Development and Integration of Geometric and Optimization Algorithms for Packing and Layout Design

The research work presented in this dissertation focuses on the development and application of optimization and geometric algorithms to packing and layout optimization problems. As part of this research work, a compact packing algorithm, a physically-based shape morphing algorithm, and a general purpose constrained multi-objective optimization algorithm are proposed. The compact packing algorithm is designed to pack three-dimensional free-form objects with full rotational freedom inside an arbitrary enclosure such that the packing efficiency is maximized. The proposed compact packing algorithm can handle objects with holes or cavities and its performance does not degrade significantly with the increase in the complexity of the enclosure or the objects. It outputs the location and orientation of all the objects, the packing sequence, and the packed configuration at the end of the packing operation. An improved layout algorithm that works with arbitrary enclosure geometry is also proposed. Different layout algorithms for the SAE and ISO luggage are proposed that exploit the unique characteristics of the problem under consideration. Several heuristics to improve the performance of the packing algorithm are also proposed. The proposed compact packing algorithm is benchmarked on a wide variety of synthetic and hypothetical problems and is shown to outperform other similar approaches. The physically-based shape morphing algorithm proposed in this dissertation is specifically designed for packing and layout applications, and thus it augments the compact packing algorithm. The proposed shape morphing algorithm is based on a modified mass-spring system which is used to model the morphable object. The shape morphing algorithm mimics a quasi-physical process similar to the inflation/deflation of a balloon filled with air. The morphing algorithm starts with an initial manifold geometry and morphs it to obtain a desired volume such that the obtained geometry does not interfere with the objects surrounding it. Several modifications to the original mass-spring system and to the underlying physics that governs it are proposed to significantly speed-up the shape morphing process. Since the geometry of a morphable object continuously changes during the morphing process, most collision detection algorithms that assume the colliding objects to be rigid cannot be used efficiently. And therefore, a general-purpose surface collision detection algorithm is also proposed that works with deformable objects and does not require any preprocessing. Many industrial design problems such as packing and layout optimization are computationally expensive, and a faster optimization algorithm can reduce the number of iterations (function evaluations) required to find the satisfycing solutions. A new multi-objective optimization algorithm namely Archive-based Micro Genetic Algorithm (AMGA2) is presented in this dissertation. Improved formulation for various operators used by the AMGA2 such as diversity preservation techniques, genetic variation operators, and the selection mechanism are also proposed. The AMGA2 also borrows several concepts from mathematical sciences to improve its performance and benefits from the existing literature in evolutionary optimization. A comprehensive benchmarking and comparison of AMGA2 with other state-of-the-art optimization algorithms on a wide variety of mathematical problems gleaned from literature demonstrates the superior performance of AMGA2. Thus, the research work presented in this dissertation makes contributions to the development and application of optimization and geometric algorithms

Optimisation sous contraintes de problèmes distribués par auto-organisation coopérative

Quotidiennement, divers problèmes d'optimisation : minimiser un coût de production, optimiser le parcours d'un véhicule, etc sont à résoudre. Ces problèmes se caractérisent par un degré élevé de complexité dû à l'hétérogénéité et la diversité des acteurs en jeu, à la masse importante des données ainsi qu'à la dynamique des environnements dans lesquels ils sont plongés. Face à la complexité croissante de ces applications, les approches de résolution classiques ont montré leurs limites. Depuis quelques années, la communauté scientifique s'intéresse aux développements de nouvelles solutions basées sur la distribution du calcul et la décentralisation du contrôle plus adaptées à ce genre de problème. La théorie des AMAS (Adaptive Multi-Agents Systems) propose le développement de solutions utilisant des systèmes multi-agents auto-adaptatifs par auto-organisation coopérative. Cette théorie a montré son adéquation pour la résolution de problèmes complexes et dynamiques, mais son application reste à un niveau d'abstraction assez élevé. L'objectif de ce travail est de spécialiser cette théorie pour la résolution de ce genre de problèmes. Ainsi, son utilisation en sera facilitée. Pour cela, le modèle d'agents AMAS4Opt avec des comportements et des interactions coopératifs et locaux a été défini. La validation s'est effectuée sur deux problèmes clés d'optimisation : le contrôle manufacturier et la conception de produit complexe. De plus, afin de montrer la robustesse et l'adéquation des solutions développées, un ensemble de critères d'évaluation permettant de souligner les points forts et faibles des systèmes adaptatifs et de les comparer à des systèmes existants a été défini.We solve problems and make decisions all day long. Some problems and decisions are very challenging: What is the best itinerary to deliver orders given the weather, the traffic and the hour? How to improve product manufacturing performances? etc. Problems that are characterized by a high level of complexity due to the heterogeneity and diversity of the participating actors, to the increasing volume of manipulated data and to the dynamics of the applications environments. Classical solving approaches have shown their limits to cope with this growing complexity. For the last several years, the scientific community has been interested in the development of new solutions based on computation distribution and control decentralization. The AMAS (Adaptive Multi-Agent-Systems) theory proposes to build solutions based on self-adaptive multi-agent systems using cooperative self-organization. This theory has shown its adequacy to solve different complex and dynamic problems, but remains at a high abstraction level. This work proposes a specialization of this theory for complex optimization problem solving under constraints. Thus, the usage of this theory is made accessible to different non-AMAS experts' engineers. Thus, the AMAS4Opt agent model with cooperative, local and generic behaviours and interactions has been defined.This model is validated on two well-known optimization problems: scheduling in manufacturing control and complex product design. Finally, in order to show the robustness and adequacy of the developed solutions, a set of evaluation criteria is proposed to underline the advantages and limits of adaptive systems and to compare them with already existing systems

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

Dynamic Maps: Representations of Change in Geospatial Modeling and Visualization

By coining the descriptive phrase ―user-centric geographic cosmology, Goodchild (1998), challenges the geographically oriented to address GIS in the broadest imaginable context: as interlocutor between persons and geo-phenomena. This investigation responds both in a general way, and more specifically, to the representations of change in GIS modeling and visualization leading to dynamic mapping. The investigation, consisting of a report and a series of experiments, explores and demonstrates prototype workarounds that enhance GIS capabilities by drawing upon ideas, techniques, and components from agent-based modeling and visualization software, and suggests shifts at the conceptual, methodological, and technical levels. The workarounds and demonstrations presented here are four-dimensional visualizations, representing changes and behaviors of different types of entities such as living creatures, mobile assets, features, structures, and surfaces, using GIS, agent-based modeling and animation techniques. In a typical case, a creature begins as a point feature in GIS, becomes a mobile and interactive object in agent-based modeling, and is fleshed out to three dimensions in an animated representation. In contrast, a land surface remains much the same in all three stages. The experiments address change in location, orientation, shape, visual attributes, viewpoint, scale, and speed in applications representing predator-prey, search and destroy, sense and locate and urban sprawl. During the experiments, particular attention is paid to factors of modeling and visualization involved in engaging human sensing and cognitive abilities