Hybrid constraint programming and metaheuristic methods for large scale optimization problems

This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatorial optimization problems are widely spread in everyday life and the need of solving difficult problems is more and more urgent. Metaheuristic techniques have been developed in the last decades to effectively handle the approximate solution of combinatorial optimization problems; we will examine metaheuristics in detail, focusing on the common aspects of different techniques. Each metaheuristic approach possesses its own peculiarities in designing and guiding the solution process; our work aims at recognizing components which can be extracted from metaheuristic methods and re-used in different contexts. In particular we focus on the possibility of porting metaheuristic elements to constraint programming based environments, as constraint programming is able to deal with feasibility issues of optimization problems in a very effective manner. Moreover, CP offers a general paradigm which allows to easily model any type of problem and solve it with a problem-independent framework, differently from local search and metaheuristic methods which are highly problem specific. In this work we describe the implementation of the Local Branching framework, originally developed for Mixed Integer Programming, in a CP-based environment. Constraint programming specific features are used to ease the search process, still mantaining an absolute generality of the approach. We also propose a search strategy called Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent solution by performing CP-based tree search and encloses concepts from metaheuristic techniques. SNS can be used as a stand alone search strategy, but it can alternatively be embedded in existing strategies as intensification and diversification mechanism. In particular we show its integration within the CP-based local branching. We provide an extensive experimental evaluation of the proposed approaches on instances of the Asymmetric Traveling Salesman Problem and of the Asymmetric Traveling Salesman Problem with Time Windows. The proposed approaches achieve good results on practical size problem, thus demonstrating the benefit of integrating metaheuristic concepts in CP-based frameworks.

Metodi euristici per il problema dei trasporti con costi fissi

In questa tesi viene considerato il problema dei trasporti con costi fissi (FCTP) che, assieme al Traveling Salesman Problem (TSP), è uno dei problemi nobili dell’ottimizzazione combinatoria. Esso generalizza il ben noto problema dei trasporti (TP) imponendo che il costo per spedire prodotti da un’origine ad una destinazione sia composto da un costo fisso ed un costo proporzionale alla quantità spedita. Il FCTP è stato formulato per la prima volta in un articolo di Hirsch e Dantzig (1968) ed è stato da allora oggetto di studio per la ricerca di nuovi e sempre migliori algoritmi di risoluzione. Nessuno dei metodi esatti fin ora pubblicati è in grado di risolvere istanze con più di 15 origini e 15 destinazioni. Solo recentemente, Roberti et al. (2013), in un paper in corso di pubblicazione, hanno presentato un metodo esatto basato su una nuova formulazione matematica del problema, il quale è in grado di risolvere istanze di FCTP con 70 origini e 70 destinazioni. La crescita esponenziale dello sforzo computazionale richiesto dai metodi esatti ne ha confinato l’applicazione a problemi di dimensioni ridotte. Tali limitazioni hanno portato allo studio e alla ricerca di approcci approssimativi, euristici e metaeuristici i quali sfruttano varie strategie di local search. Fra i molteplici metodi euristici presentati in letteratura, meritano particolare attenzione quelli di Sun et al. (1998) e Glover et al. (2005). Recentemente, Buson et al. (2013) hanno presentato un nuovo euristico che domina tutti i precedenti sui problemi test proposti in letteratura. In questa tesi viene presentato un approccio Tabu Search che migliora il metodo originalmente proposto da Sun et al. (1998). I risultati computazionali ottenuti con un codice prototipale indicano che l’algoritmo sviluppato è migliore del metodo originario di Sun et al. (1998) e competitivo con il più recente metodo proposto da Buson et al. (2013).

Dynamic weighting in Monte Carlo and optimization

Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks arising from multimodal sampling, neural network training, and the traveling salesman problem. Numerical tests on these problems confirm the effectiveness of the method.

The State of the art in the routing and scheduling of vehicles and crews /

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Method of Finding Hamilton Routes in Transport Network

This article discusses a solution method for Hamilton Problem, which either finds the task's solution, or indicates that the task is unsolvable. Offered method has significantly smaller requirements for computing resources than known algorithms.

