TY - GEN
T1 - On Solving the Capacitated Vehicle Routing Problem with Time Windows using Quantum Annealing
AU - Vargas, Axel
AU - Shukla, Pradyumn
AU - Allmendinger, Richard
AU - Jaeger, Andreas
N1 - Publisher Copyright:
© 2024 is held by the owner/author(s).
PY - 2024/8/1
Y1 - 2024/8/1
N2 - This study explores Quantum Annealing (QA) versus Classical Computing (CC) approaches in solving the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), using real-world logistics data from Siemens Advanta. By modeling the problem as a Service Network Design (SND) with binary variables and framing CVRPTW within a quantum mechanical paradigm suitable for QA, this approach leverages quantum annealing in a novel manner to tackle the complex optimization challenges inherent in the VRP. Contrasting it with the performance of the traditional branch and cut solver - -a specific CC technique - -this research unveils the prospective benefits and existing challenges of QA in complex optimization scenarios, thereby emphasizing the need for advancing quantum computing.
AB - This study explores Quantum Annealing (QA) versus Classical Computing (CC) approaches in solving the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), using real-world logistics data from Siemens Advanta. By modeling the problem as a Service Network Design (SND) with binary variables and framing CVRPTW within a quantum mechanical paradigm suitable for QA, this approach leverages quantum annealing in a novel manner to tackle the complex optimization challenges inherent in the VRP. Contrasting it with the performance of the traditional branch and cut solver - -a specific CC technique - -this research unveils the prospective benefits and existing challenges of QA in complex optimization scenarios, thereby emphasizing the need for advancing quantum computing.
KW - capacitated vehicle routing problem with time windows
KW - network optimization
KW - operations research
KW - optimization algorithms
KW - quantum annealing
KW - scheduling algorithms
UR - http://www.scopus.com/inward/record.url?scp=85201961504&partnerID=8YFLogxK
U2 - 10.1145/3638530.3664139
DO - 10.1145/3638530.3664139
M3 - Conference contribution
AN - SCOPUS:85201961504
T3 - GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
SP - 1979
EP - 1983
BT - GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery
T2 - 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion
Y2 - 14 July 2024 through 18 July 2024
ER -