Fig 8 - uploaded by Jay Kang
Content may be subject to copyright.
Source publication
The three-dimensional bin packing problem is a practical problem faced in modern industrial processes such as container ship loading, pallet loading, plane cargo management, and warehouse management. This article considers a three-dimensional bin packing problem in which objects of various volumes are packed into a single bin to maximize the number...
Context in source publication
Context 1
... of box insertion before implement- ing computationally intensive processes, such as checking for intersections and local search methods. I-DBLF's packing strategy performs an additional iteration of the list of spaces after the initial packing area is selected. Conditions for remov- ing infeasible space objects are applied for optimization. Fig. 8 shows a flowchart of the I-DBLF packing ...
Similar publications
Horizontal sectional loads (horizontal shear force force and horizontal bending moment) and torsional moment are more difficult to predict with potential flow methods than vertical loads, especially in stern-quartering waves. Accurate computation of torsional moment is especially important for large modern container ships. The 3D seakeeping code GL...
Citations
... Gonçalves et al. [5] introduced a biased random-key genetic algorithm, which optimizes the packing sequence and parameters of boxes by combining a new placement procedure with a random-key-based genetic algorithm, effectively addressing both 2D and 3D packing problems. Kang et al. [6] combined knowledge guidance and heuristic reasoning to propose an improved grouped genetic algorithm for solving the three-dimensional packing problem. They enhanced algorithm performance by controlling gene transmission while maintaining a balance between selection pressure and population diversity. ...
Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm’s global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications.
... The 3D bin packing problem can be formulated as follows 27,28 : given a set of n three-dimensional items, each with width w i , height h i , and depth d i , and a set of identical three-dimensional bins, each with a fixed width W, height H, and depth D, the objective is to find a packing assignment that minimizes the number of bins used subject to the following constraints: (1) each item can only be packed once; (2) the total volume of the packed items in each bin cannot exceed the volume of the bin; (3) the orientation of each item is fixed, and it cannot be rotated or reflected. In other words, the problem can be formulated as an integer programming problem: which subject to: ...
The 3D bin packing problem is a challenging combinatorial optimization problem with numerous real-world applications. In this paper, we present a novel approach for solving this problem by integrating a generative adversarial network (GAN) with a genetic algorithm (GA). Our proposed GAN-based GA utilizes the GAN to generate high-quality solutions and improve the exploration and exploitation capabilities of the GA. We evaluate the performance of the proposed algorithm on a set of benchmark instances and compare it with two existing algorithms. The simulation studies demonstrate that our proposed algorithm outperforms both existing algorithms in terms of the number of used bins while achieving comparable computation times. Our proposed algorithm also performs well in terms of solution quality and runtime on instances of different sizes and shapes. We conduct sensitivity analysis and parameter tuning simulations to determine the optimal values for the key parameters of the proposed algorithm. Our results indicate that the proposed algorithm is robust and effective in solving the 3D bin packing problem. The proposed GAN-based GA algorithm and its modifications can be applied to other optimization problems. Our research contributes to the development of efficient and effective algorithms for solving complex optimization problems, particularly in the context of logistics and manufacturing. In summary, the proposed algorithm represents a promising solution to the challenging 3D bin packing problem and has the potential to advance the state-of-the-art in combinatorial optimization.
... The BPP has a wide range of applications in many fields. Some examples include: reducing material cost in both additive and subtractive manufacturing (for arranging object placement in 3D printing or cutting sheet material) [17,18], reducing costs in transportation (for arranging packages in shipping containers) [14], and warehouse optimization (for optimal placement and storage of goods) [21]. ...
The Bin Packing Problem (BPP) is an optimization problem where a number of objects are placed within a finite space. This problem has a wide range of applications, from improving the efficiency of transportation to reducing waste in manufacturing. In this paper, we are considering a variant of the BPP where irregular shaped polygons are required to be placed as close to the center as possible. This variant is motivated by its application in 3D printing, where central placement of the objects improves the printing reliability. To find (near) optimum solutions to this problem, we employ Evolutionary Algorithms, and propose several heuristics. We show how these heuristics interact with each other, and their most effective configurations in providing the best solutions.
... In recent years, most scholars use heuristic algorithms to solve the bin packing problem(BPP) [12][24] [11]. However, there is a problem with the cold start. ...
... Alvim et al. used lower bounding strategies to propose a hybrid improvement procedure for the BPP [1]. Kyungdaw Kang et al. proposed a hybrid genetic algorithm with a variant of the deepest bottom left packing strategy to solve the 3D-BPP and compared it with others [11]. Andreas Bortfeldt et al. proposed a hybrid genetic algorithm(GA) based on an integrated greedy heuristic and proved its good performance [3]. ...
