Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem

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

doi:10.1109/ssci51031.2022.10022156

Cited by 8 publications

(4 citation statements)

References 26 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 . Besides those two papers, an additional study formulates an atomic energy industry related problem as a 1dBPP, solving it using the D-Wave quantum annealer 22 .…”

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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“…At the time of writing this paper, two end-to-end QUBO models have been proposed for BPP, namely the Pseudo-Polynomial formulation 33 and the Unbalanced Penalization approach 34 . Alternatively, another existing approach 35 addresses the BPP through a hybrid approach, using quantum annealing to solve the sub-problem of filling a single bin, with the chance of reaching a sub-optimal solution.…”

Section: Related Workmentioning

confidence: 99%

QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing

Cellini,

Macaluso,

Lombardi

2023

Preprint

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The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions. Conversely, recent advancements in quantum technologies have shown promising potential for achieving substantial computational speedup, particularly in certain problem classes, such as combinatorial optimization. In this study, we introduce QAL-BP, a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation designed specifically for bin packing and suitable for quantum computation. QAL-BP utilizes the augmented Lagrangian method to incorporate the bin packing constraints into the objective function while also facilitating an analytical estimation of heuristic, but empirically robust, penalty multipliers. This approach leads to a more versatile and generalizable model that eliminates the need for empirically calculating instance-dependent Lagrangian coefficients, a requirement commonly encountered in alternative QUBO formulations for similar problems. To assess the effectiveness of our proposed approach, we conduct experiments on a set of bin-packing instances using a real Quantum Annealing device. Additionally, we compare the results with those obtained from two different classical solvers, namely simulated annealing and Gurobi. The experimental findings not only confirm the correctness of the proposed formulation but also demonstrate the potential of quantum computation in effectively solving the bin-packing problem, particularly as more reliable quantum technology becomes available.

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Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem

Cited by 8 publications

References 26 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

QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing

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