Planning in logistics

Kolhe, Pushkar; Christensen, Henrik I.

doi:10.1145/2377576.2377586

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The 3D BPP is a real-world-driven combinatorial optimization problem with significant economic, environmental, and safety implications [ 7 ]. The geometric 3D BPP is a variation of the knapsack problem and is a well-known NP-hard problem in computer science literature [ 8 ]. The purpose is to create a packing method for boxes of various shapes and sizes that maximizes the space within the bin.…”

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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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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“…Solutions to bin-packing problems: The online version of the 3D bin-packing problem (RT-3D-BPP) is commonly found in the warehouse parcel loading context, and has been formulated as a knapsack problem (Kolhe and Christensen 2010). The survey (Kolhe and Christensen 2010) is limited to the description of the problem and benchmarks that are used to compare different algorithms. A more detailed study of n-dimensional BPP (Christensen et al 2016) describes state-of-the-art algorithms and their 'competitive ratio'.…”

Section: Related Workmentioning

confidence: 99%

“…The geometric three-dimensional bin-packing problem is a known NP-hard problem in computer science literature, and is a variant of the knapsack problem (Kolhe and Christensen 2010). The goal is to define a packing strategy for boxes of different shape and size such that the space inside the bin is maximally utilized (volume efficiency version of the objective).…”

Section: Introductionmentioning

confidence: 99%

A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing

Verma¹,

Singhal²,

Khadilkar³

et al. 2020

Preprint

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We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on producing decisions that can be physically implemented by a robotic loading arm, a laboratory prototype used for testing the concept. The problem considered in this paper is novel in two ways. First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori. Instead, a fixed number of upcoming objects is visible to the loading system, and they must be loaded in the order of arrival. Second, the goal is not to move objects from one point to another via a feasible path, but to find a location and orientation for each object that maximises the overall packing efficiency of the bin(s). Finally, the learnt model is designed to work with problem instances of arbitrary size without retraining. Simulation results show that the RLbased method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.

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Analysis of Operational Efficiency in Picking Activity on a Pipes and Fittings Company

Silva

Moresco²

2019

Advances in Manufacturing, Production Management and Process Control

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Planning in logistics

Cited by 6 publications

References 28 publications

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

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

A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing

Analysis of Operational Efficiency in Picking Activity on a Pipes and Fittings Company

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