The online 3D bin packing problem(3D-BPP) is widely used in the logistics industry and is of great practical significance for promoting the intelligent transformation of the industry. The heuristic algorithm relies too much on manual experience to formulate more perfect packing rules. In recent years, many scholars solve 3D-BPP via deep reinforcement learning(DRL) algorithms. However, they ignore many skills used in manual packing, one of the most important skill is workers put the item aside if the item is packed improperly. Inspired by this skill, we propose a DRL algorithm with a buffer zone. Firstly, we define the wasted space and the buffer zone. And then, we integrate them into the DRL algorithm framework. Importantly, we compare the bin utilization with di erent thresholds of wasted space and di erent buffer zone sizes. Experimental results show that our algorithm outperforms existing heuristic algorithms and DRL algorithms.
... The genetic algorithm's stochastic nature, as highlighted by Goldberg [5], increases the likelihood of discovering global solutions, making it an efficient and robust approach to finding optimal solutions. Sarih et al. [6] emphasize the usefulness of genetic algorithms in the context of vehicle loading optimization problems, as they can produce valuable solutions that ensure adequate load distribution and optimal utilization of vehicle capacity. Furthermore, the adaptable nature of genetic algorithms allows them to effectively handle diverse vehicle types, dimensions, and capacity restrictions, demonstrating their great potential in solving vehicle loading optimization problems. ...
... In a study conducted by Kyungdaw Kang et al. [6], they aimed to optimize the loading arrangement for vehicles and containers using a genetic algorithm-based approach. In this study, the genetic algorithm aimed to optimize the arrangement of loads, weight distribution, and volume utilization to reduce logistics costs and transportation times. ...
... In this study, the genetic algorithm aimed to optimize the arrangement of loads, weight distribution, and volume utilization to reduce logistics costs and transportation times. This method presents significant potential for increasing operational efficiency and sustainability in the logistics sector [6]. ...
Speed is a crucial evaluation criterion in the cargo transportation industry. The processes carried out during transfers before cargo reaches the destination unit are vital for timely delivery. Time spent at cargo distribution centers should be minimized, the maximum number of packages should be transported in a single action between transfers, and cargo vehicles should be used as accurately and efficiently as possible. Minimizing unused space inside the vehicles and ensuring that vehicles at cargo transfer centers are arranged correctly and efficiently for faster departures and increased cargo capacity are of great importance. In pursuit of this goal, the present research concentrates on optimizing the loading of vehicle interiors. This investigation proposes a method that determines the optimal dimensions of packages to be loaded within the vehicle, with the intention of maximizing the number of cargo packages while minimizing the interior volume used. A preliminary implementation of the suggested approach has been created. This developed prototype application can assist cargo transportation companies in planning their cargo loading and optimizing the utilization of vehicle interiors. The efficacy of the prototype application was evaluated using a representative vehicle loading dataset, and the results demonstrate its success.
... The problem of optimizing the position of boxes inside a bigger container is a well-known problem in the field operational research, named the bin packing problem or three-dimensional bin packing problem. The usage of a genetic algorithm (GA) has been addressed by many researchers as the optimal solution for this type of problem, also applied to spacecraft design [38][39][40][41][42][43][44][45]. ...
CubeSats have a 65% success rate. Failures derive from design mistake or components malfunctions. To improve the success rate, Technical University of Dresden and FHWN (University of Applied Sciences Wiener Neustadt) developed GREATCUBE+, a software tool for the conceptual design of CubeSats. Its layered structure comprises three levels: empirical, where successfully flown missions are used for an initial tradeoff; analytical, where a design refinement is performed; and numerical, for the final assessment of the proposed architecture. The tool provides teams with information related to commercial off-the-shelf products which will satisfy the mission requirements. To turn this software into a universally applicable tool, it is possible to perform the design of CubeSat mission with many payload’s typologies such as attitude determination and control subsystem, telemetry telecommunication and command, onboard computer, propulsion unit and technology demonstration or scientific payloads. GREATCUBE+ has been validated using the information of existing CubeSats as baseline for its simulation. The achievable accuracy when comparing the simulated outcomes and the real design is of almost 100% for volumes, 90% for masses, and 80% for power generation. By implementing this tool during the conceptual development phase, it is hoped that teams could benefit in reliability thanks to the usage of flight proven equipment recommended via GREATCUBE+ together with a quicker development time.
... Some exact methods, e.g., branch-and-bound [20], try to reduce the search space for the optimal solution. Instead of exhaustively searching the whole solution space, meta-heuristic methods such as the genetic algorithm [22], [16], [15], tabu search [5], simulated annealing [18], and integer linear programming [17], [14] search for sub-optimal solutions. Yet, these methods require trying multiple packing sequences and thus are still computationally expensive. ...
... Compared with a genetic-algorithm-based method. Also, we compare our method on 200 random packing cases with [15], which uses a genetic algorithm to re-plan the whole packing sequence. In short, the method adopts DBL to suggest each placement location and uses the genetic algorithm to find the order. ...
Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings.
... This simplification is a well-known problem in operations research (OR) which is called bin packing problem (BPP). From a literature review performed [29][30][31][32][33][34][35][36], it emerged that the BPP with a genetic algorithm (GA) is a promising approach for the application on GNL. Additionally, the algorithm necessary for the subsystem position placement of GNL needs to take into account an extra feature: the payload position. ...
