A novel hybrid intelligence approach for 2D packing through Internet crowdsourcing

Packing problems on its current state are being utilized for wide area of industrial applications. The aim of present research is to create and implement an intelligent system that tackles the problem of 2D packing of objects inside a 2D container, such that objects do not overlap and the container area is to be maximized. The packing problem becomes easier, when regular/rectangular objects and container are used. In most of the practical situations, the usage of irregular objects comes to existence. To solve the packing problem of irregular objects inside a rectangular container, a hybrid intelligence approach is introduced in our proposed work. The combination of machine intelligence and human intelligence is referred as the hybrid intelligence or semi-automated approach in the proposed methodology. The incorporation of human intelligence in the outcome of machine intelligence is possible to obtain using the internet crowdsourcing as we wish to handle the packing problem through internet crowdsourcing involving rural people. The proposed methodology is tested on different standard data sets and it is observed that it has clear advantage over both manual as well as fully automated heuristic based methods in terms of time and space efficiency

Curve-Based Shape Matching Methods and Applications

One of the main cues we use in our everyday life when interacting with the environment is shape. For example, we use shape information to recognise a chair, grasp a cup, perceive traffic signs and solve jigsaw puzzles. We also use shape when dealing with more sophisticated tasks, such as the medical diagnosis of radiographs or the restoration of archaeological artifacts. While the perception of shape and its use is a natural ability of human beings, endowing machines with such skills is not straightforward. However, the exploitation of shape cues is important for the development of competent computer methods that will automatically perform tasks such as those just mentioned. With this aim, the present work proposes computer methods which use shape to tackle two important tasks, namely packing and object recognition. The packing problem arises in a variety of applications in industry, where the placement of a set of two-dimensional shapes on a surface such that no shapes overlap and the uncovered surface area is minimised is important. Given that this problem is NP-complete, we propose a heuristic method which searches for a solution of good quality, though not necessarily the optimal one, within a reasonable computation time. The proposed method adopts a pictorial representation and employs a greedy algorithm which uses a shape matching module in order to dynamically select the order and the pose of the parts to be placed based on the “gaps” appearing in the layout during the execution. This thesis further investigates shape matching in the context of object recognition and first considers the case where the target object and the input scene are represented by their silhouettes. Two distinct methods are proposed; the first method follows a local string matching approach, while the second one adopts a global optimisation approach using dynamic programming. Their use of silhouettes, however, rules out the consideration of any internal contours that might appear in the input scene, and in order to address this limitation, we later propose a graph-based scheme that performs shape matching incorporating information from both internal and external contours. Finally, we lift the assumption made that input data are available in the form of closed curves, and present a method which can robustly perform object recognition using curve fragments (edges) as input evidence. Experiments conducted with synthetic and real images, involving rigid and deformable objects, show the robustness of the proposed methods with respect to geometrical transformations, heavy clutter and substantial occlusion

Modelado y solución del problema del corte irregular : aplicación en la industria del cuero Colombiana

El problema del Irregular Two Dimensional Cutting Stock Problem (ITDCSP) está presente en diversas aplicaciones industriales que incluyen la confección, fabricación del calzado y la marroquinería. Debido a su naturaleza matemática NP completa, ha sido particularmente difícil de formular y resolver. Este Trabajo de Grado propone una Formulación Lineal Mixta base que sirve como base para emplear diversos procedimientos meta heurísticos para la búsqueda de soluciones cercanas al óptimo. Al respecto, se expone el uso de dos herramientas meta heurísticas, GRASP y Algoritmos Genéticos, para su solución. En estos casos se han encontrado soluciones de calidad aceptables en tiempos de ejecución razonable.The problem of Two Dimensional Irregular Cutting Stock Problem (ITDCSP) is present in various industrial applications including clothing, shoemaking and leather. Because NP complete mathematical nature, has been particularly difficult to formulate and solve. This work proposes a linear formulation Grade Mixed base that serves as the basis for meta heuristics use various procedures for finding near-optimal solutions. In this regard, we discuss the use of two tools meta heuristics, GRASP and Genetic Algorithms for settlement. In these cases, solutions have been found acceptable quality in reasonable runtimes.Magíster en Ingeniería IndustrialMaestrí

