Packing Optimization Research Articles

Level-set-based partitioning and packing optimization of a printable model

As the 3D printing technology starts to revolutionize our daily life and the manufacturing industries, a critical problem is about to e-merge: how can we find an automatic way to divide a 3D model into multiple printable pieces, so as to save the space, to reduce the printing time, or to make a large model printable by small printers. In this paper, we present a systematic study on the partitioning and packing of 3D models under the multi-phase level set framework. We first construct analysis tools to evaluate the qualities of a partitioning using six metrics: stress load, surface details, interface area, packed size, printability, and assembling. Based on this analysis, we then formulate level set methods to improve the qualities of the partitioning according to the metrics. These methods are integrated into an automatic system, which repetitively and locally optimizes the partitioning. Given the optimized partitioning result, we further provide a container structure modeling algorithm to facilitate the packing process of the printed pieces. Our experiment shows that the system can generate quality partitioning of various 3D models for space saving and fast production purposes.

Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm

In this paper, we try to solve 3D offline packing optimization problem by combining two methods-genetic algorithm’ global performance and simulated annealing algorithm’ local performance. Given Heuristic rules in loading conditions, we use the optimal preservation strategy and the roulette wheel method to choose selection operator, integrating simulated annealing algorithm into genetic algorithm , and achieving code programming and algorithms by Matlab.This paper carries out an actual loading in a vehicle company in Changchun City, then makes a contrast between the final optimization results and each suppliers’ current packing data.The experimental results show that the algorithm has a certain validity and practicability in multiple container packing problem.

Equilibrium orientations of non-spherical and chemically anisotropic particles at liquid–liquid interfaces and the effect on emulsion stability

The effective stabilization of emulsions by solid particles, a phenomenon known as Pickering stabilization, is well known to be highly dependent on the wettability and the adhesion energy of the stabilizer employed at the liquid–liquid interface. We present a user-friendly computational model that can be used to determine equilibrium orientations and the adhesion energy of colloidal particles at interfaces. The model determines the free energy profile of particle adsorption at liquid–liquid interfaces using a triangular tessellation scheme. We demonstrate the use of the model, using a variety of anisotropic particles and demonstrate its ability to predict and explain experimental observations of particle behaviour at interfaces. In particular, we show that the concept of hydrophilic lipophilic balance commonly applied to molecular surfactants is insufficient to explain the complexity of the activity of colloidal particles at interfaces. In addition, we show the importance of the knowledge of the free energy adsorption profile of single particles at interfaces and the impact on overall free energy of emulsification of packed ensembles of particles. The delicate balance between optimization of adhesion energy, adsorption dynamics and particle packing is shown to be of great importance in the formation of thermodynamically stable emulsions. In order to use the model, the code is implemented by freely available software that can be readily deployed on personal computers.

CFD Simulation and Optimization of Winpak-based Modular Catalytic Structured Packing

A recently designed packing sheet Winpak was assembled with catalysts in a modular catalytic structured packing (MCSP) column. A combined mesoscale–microscale methodology was used to analyze and determine the pressure drop mechanism in the MCSP. The proposed methodology differentiates the pressure drop into six different principles, which was determined and compared using computational fluid dynamics (CFD). The three major causes are gas flow confluence and diffluence through the packing windows, the sudden change in the effective gas channel area and the wall effect. Inspired by constructal theory, the height of packing sheets loaded with catalysts was reduced, which resulted in a less abrupt gas channel contraction and expansion at the packing layer junction and more favorable for the undisturbed gas flow. The optimized overall pressure drop was reconstructed in Fluent and was validated by both dry and irrigated packing experiments. Furthermore, irrigated packing experiments were also conducted. Result ...

Charge carrier mobilities in organic semiconductors: crystal engineering and the importance of molecular contacts.

We have conducted a combined experimental and theoretical study on the optimization of hexa-peri-hexabenzocoronene (HBC) as organic semiconductor. While orientations with high electronic coupling are unfavorable in the native liquid crystalline phase of HBC, we enforced such orientations by applying external constraints. To this end, self-assembled monolayers (SAMs) were formed by a non-conventional preparation method on an Au-substrate using electrochemical control. Within these SAMs the HBC units are forced into favorable orientations that cannot be achieved by unconstrained crystallization. For simulating the charge transport we applied a recently developed approach, where the molecular structure and the charge carrier are propagated simultaneously during a molecular dynamics simulation. Experiments as well as simulations are mutually supportive of an improved mobility in these novel materials. The implication of these findings for a rational design of future organic semiconductors will be discussed.

