Using Quantum Annealing to Solve Large-Scale Optimization Problems in Logistics and Scheduling

In planning and scheduling, large-scale optimization problems often have complex limits and a lot of factors, which makes them hard to answer with standard methods. Quantum annealing has become a hopeful way to deal with these problems because it can quickly look into a lot of possible solutions. Th...

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Bibliographic Details
Published in:International Conference on Emerging Smart Computing and Informatics (Online) pp. 1 - 6
Main Author: Kumar, Sandeep
Format: Conference Proceeding
Language:English
Published: IEEE 05.03.2025
Subjects:
Annealing
Computational modeling
Linear programming
Logistics
Optimization
Processor scheduling
Quantum annealing
Quantum Computing
QUBO
Schedules
Scheduling
Simulated annealing
Transportation
ISSN:2996-1815
Online Access:Get full text
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Summary:In planning and scheduling, large-scale optimization problems often have complex limits and a lot of factors, which makes them hard to answer with standard methods. Quantum annealing has become a hopeful way to deal with these problems because it can quickly look into a lot of possible solutions. This essay looks into how quantum annealing might be used to solve optimization problems in transportation and scheduling, with a focus on how it could be used for route planning, inventory management, and arranging workers. To do these optimization jobs, quantum annealing is used. This is a quantum computing method that gets the global minimum of a cost function. We use the D-Wave quantum annealer in this method. It turns the problem's limits and objective function into a quadratic unconstrained binary optimization (QUBO) model. The quantum annealer then works on this model to find the best answers. The suggested method combines traditional preparation methods with quantum annealing to get the best results for big schedule and transportation issues. We use quantum annealing to solve difficult multi-objective optimization problems, like reducing shipping time, cost, and resource use all at the same time. Preliminary results show that quantum annealing can make solutions much better and use computers much more efficiently than traditional methods for optimizing supplies and schedules. Quantum annealing works especially well with problems that have a lot of dimensions. It can provide scalable answers that sometimes work better than standard methods.
ISSN:2996-1815
DOI:10.1109/ESCI63694.2025.10988208