Hybrid quantum-classical heuristic for the bin packing problem

Andoin, Mikel Garcia de; Osaba, Eneko; Oregi, Izaskun; Villar-Rodríguez, Esther; Sanz, Mikel

doi:10.1145/3520304.3533986

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…where (19) guarantees that items i placed inside the bin j are not outside of the last bin (n-th bin) along the x axis, (20) ensures that item i is located inside of its corresponding bin j along the x axis (activated if n > 1), (21) confirms that item i placed inside the bin j is not outside along the y axis, while (22) ensures that item i allocated inside the bin j is not outside along the z axis.…”

Section: /9mentioning

confidence: 99%

“…The pioneering work on BPP in the field of quantum computing presents a hybrid quantum-classical method for solving the 1dBPP 20 , whose solver is composed of two modules: i) a quantum subroutine with which to search a set of feasible configurations to fill one single bin and ii) a classical computational heuristic which builds complete solutions employing the subsets given by the quantum subroutine. To deepen the performance of the quantum subroutine developed, further tests were conducted against a random sampling and a random walk-based heuristic 21 .…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers

Romero

Osaba

Villar-Rodríguez

et al. 2023

Preprint

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Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wide interest from industry and logistics. Since decades, many variants have been proposed, with the three-dimensional Bin Packing Problem as the closest one to real-world use cases. We introduce a hybrid quantum-classical framework for solving real-world three-dimensional Bin Packing Problems (Q4RealBPP), considering different realistic characteristics, such as: i) package and bin dimensions, ii) overweight restrictions, iii) affinities among item categories and iv) preferences for item ordering. Q4RealBPP permits the solving of real-world oriented instances of 3dBPP, contemplating restrictions well appreciated by industrial and logistics sectors.

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Section: /9mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers

Romero

Osaba

Villar-Rodríguez

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many researchers have suggested various approximations or heuristic algorithms [ 2 , 5 ] due to the difficulties in achieving optimum bin packing problem solutions, including integer linear programming [ 9 ], space minimizing heuristics [ 10 ], genetic algorithms [ 11 , 12 ], quantum algorithms [ 13 , 14 ] and machine-learning-based smart heuristic selection [ 15 ]. Solving bin packing in a 3D environment is not an easy task.…”

Section: Literature Surveymentioning

confidence: 99%

BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems

Murdivien,

2023

Sensors

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Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning training, which allows visualization of view learning and result processes more intuitively than other tools, as well as a physical engine for a more realistic problem-solving environment. The present research demonstrates that a Deep Reinforcement Learning model can effectively address the real-time sequential 3D bin packing problem by utilizing a game engine to visualize the environment. The results indicate that this approach holds promise for tackling complex logistical challenges in dynamic settings.

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QOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems

Osaba,

Villar-Rodriguez

2023

Benchmarks and Hybrid Algorithms in Optimization and Applications

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Hybrid quantum-classical heuristic for the bin packing problem

Cited by 8 publications

References 49 publications

Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers

Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers

BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems

QOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems

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