Face Recognition Using Fuzzy Clustering and Kernel Least Square

Over the last fifteen years, face recognition has become a popular area of research in image analysis and one of the most successful applications of machine learning and understanding. To enhance the classification rate of the image recognition, several techniques are introduced, modified and combined. The suggested model extracts the features using Fourier-Gabor filter, selects the best features using signal to noise ratio, deletes or modifies anomalous images using fuzzy c-mean clustering, uses kernel least square and optimizes it by using wild dog pack optimization. To compare the suggested method with the previous methods, four datasets are used. The results indicate that the suggested methods without fuzzy clustering and with fuzzy clustering outperform state- of-art methods for all datasets.

Stabilization of Ostwald ripening in low molecular weight amino lipid nanoparticles for systemic delivery of siRNA therapeutics.

Lipid nanoparticles (LNPs) represent the most clinically advanced technology for the systemic delivery of therapeutic siRNA in vivo. Toward this end, a novel class of LNPs comprising low molecular weight (MW) ionizable amino lipids having asymmetric architecture was recently reported.1 LNPs of these amino lipids, termed asymmetric LNPs, were shown to be highly efficacious and well tolerated in vivo; advances were enabled by improved endosomal escape, coupled with enhanced amino lipid metabolism and clearance. In this work, we show that, in contrast to their desirable pharmacological performance, asymmetric LNPs present a significant pharmaceutical developability challenge, namely physical instability limiting extended shelf life. Using orthogonal characterization methods, we identify the mechanism of LNP instability as Ostwald ripening and establish it to be driven predominantly by the asymmetric amino lipid component. Through rational optimization of LNP physical and macromolecular properties, we are able to significantly attenuate or entirely eliminate the Ostwald ripening instability. Modulation of LNP size, for example, effectively halts particle growth. Similarly, optimization of LNP macromolecular packing through deliberate selection of structurally matched colipids significantly diminishes the rate of ripening. This later experimental observation is substantiated by molecular dynamics simulations of LNP self-assembly, which establish a quantitative dependence of LNP macromolecular order on colipid structure. In totality, the experimental and molecular dynamics outcomes of this work support the rational design of LNP physical and chemical properties leading to effective Ostwald ripening stabilization and enable the advance of asymmetric LNPs as a clinic-ready platform for siRNA therapeutics.

Heuristic Algorithm for Constrained 3D Container Loading Problem: A Genetic Approach

This paper presents an heuristic Genetic Algorithm for solving 3-Dimensional Single container packing optimization problem. The 3D container loading problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and inturn profit. Furthermore, various practical constraints like box orientation, stack priority, container stability, etc also applied. Boxes to be packed are of various sizes and of heterogeneous shapes. In this research work, several heuristic improvements were proposed over Genetic Algorithm (GA) to solve the container loading problem that significantly improves the search efficiency and to load most of heterogeneous boxes into a container along with the optimal position of loaded boxes, box orientation and boxes to be loaded by satisfying practical constraints. In this module, both the guillotine and non-guillotine moves were allowed. In general, these heuristic GA solutions being substantially better and satisfactory than those obtained by applying heuristics to the bin packing directly.

Intelligent 3D packing using a grouping algorithm for automotive container engineering