A heuristic algorithm to generate test program sequences for moving probe electronic test equipment

The electronics industry, is experiencing two trends one of which is the drive towards miniaturization of electronic products. The in-circuit testing predominantly used for continuity testing of printed circuit boards (PCB) can no longer meet the demands of smaller size circuits. This has lead to the development of moving probe testing equipment. Moving Probe Test opens up the opportunity to test PCBs where the test points are on a small pitch (distance between points). However, since the test uses probes that move sequentially to perform the test, the total test time is much greater than traditional in-circuit test. While significant effort has concentrated on the equipment design and development, little work has examined algorithms for efficient test sequencing. The test sequence has the greatest impact on total test time, which will determine the production cycle time of the product. Minimizing total test time is a NP-hard problem similar to the traveling salesman problem, except with two traveling salesmen that must coordinate their movements. The main goal of this thesis was to develop a heuristic algorithm to minimize the Flying Probe test time and evaluate the algorithm against a "Nearest Neighbor" algorithm. The algorithm was implemented with Visual Basic and MS Access database. The algorithm was evaluated with actual PCB test data taken from Industry. A statistical analysis with 95% C.C. was performed to test the hypothesis that the proposed algorithm finds a sequence which has a total test time less than the total test time found by the "Nearest Neighbor" approach. Findings demonstrated that the proposed heuristic algorithm reduces the total test time of the test and, therefore, production cycle time can be reduced through proper sequencing.

Nested Rollout Policy Adaptation for optimizing vehicle selection in complex VRPs

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, different constraints are handled concurrently. This research extends solutions obtained for the traveling salesman problem with time windows to a much wider class of route planning problems in logistics. The work describes a novel approach that:  supports a heterogeneous fleet of vehicles  dynamically reduces the number of vehicles  respects individual capacity restrictions  satisfies pickup and delivery constraints  takes Hamiltonian paths (rather than cycles) The proposed approach uses Monte-Carlo Tree Search and in particular Nested Rollout Policy Adaptation. For the evaluation of the work, real data from the industry was obtained and tested and the results are reported.

Estudio y aplicación de metaheurísticas y comparación con métodos exhaustivos

Cuando nos enfrentamos a problemas reales haciendo uso de recursos computacionales, hemos de tener en cuenta que el número de posibles soluciones candidatas a tener en cuenta puede llegar a ser tan inmenso que abordarlas mediante técnicas algorítmicas clásicas, en la mayoría de los casos, pueden llegar a convertirse en un problema en sí mismo debido al gran coste en recursos que pueden llegar a generar. En este contexto, aspectos como el tiempo utilizado en la búsqueda de una solución mediante algoritmos de búsqueda exhaustiva tales como fuerza bruta, vuelta atrás, ramificación y poda, etc., puede llegar a ser prohibitivo en la práctica. Ante este problema que se nos plantea, podemos hacer un estudio sobre otros métodos, tales como los metaheurísticos, que, aunque no siempre aseguran la optimalidad de las soluciones producidas; tienen un tiempo de ejecución mucho menor que los métodos exhaustivos. En el presente trabajo hemos seleccionado dos problemas NP-completos de entre los más famosos de la literatura y hemos realizado un estudio de ambos. Concretamente, los problemas seleccionados han sido el TSP (Traveling Salesman Problem) y el problema de la Mochila 0-1. Por otro lado, hemos llevado a cabo un estudio sobre distintas metaheurísticas para poder resolver los problemas mencionados. Entre estas metaheurísticas, hemos seleccionado cuatro: metaheurísticas evolutivas, metaheurísticas inspiradas en colonias de hormigas, metaheurísticas simulated annealing (enfriamiento simulado) y metaheurísticas GRASP (Greedy Randomized Adaptive Search Procedure). Después de esto, cada problema ha sido resuelto aplicando tanto algoritmos de búsqueda exhaustiva como metaheurísticas. Una vez adaptados los algoritmos a la resolución de los problemas concretos, hemos realizado un estudio experimental, donde se realizaron comparativas de rendimiento. Finalmente, todo este trabajo ha sido plasmado en el desarrollo de una aplicación software, la cual consta de dos partes: una que contiene la implementación los algoritmos adaptados para la resolución de los problemas y que son ofrecidos a modo de servicios web y otra parte donde se ha implementado un cliente web que puede consumir estos servicios y realizar una presentación más vistosa de la ejecución de los algoritmos y los resultados obtenidos. Esta arquitectura podrá servir como base para futuras ampliaciones de este estudio.