Nesting Problems : Exact and Heuristic Algorithms

Nesting problems are two-dimensional cutting and packing problems involving irregular shapes. This thesis is focused on real applications on Nesting problems such as the garment industry or the glass cutting. The aim is to study different mathematical methodologies to obtain good lower bounds by exact procedures and upper bounds by heuristic algorithms. The core of the thesis is a mathematical model, a Mixed Integer Programming model, which is adapted in each one of the parts of the thesis. This study has three main parts: first, an exact algorithm for Nesting problems when rotation for the pieces is not allowed; second, an Iterated Greedy algorithm to deal with more complex Nesting problems when pieces can rotate at several angles; third, a constructive algorithm to solve the two-dimensional irregular bin packing problem with guillotine cuts. This thesis is organized as follows. The first part is focused on developing exact algorithms. In Chapter 2 we present two Mixed Integer Programming (MIP) models, based on the Fischetti and Luzzi MIP model. We consider horizontal lines in order to define the horizontal slices which are used to separate each pair of pieces. The second model, presented in Section 2.3, uses the structure of the horizontal slices in order to lift the bound constraints. Section 2.5 shows that if we solve these formulations with CPLEX, we obtain better results than the formulation proposed by Gomes and Oliveira. The main objective is to design a Branch and Cut algorithm based on the MIP, but first a Branch and Bound algorithm is developed in Chapter 3. Therefore, we study different branching strategies and design an algorithm which updates the bounds on the coordinates of the reference point of the pieces in order to find incompatible variables which are fixed to 0 in the current branch of the tree. The resulting Branch and Bound produces an important improvement with respect to previous algorithms and is able to solve to optimality problems with up to 16 pieces in a reasonable time. In order to develop the Branch and Cut algorithm we have found several classes of valid inequalities. Chapter 4 presents the different inequalities and in Chapter 5 we propose separation algorithms for some of these inequalities. However, our computational experience shows that although the number of nodes is reduced, the computational time increases considerably and the Branch and Cut algorithm becomes slower. The second part is focused on building an Iterated Greedy algorithm for Nesting problems. In Chapter 6 a constructive algorithm based on the MIP model is proposed. We study different versions depending on the objective function and the number of pieces which are going to be considered in the initial MIP. A new 11 idea for the insertion is presented, trunk insertion, which allows certain movements of the pieces already placed. Chapter 7 contains different movements for the local search based on the reinsertion of a given number of pieces and compaction. In Chapter 8 we present a math-heuristic algorithm, which is an Iterated Greedy algorithm because we iterate over the constructive algorithm using a destructive algorithm. We have developed a local search based on the reinsertion of one and two pieces. In the constructive algorithm, for the reinsertion of the pieces after the destruction of the solution and the local search movements, we use several parameters that change along the algorithm, depending on the time required to prove optimality in the corresponding MIP models. That is, somehow we adjust the parameters, depending on the difficulty of the current MIP model. The computational results show that this algorithm is competitive with other algorithms and provides the best known results on several known instances. The third part is included in Chapter 9. We present an efficient constructive algorithm for the two dimensional irregular bin packing problem with guillotine cuts. This problem arises in the glass cutting industry. We have used a similar MIP model with a new strategy to ensure a guillotine cut structure. The results obtained improve on the best known results. Furthermore, the algorithm is competitive with state of the art procedures for rectangular bin packing problems

Making More Cars with Less Metal

Reducing sheet metal yield losses in automotive manufacturing would reduce material demand, providing environmental and financial benefits. This thesis explores material efficiency from four perspectives: 1. The opportunity for improvement: A part by part analysis of yield losses for every sheet metal component in a vehicle highlights nine material efficiency strategies. An industry study finds that on average only 56% of sheet metal purchased is used on the vehicle. Improving material utilisation to best practice of 70% would save £8 billion and 25 million tonnes of CO2 annually. 2. The potential to realise this opportunity: A design process to improve material efficiency is trialled within an automotive manufacturer and identifies opportunities of 20%. However, only 3% improvement is realised since the material efficiency opportunity is locked in at the start of the design process, where resource is not currently allocated. Earlier consideration of material utilisation is required. 3. Material efficiency within the circular economy: All existing metrics for recycling in sheet metal forming processes are mapped onto a diagram. A case study demonstrates that existing recycling metrics, do not promote the reduction of yield losses. Considering recycling process efficiencies rather than recycled content would enable recycling rates to be measured without penalising material efficiency. 4. Design for material efficiency: There are currently no suitable tools to inform process selection and geometry decisions for material efficiency. To address this, a novel set of experiments is conducted. These experiments identify a trend between the maximum draw depth and three critical radii. This trend could form a geometry based formability guideline which would enable design for material efficiency. Approaching material utilisation from these perspectives gives greater certainty of the saving opportunity for sheet metal material efficiency and clarifies the priority of material efficiency compared to other strategies to meet global climate change goals.Sponsored by Jaguar Land Rove