Abstract Storing, and the loading and unloading of materials at production sites in the manufacturing sector for mass production is a critical problem that affects various aspects: the layout of the factory, line-side space, logistics, workers' work paths and ease of work, automatic procurement of components, and transfer and supply. Traditionally, the nesting problem has been an issue to improve the efficiency of raw materials; further, research into mainly 2D optimization has progressed. Also, recently, research into the expanded usage of 3D models to implement packing optimization has been actively carried out. Nevertheless, packing algorithms using 3D models are not widely used in practice, due to the large decrease in efficiency, owing to the complexity and excessive computational time. In this paper, the problem of efficiently loading and unloading freeform 3D objects into a given container has been solved, by considering the 3D form, ease of loading and unloading, and packing density. For this reason, a Group Packing Approach for workers has been developed, by using analyzed truck packing work patterns and Group Technology, which is to enhance the efficiency of storage in the manufacturing sector. Also, an algorithm for 3D packing has been developed, and implemented in a commercial 3D CAD modeling system. The 3D packing method consists of a grouping algorithm, a sequencing algorithm, an orientating algorithm, and a loading algorithm. These algorithms concern the respective aspects: the packing order, orientation decisions of parts, collision checking among parts and processing, position decisions of parts, efficiency verification, and loading and unloading simulation. Storage optimization and examination of the ease of loading and unloading are possible, and various kinds of engineering analysis, such as work performance analysis, are facilitated through the intelligent 3D packing method developed in this paper, by using the results of the 3D model.

The Statistical Mechanics of Random Set Packing and a Generalization of the Karp-Sipser Algorithm

We analyse the asymptotic behaviour of random instances of the maximum set packing (MSP) optimization problem, also known as maximum matching or maximum strong independent set on hypergraphs. We give an analytic prediction of the MSPs size using the 1RSB cavity method from statistical mechanics of disordered systems. We also propose a heuristic algorithm, a generalization of the celebrated Karp-Sipser one, which allows us to rigorously prove that the replica symmetric cavity method prediction is exact for certain problem ensembles and breaks down when a core survives the leaf removal process. The e-phenomena threshold discovered by Karp and Sipser, marking the onset of core emergence and of replica symmetry breaking, is elegantly generalized to Cs=e/(d-1) for one of the ensembles considered, where d is the size of the sets.

Packing Different Cuboids with Rotations and Spheres into a Cuboid

The paper considers a packing optimization problem of different spheres and cuboids into a cuboid of the minimal height. Translations and continuous rotations of cuboids are allowed. In the paper, we offer a way of construction of special functions (Φ-functions) describing how rotations can be dealt with. These functions permit us to construct the mathematical model of the problem as a classical mathematical programming problem. Basic characteristics of the mathematical model are investigated. When solving the problem, the characteristics allow us to apply a number of original and state-of-the-art efficient methods of local and global optimization. Numerical examples of packing from 20 to 300 geometric objects are given.

A global optimization approach for solving three-dimensional open dimension rectangular packing problems

The three-dimensional open dimension rectangular packing problem (3D-ODRPP) aims to pack a set of given rectangular boxes into a large rectangular container of minimal volume. This problem is an important issue in the shipping and moving industries. All the boxes can be any rectangular stackable objects with different sizes and may be freely rotated. The 3D-ODRPP is usually formulated as a mixed-integer non-linear programming problem. Most existing packing optimization methods cannot guarantee to find a globally optimal solution or are computationally inefficient. Therefore, this paper proposes an efficient global optimization method that transforms a 3D-ODRPP as a mixed-integer linear program using fewer extra 0–1 variables and constraints compared to existing deterministic approaches. The reformulated model can be solved to obtain a global optimum. Experimental results demonstrate the computational efficiency of the proposed approach in globally solving 3D-ODRPPs drawn from the literature and the practical applications.

Particle Size Distribution of Limestone Fillers: Granulometry and Specific Surface Area Investigations

Mineral fillers can be defined as “inert materials included in a mix design for some useful purpose” (NF P18-508 Janvier 2012). They can be added to compounds in order to complete a large variety of final properties without increasing costs or to improve specific characteristics like hardness, brittleness, impact strength, compressive strength, softening point, fire resistance, surface texture, electrical conductivity, and so on. In Belgium, locally available limestone fillers are specifically very well adapted for the optimization of particle packing and flow behavior of cementitious pastes in concrete mixes. Limestone fillers may be easily characterized in terms of chemical and mineralogical properties. These properties are fundamental for the study of the behavior of concrete mixes in fresh state and for understanding interactions existing at the level of the interfacial transition zone between aggregates and cement paste. These properties are however insufficiently discriminant and particle size, as well as shape distribution, seem to have a potential influence on physical phenomena which happen during the setting process. The aim of this article is to compare five major techniques used to quantify the size and the shape of limestone fillers particles: laser diffraction scattering, wet sieving, and image analysis for particle size measurement; and BET adsorption and Blaine permeability methods for specific surface area.

