Designing a Flexible Evaluation of Container Loading Using Physics Simulation

Nishiyama, Shuhei; Lee, Chonho; Mashita, Tomohiro

doi:10.1007/978-3-030-41913-4_21

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In the literature, the general approach for integrating this constraint is to decide an upper limit on the weight an item can support for a given orientation [21]- [23]. However, in many cases, the load limits are simply not known and, even in realworld applications, the general rule of not placing heavier items on top of lighter or fragile ones is usually used as an approximation to the stacking constraint [11], [15], [28], [30], [39], [40].…”

Section: A Constraintsmentioning

confidence: 99%

A Multi-Heuristic Algorithm for Multi-Container 3-D Bin Packing Problem Optimization Using Real World Constraints

Ananno,

Ribeiro

2024

IEEE Access

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With the growing demand for sustainable and optimal packaging solutions, this study proposes a novel two-stage algorithm for the multi-container three-dimensional bin packing problem. The research addresses this problem within the context of a real-world industrial scenario and implements several practical constraints including: full shipment, customer positioning requirements, and product geometric interlocking, for increased stability and with the purpose of minimizing the use of plastic wrapping and/or additional supporting surfaces. The main optimization target is to minimize the total number of containers used in the palletization process of custom orders with varying degrees of complexity. The proposed algorithm includes two stages/phases of processing. In the first phase, the algorithm uses constructive heuristics to generate homogeneous product layers. The layers are then stacked to produce blocks, which are then placed on individual containers or pallets. The second phase packs the leftover items using a genetic algorithm. The performance of the proposed solution is benchmarked using real-world industrial data, as well as a more classic academic benchmark. It is demonstrated, across a very large set of orders, that the algorithm always achieves solutions for full palletization of the orders. The analysis shows that the approach is generic and the quality of the solutions generated is relatively even for both small and large, homogeneous and heterogeneous problem instances.

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Section: A Constraintsmentioning

confidence: 99%

A Multi-Heuristic Algorithm for Multi-Container 3-D Bin Packing Problem Optimization Using Real World Constraints

Ananno,

Ribeiro

2024

IEEE Access

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“…This is a very important practical factor, and researchers have focused on developing models that limit the weight and pressure that a carton container can withstand per unit area [13,14]. In addition, other researchers such as Nishiyama et al [15] and Paquay et al [16] have expanded their research directions by limiting the stacking of heavy containers on light containers and not placing fragile items on top of other fragile items.…”

Section: Bin Packing Problemmentioning

confidence: 99%

“…This constraint ensures that the containers are securely standing on the container floor and the surfaces of other boxes [22]. Dynamic stability (in the horizontal direction) is relevant when containers are in motion, experiencing forces such as acceleration, braking, and inertia [8,15]. As for cargo stacking positions, products with specific size and weight requirements may need to be positioned on the floor or in designated spots within the container [23].…”

Section: Bin Packing Problemmentioning

confidence: 99%

Optimizing Container Fill Rates for the Textile and Garment Industry Using a 3D Bin Packing Approach

Thi Xuan Hoa

2024

HighTech. Innov. J.

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The scarcity of empty containers presents a significant logistical challenge globally. To address this issue, this study proposes the application of the optimal box arrangement in a container with a 3D bin packing problem to enhance fill rates and accommodate the complex packing criteria of the textile and garment industry. The study’s objective is to optimize box stacking into containers by considering various factors such as multiple product types, diverse box sizes, varying container sizes, and prioritizing stacking according to purchase orders (PO). In tackling the NP-hard problem with the added constraint of PO-based stacking, this study advocates employing a genetic algorithm combined with a wall-building algorithm to address practical challenges. The genetic algorithm demonstrates optimal efficacy in solving large-scale optimization problems within specified timeframes, yielding high-quality results. In addition, normalization methods are applied to convert box sizes to pallet sizes, expediting problem-solving and facilitating the selection of appropriate container sizes, namely 20- or 40-feet. The research findings indicate that the proposed method achieved a container fill rate of up to 91.67% and minimized the number of containers used. Doi: 10.28991/HIJ-2024-05-02-017 Full Text: PDF

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On-line three-dimensional packing problems: A review of off-line and on-line solution approaches

Ali

Ramos

Carravilla

et al. 2022

Computers & Industrial Engineering

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Designing a Flexible Evaluation of Container Loading Using Physics Simulation

Cited by 3 publications

References 8 publications

A Multi-Heuristic Algorithm for Multi-Container 3-D Bin Packing Problem Optimization Using Real World Constraints

A Multi-Heuristic Algorithm for Multi-Container 3-D Bin Packing Problem Optimization Using Real World Constraints

Optimizing Container Fill Rates for the Textile and Garment Industry Using a 3D Bin Packing Approach

On-line three-dimensional packing problems: A review of off-line and on-line solution approaches

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