A study on crate sizing, inventory and packing problem

Ph.DDOCTOR OF PHILOSOPH

On homogenization and de-homogenization of composite materials

Composite homogenization is a modeling concept that allows the description of heterogeneous materials by constitutively equivalent, homogeneous ones. The concept is universally applied to fibrous composites, resulting in many modeling approaches. But, by homogenization, the composite is voided of its physical microstructure; elements that may affect failure mechanisms physically are also voided. This fact often leads to difficulties in failure theories formulated at the homogenized material scale. De-homogenization is a reverse scheme in that the microstructure is restored, albeit locally, back in the homogenized composite. Clearly, this is done after composite homogenization and field analysis of composite structures under global loading; so the micro fields in the desired locations with restored microstructure can be recovered. The recovered micro fields may then provide the needed information for some failure theories to be formulated at the composite micro scale instead. This thesis presents a unified modeling approach for homogenization (forward) and de-homogenization (backward), applicable to unidirectional composite systems. Emphasis is placed on the uniqueness between the forward and the backward modeling processes; so the desired micro fields are truly recovered within the confines of mechanics. Micro fields in several laminates made of the UD systems are recovered; key effects that influence failure mechanisms therein are studied. An inter-scale failure theory that describes matrix cracking in laminates is then formulated, being based on the recovered micro-fields. Laminate matrix cracking in several well-documented experimental studies are simulated using the inter-scale theory. The simulation captures the major cracking characteristics that are otherwise excluded in failure theories derived at the homogenized composite scale. The general concept of homogenization/de-homogenization is applicable to all composite systems, where responses from micro-macro-global interactions are to be physically described. The approach taken in the formulation of the inter-scale theory serves as an example of both conceptual and practical importance.Ph.D., Mechanical Engineering -- Drexel University, 200

Self Assembly Problems of Anisotropic Particles in Soft Matter.

Anisotropic building blocks assembled from colloidal particles are attractive building blocks for self-assembled materials because their complex interactions can be exploited to drive self-assembly. In this dissertation we address the self-assembly of anisotropic particles from multiple novel computational and mathematical angles. First, we accelerate algorithms for modeling systems of anisotropic particles via massively parallel GPUs. We provide a scheme for generating statistically robust pseudo-random numbers that enables GPU acceleration of Brownian and dissipative particle dynamics. We also show how rigid body integration can be accelerated on a GPU. Integrating these two algorithms into a GPU-accelerated molecular dynamics code (HOOMD-blue), make a single GPU the ideal computing environment for modeling the self-assembly of anisotropic nanoparticles. Second, we introduce a new mathematical optimization problem, filling, a hybrid of the familiar shape packing and covering problem, which can be used to model shaped particles. We study the rich mathematical structures of the solution space and provide computational methods for finding optimal solutions for polygons and convex polyhedra. We present a sequence of isosymmetric optimal filling solutions for the Platonic solids. We then consider the filling of a hyper-cone in dimensions two to eight and show the solution remains scale-invariant but dependent on dimension. Third, we study the impact of size variation, polydispersity, on the self-assembly of an anisotropic particle, the polymer-tethered nanosphere, into ordered phases. We show that the local nanoparticle packing motif, icosahedral or crystalline, determines the impact of polydispersity on energy of the system and phase transitions. We show how extensions of the Voronoi tessellation can be calculated and applied to characterize such micro-segregated phases. By applying a Voronoi tessellation, we show that properties of the individual domains can be studied as a function of system properties such as temperature and concentration. Last, we consider the thermodynamically driven self-assembly of terminal clusters of particles. We predict that clusters related to spherical codes, a mathematical sequence of points, can be synthesized via self-assembly. These anisotropic clusters can be tuned to different anisotropies via the ratio of sphere diameters and temperature. The method suggests a rich new way for assembling anisotropic building blocks.Ph.D.Applied Physics and Scientific ComputingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91576/1/phillicl_1.pd

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set