Travel time optimization on multi-AGV routing by reverse annealing

Haba, Renichiro; Ohzeki, Masayuki; Tanaka, Kazuyuki

doi:10.1038/s41598-022-22704-0

Cited by 14 publications

(7 citation statements)

References 43 publications

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“…This allows us to search for a better solution. Haba's study 11 has shown that reverse annealing makes it easier to obtain the ground state.…”

Section: Discussionmentioning

confidence: 99%

Traffic signal optimization using quantum annealing on real map

Shikanai,

Ohzeki,

Tanaka

2023

Preprint

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The quantum annealing machine manufactured by D-Wave Systems is expected to find the optimal solution for QUBO (Quadratic Unconstrained Binary Optimization) accurately and quickly.This would be useful in future applications where real-time calculation is needed.One such application is traffic signal optimization.Some studies use quantum annealing for this.However, they are formulated in unrealistic settings, such as only crossroads on the map.Therefore, we suggest a QUBO, which can deal with T-junctions and multi-forked roads.To validate the efficiency of our approach, SUMO (Simulation of Urban MObility) is used.This enables us to experiment with geographic information data very close to the real world.We compared results with those using the Gurobi Optimizer in the experiment to confirm that quantum annealing can find a ground state.The results show that the quantum annealing cannot find the ground state, but our model can reduce the time that vehicles wait at a red light.It is also inferior to the Gurobi Optimizer in calculation time. This seems to be due to the D-Wave machine's hardware limitations and noise effects, such as ambient temperature.If these problems are solved, and the number of qubits is increased, the use of quantum annealing is likely to be superior in terms of the speed of calculating an optimal solution.

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“…This allows us to search for a better solution. Haba's study 11 has shown that reverse annealing makes it easier to obtain the ground state.…”

Section: Discussionmentioning

confidence: 99%

Traffic signal optimization using quantum annealing on real map

Shikanai,

Ohzeki,

Tanaka

2023

Preprint

Self Cite

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“…RA is utilized to seek a more optimal solution. There have been instances where RA has expedited the search for solutions with high accuracy using shorter annealing schedules than those in QA (FA) [34,35]. This paper extends this approach and presents a method that employs reverse annealing to enhance the efficiency of MOT.…”

Section: Enhanced Mot Through Ramentioning

confidence: 99%

Enhancing Multiple Object Tracking Accuracy via Quantum Annealing

Ihara

2024

Preprint

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Multiple object tracking (MOT), a key task in image recognition, presents a persistent challenge in balancing processing speed and tracking accuracy. This study introduces a novel approach that leverages quantum annealing (QA) to expedite computation speed, while enhancing tracking accuracy through the ensembling of object tracking processes. A method to improve the matching integration process is also proposed. By utilizing the sequential nature of MOT, this study further augments the tracking method via reverse annealing (RA). Experimental validation confirms the maintenance of high accuracy with an annealing time of a mere 3 µs per tracking process. The proposed method holds significant potential for real-time MOT applications, including traffic flow measurement for urban traffic light control, collision prediction for autonomous robots and vehicles, and management of products mass-produced in factories.

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“…With the constant improvement of information technology and the establishment of autonomous factories, automatic guided vehicles (AGVs) are gradually being used in the workshop to transport workpieces [1][2][3][4][5]. The performance of a workshop is affected in many aspects [6].…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization Method of Configuration Number of Automatic Guided Vehicles and Transportation Sequence of Workpieces in Workshop

Xu,

Ariel M.F,

Zhou

et al. 2023

J. Phys.: Conf. Ser.

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The number of automatic guided vehicles (AGVs) and the sequence of workpieces transported from the warehouse affect the final processing completion time of the system. Therefore, this paper researches the optimal configuration number of AGVs and sequence of transport workpieces in the workshop. Firstly, an optimization model is built with minimizing completion time. Secondly, Plant Simulation is used to determine the optimal configuration number of AGVs. Thirdly, linear sequence and uniform crossover, inversion, and exchange mutation are used to generate a new generation of populations, and a genetic algorithm (GA) is improved to optimize the transportation sequence of workpieces. Finally, an example verifies the effectiveness of the proposed method.

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Travel time optimization on multi-AGV routing by reverse annealing

Cited by 14 publications

References 43 publications

Traffic signal optimization using quantum annealing on real map

Traffic signal optimization using quantum annealing on real map

Enhancing Multiple Object Tracking Accuracy via Quantum Annealing

Research on Optimization Method of Configuration Number of Automatic Guided Vehicles and Transportation Sequence of Workpieces in Workshop

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