A New Variation of Adaptive Simulated Annealing for 2D/3D Packing Optimization

A New Variation of Adaptive Simulated Annealing for 2D/3D Packing Optimization

A New Variation of Adaptive Simulated Annealing for 2D/3D Packing Optimization

A New Variation of Adaptive Simulated Annealing for 2D/3D Packing Optimization

Performance Analysis of Selective Breeding Algorithm on One Dimensional Bin Packing Problems

The bin packing optimization problem packs a set of objects into a set of bins so that the amount of wasted space is minimized. The bin packing problem has many important applications. The objective is to find a feasible assignment of all weights to bins that minimizes the total number of bins used. The bin packing problem models several practical problems in such diverse areas as industrial control, computer systems, machine scheduling, VLSI chip layout and etc. Selective breeding algorithm (SBA) is an iterative procedure which borrows the ideas of artificial selection and breeding process. By simulating artificial evolution in this way SBA algorithm can easily solve complex problems. One dimensional bin packing benchmark problems are taken for evaluating the performance of the SBA. The computational results of SBA algorithm show optimal solution for the tested benchmark problems. The proposed SBA algorithm is a good problem-solving technique for one dimensional bin packing problems.

Packaging Structure Optimization Design Based on Low-Carbon Packaging Idea

The present situation and existing problems of low carbon package as the high energy consumption, over-packaging and low recovery rate etc., have been analyzed in this paper. To realize minimizing the comprehensive carbon emission of packaging in the process of circulation, the low carbon consumption logistics strategy in the process of transportation, warehousing, loading and unloading, the consumption packaging strategy of simply and humanization in the process of consuming or using the product, and the principle of green design after discarded have been put forward based on the research on the structure optimization of low carbon packing in the whole life cycle according to the characteristics such as reduction, low energy consumption, low pollution and recyclable use etc and effective way of low carbon packing.

Mathematical modeling of the interaction of non-oriented convex polytopes

An ?-function for two non-oriented convex polytopes is set up. The ?-function can be used to construct a mathematical model of packing optimization problem for non-oriented polytopes. An example of an ?-function for two non-oriented parallelepipeds is given.

High density packings of equal circles in rectangles with variable aspect ratio

Arranging a fixed number n of equal non-overlapping circles in a rectangle with variable aspect ratio is a non-standard packing problem. It arises if one has to decide how a certain number of circular items should be packed into a rectangular box when no assumption is made on the shape of the box. How must the box be designed to achieve the maximum packing density? This special problem was investigated by Lubachevsky and Graham in 2003, where they classified record packings for n≤213. However, their work lacks a precise treatment of the closure of observed vacancies as well as any numerical data of the best arrangements found.This article introduces a shorter notation for hexagonal dense packings in rectangular containers. Furthermore, it describes a deterministic procedure for the optimization of packings that differ from usual regular hexagonal patterns by one or more vacancies, without relying on simulation programs. A couple of improvements were found (for n=37, 101, 146, 169) and numerical values are given for all packings in the extended range 1≤n≤500 (except for n=393, 411, 421, 453). Numerical results are also available at www.packomania.com/crc_var/.

Postscheduling buffer management trade-offs in streaming software synthesis

Streaming applications, which are abundant in many disciplines such as multimedia, networking, and signal processing, require efficient processing of a seemingly infinite sequence of input data. In the context of streaming software synthesis from data flow graphs, we study the inherent trade-off between memory requirement and compilation runtime, under a given task firing schedule. We utilize postscheduling analysis granularity to control the amount of details in characterization of buffer's spatio-temporal footprints. Subsequently, we transform the buffer allocation problem to two-dimensional packing of polygons, where complexity of the packing problem (e.g., polygon shapes) is determined by the analysis granularity. We develop an evolutionary packing optimization algorithm which readily yields buffer allocations. Experimental results highlight the trade-off between complexity of the analysis and the total buffer size of generated implementations. In addition, they show dramatic improvements in total buffer size, if one is willing to pay the additional cost in optimization runtime.