CubeSats are a type of spacecraft which have become popular since the early 2000. They are known for their quick development time and low cost, when comparing them to larger satellites. However, there is a significant drawback which has been recorded during these years of operations, namely an high failure rate which turns almost half of them into space debris. The reasons behind these malfunctions are often attributed to flawed spacecraft design choices or failures of commercial off-the-shelf (COTS) products. To improve the design of CubeSats, several software tools for performing the conceptual design have been proposed. These tools are often limited in their capabilities and are not suitable for all types of CubeSat missions. To address this issue, the University of Applied Sciences Wiener Neustadt (FHWN) in cooperation with Technische Universität Dresden is developing a software tool called GREATCUBE+. Its goal is to increase the success rate of CubeSats by providing a satellite model which is composed of commercial-off-the-shelf (COTS) products backed up by empirical heritage, analytical proof, and numerical analysis. One of the main features of this tool is the ability of dealing with different typologies of payloads. GREATCUBE+ has been validated with various successful CubeSat missions and it provides design solutions with an accuracy of above 90% when it comes to CubeSat weights and volumes. Using this software, CubeSat design teams can proceed from the conceptual development to the testing and assembly phases quicker, hoping to result in higher quality CubeSats and fewer failures.
... Among them, threedimensional bin packing problem is one of the most concerned combinatorial optimization problems Sridharan and Domnic [2]. The main goal is to find a space allocation method that can achieve the highest space utilization or the lowest cost [3], which can be divided into container packing problem, box packing problem, and back packing problem (Kyungdaw et al. [4]; Mhand et al. [5]). ...
... If the length, width, and height of the cargos are l, w, and h, respectively, and the length, width, and height of the box are x, y, and z, then there are six states of relative rotation between the two, i.e., ðx − l, y − w, z − hÞ, ðx − l, y − h, z − wÞ, ðx − w, y − l, z − hÞ, ðx − w, y − h, z − lÞ, ðx − h, y − l, z − wÞ, and ðx − h, y − w, z − lÞ. The volume constraint that the cargo in the rotating state should satisfy is shown in equation (4). The rotation constraints for the other five rotation states are similar to ...
The box packing problem can be generalized as placing a batch of cargos with a specified number of different physical characteristics into a specified box. Suppose that a batch of cuboid cargos of different sizes are to be loaded into a batch of boxes of the same type, the cargos have constraints such as orientation and stability. Taking the mean value of the reciprocal of space utilization as the objective function, this paper designs a hybrid genetic algorithm that combines genetic algorithm and tabu search algorithm. Aiming at the information of the packing sequence and the rotating state of the box in the packing scheme, a two-stage real number encoding method and decoding method based on random keys are designed, and a crossover operation based on partial random keys and uniform crossover is designed. In order to convert the solution searched by the optimization algorithm into the actual packing scheme, a heuristic loading algorithm is designed while using the positioning rule of the lower left corner, the space selection rule of the minimum space, and the division and merging rules of the remaining space. In the early stage, the roulette method was used to strengthen the global search ability, and in the later stage, the optimal preservation strategy was used to speed up the algorithm convergence. To make up for the shortcomings of the genetic algorithm’s weak local search ability and slow convergence speed, the tabu search algorithm was used as a mutation operation in the genetic algorithm. The solution in the generation is used as the initial solution of the tabu search algorithm, and the search process is carried out. Finally, this paper tests the proposed hybrid algorithm on 6 groups of weakly heterogeneous and strongly heterogeneous data in the BR dataset. The results prove that the proposed algorithm can reduce the usage of boxes.
... Since the emergence of the cutting inventory problem in the 1960s, the solution to the threedimensional loading problem has developed rapidly. Many researchers have proved that the hybrid genetic algorithm, heuristic method, and differential evolution (DE) algorithm can solve the heterogeneous material loading problem [1][2][3]. To increase the practicability of the loading algorithm, some scholars have studied the relationship between the constraints related to the box direction, load stability, and box separation, which greatly reduces the variables and constraints contained in the mathematical model and increases the solution efficiency [4][5][6]. ...
In an attempt to solve the problems of the low intelligent distribution degree and high working intensity of auxiliary transportation systems in underground coal mines, an intelligent distribution strategy of materials in the whole mine is put forward. Firstly, combined with the characteristics of materials and standard containers, a three-dimensional loading model is established with the goal of maximizing the space utilization of standard containers, and a three-dimensional space segmentation heuristic algorithm is used to solve the material loading scheme. Then, the multi-objective optimization model of distribution parameters is established with the goal of the shortest delivery distance, the shortest delay time, and the fewest number of delivery vehicles, and the dual-layer genetic algorithm is used to solve the distribution scheme. Finally, the spatiotemporal conversion coefficient is designed to solve the task list by hierarchical clustering, and the solution time is reduced by 30%. The results show that the dual-layer genetic algorithm based on hierarchical clustering has good adaptability in complex material scheduling scenarios.
