advantage quantum processors to other classical solvers and a hybrid
quantum-classical algorithm offered by D-Wave Systems. We observe that the
quantum processors are well-suited for smaller problems, and the hybrid
algorithm for intermediate sizes. However, we find that the performance of
these algorithms quickly approaches that of a simple greedy algorithm in the
large size limit. |
| doi_str_mv | 10.48550/arxiv.2109.07876 |
| format | Article |
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an automotive industry application, the multi-car paint shop (MCPS) problem.
The objective of the optimization is to minimize the number of color switches
between cars in a paint shop queue during manufacturing, a known NP-hard
problem. We distinguish between different sub-classes of paint shop problems,
and show how to formulate the basic MCPS problem as an Ising model. The problem
instances used in this study are generated using real-world data from a factory
in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and
Advantage quantum processors to other classical solvers and a hybrid
quantum-classical algorithm offered by D-Wave Systems. We observe that the
quantum processors are well-suited for smaller problems, and the hybrid
algorithm for intermediate sizes. However, we find that the performance of
these algorithms quickly approaches that of a simple greedy algorithm in the
large size limit.</description><identifier>DOI: 10.48550/arxiv.2109.07876</identifier><language>eng</language><subject>Computer Science - Emerging Technologies ; Physics - Quantum Physics</subject><creationdate>2021-09</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>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2109.07876$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2109.07876$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Yarkoni, Sheir</creatorcontrib><creatorcontrib>Alekseyenko, Alex</creatorcontrib><creatorcontrib>Streif, Michael</creatorcontrib><creatorcontrib>Von Dollen, David</creatorcontrib><creatorcontrib>Neukart, Florian</creatorcontrib><creatorcontrib>Bäck, Thomas</creatorcontrib><title>Multi-car paint shop optimization with quantum annealing</title><description>We present a generalization of the binary paint shop problem (BPSP) to tackle
an automotive industry application, the multi-car paint shop (MCPS) problem.
The objective of the optimization is to minimize the number of color switches
between cars in a paint shop queue during manufacturing, a known NP-hard
problem. We distinguish between different sub-classes of paint shop problems,
and show how to formulate the basic MCPS problem as an Ising model. The problem
instances used in this study are generated using real-world data from a factory
in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and
Advantage quantum processors to other classical solvers and a hybrid
quantum-classical algorithm offered by D-Wave Systems. We observe that the
quantum processors are well-suited for smaller problems, and the hybrid
algorithm for intermediate sizes. However, we find that the performance of
these algorithms quickly approaches that of a simple greedy algorithm in the
large size limit.</description><subject>Computer Science - Emerging Technologies</subject><subject>Physics - Quantum Physics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7tOwzAUQL0wVIUPYMI_kNTx6zpjVVGKVMQAe3ST2u2VEiekDhS-HvUxne3oHMYeC5FrZ4xY4Hii71wWoswFOLAz5t6mNlHW4MgHpJj48dAPvB8SdfSHifrIfygd-NeEMU0dxxg9thT39-wuYHv0DzfO2cf6-XO1ybbvL6-r5TZDCzZTELT0GNAAGvSNExLABxC6lF6UO1XLOniQjavBysZYrZUxShdQip31as6ertZLeTWM1OH4W50PqsuB-ge460Dp</recordid><startdate>20210916</startdate><enddate>20210916</enddate><creator>Yarkoni, Sheir</creator><creator>Alekseyenko, Alex</creator><creator>Streif, Michael</creator><creator>Von Dollen, David</creator><creator>Neukart, Florian</creator><creator>Bäck, Thomas</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210916</creationdate><title>Multi-car paint shop optimization with quantum annealing</title><author>Yarkoni, Sheir ; Alekseyenko, Alex ; Streif, Michael ; Von Dollen, David ; Neukart, Florian ; Bäck, Thomas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-37f42eafa57a5aec80277ef70492e09d3b2bfe72c8b762c5644355341790d6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computer Science - Emerging Technologies</topic><topic>Physics - Quantum Physics</topic><toplevel>online_resources</toplevel><creatorcontrib>Yarkoni, Sheir</creatorcontrib><creatorcontrib>Alekseyenko, Alex</creatorcontrib><creatorcontrib>Streif, Michael</creatorcontrib><creatorcontrib>Von Dollen, David</creatorcontrib><creatorcontrib>Neukart, Florian</creatorcontrib><creatorcontrib>Bäck, Thomas</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yarkoni, Sheir</au><au>Alekseyenko, Alex</au><au>Streif, Michael</au><au>Von Dollen, David</au><au>Neukart, Florian</au><au>Bäck, Thomas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-car paint shop optimization with quantum annealing</atitle><date>2021-09-16</date><risdate>2021</risdate><abstract>We present a generalization of the binary paint shop problem (BPSP) to tackle
an automotive industry application, the multi-car paint shop (MCPS) problem.
The objective of the optimization is to minimize the number of color switches
between cars in a paint shop queue during manufacturing, a known NP-hard
problem. We distinguish between different sub-classes of paint shop problems,
and show how to formulate the basic MCPS problem as an Ising model. The problem
instances used in this study are generated using real-world data from a factory
in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and
Advantage quantum processors to other classical solvers and a hybrid
quantum-classical algorithm offered by D-Wave Systems. We observe that the
quantum processors are well-suited for smaller problems, and the hybrid
algorithm for intermediate sizes. However, we find that the performance of
these algorithms quickly approaches that of a simple greedy algorithm in the
large size limit.</abstract><doi>10.48550/arxiv.2109.07876</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Emerging Technologies Physics - Quantum Physics |
| title | Multi-car paint shop optimization with quantum annealing |
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