Dynamic Multi-Objective Optimization With jMetal and Spark: a Case Study

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.

Une heuristique de recherche à voisinage variable pour le problème du voyageur de commerce avec fenêtres de temps

Nous adaptons une heuristique de recherche à voisinage variable pour traiter le problème du voyageur de commerce avec fenêtres de temps (TSPTW) lorsque l'objectif est la minimisation du temps d'arrivée au dépôt de destination. Nous utilisons des méthodes efficientes pour la vérification de la réalisabilité et de la rentabilité d'un mouvement. Nous explorons les voisinages dans des ordres permettant de réduire l'espace de recherche. La méthode résultante est compétitive avec l'état de l'art. Nous améliorons les meilleures solutions connues pour deux classes d'instances et nous fournissons les résultats de plusieurs instances du TSPTW pour la première fois.

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence.

Une heuristique de recherche à voisinage variable pour le problème du voyageur de commerce avec fenêtres de temps

Nous adaptons une heuristique de recherche à voisinage variable pour traiter le problème du voyageur de commerce avec fenêtres de temps (TSPTW) lorsque l'objectif est la minimisation du temps d'arrivée au dépôt de destination. Nous utilisons des méthodes efficientes pour la vérification de la réalisabilité et de la rentabilité d'un mouvement. Nous explorons les voisinages dans des ordres permettant de réduire l'espace de recherche. La méthode résultante est compétitive avec l'état de l'art. Nous améliorons les meilleures solutions connues pour deux classes d'instances et nous fournissons les résultats de plusieurs instances du TSPTW pour la première fois.

Numerical solution to the Saffman-Taylor finger problem with kinetic undercooling regularisation

The Saffman-Taylor finger problem is to predict the shape and,in particular, width of a finger of fluid travelling in a Hele-Shaw cell filled with a different, more viscous fluid. In experiments the width is dependent on the speed of propagation of the finger, tending to half the total cell width as the speed increases. To predict this result mathematically, nonlinear effects on the fluid interface must be considered; usually surface tension is included for this purpose. This makes the mathematical problem suffciently diffcult that asymptotic or numerical methods must be used. In this paper we adapt numerical methods used to solve the Saffman-Taylor finger problem with surface tension to instead include the effect of kinetic undercooling, a regularisation effect important in Stefan melting-freezing problems, for which Hele-Shaw flow serves as a leading order approximation when the specific heat of a substance is much smaller than its latent heat. We find the existence of a solution branch where the finger width tends to zero as the propagation speed increases, disagreeing with some aspects of the asymptotic analysis of the same problem. We also find a second solution branch, supporting the idea of a countably infinite number of branches as with the surface tension problem.

goDesign travelling design workshop program for regional secondary students

goDesign Travelling Workshop Program for Regional Secondary Students was an initiative of Queensland University of Technology (QUT) and the Design Institute of Australia (DIA) Queensland Branch, which aligned with the DIA unleashed: Queensland design on tour 2010 Exhibition. It was designed be delivered by university design academics in state secondary schools in Chinchilla, Mt Isa, Quilpie, Emerald, Gladstone and Bundaberg between February and September 2010, to approximately 95 secondary students and 24 teachers from the subject areas of visual art, graphics and industrial technology and design. A talk by a visiting design practitioner whose work was displayed in the exhibition, also features in the final day of the program in each town, and student work from the workshop was displayed in the exhibition alongside the professional design work. The three-day workshop is a design immersion program for regional Queensland Secondary Schools, which responds to specific actions outlined in the Queensland Government Design Strategy 2020 to ‘Build Design Knowledge and Learning’ and ‘Foster a Design Culture’. Underpinned by a place-based approach and the integration of Dr Charles Burnette’s IDESIGN teaching model, the program gives students and teachers the opportunity to explore, analyse and reimagine their local town through a series of scaffolded problem solving activities around the theme of ‘place’. The program allows students to gain hands-on experience designing graphics, products, interior spaces and architecture to assist their local community, with the support of design professionals. Students work individually and in groups on real design problems learning sketching, making, communication, presentation and collaboration skills to improve their design process, while considering social, cultural and environmental opportunities. The program was designed to facilitate an understanding of the value of design thinking and its importance to regional communities, to give students more information about various design disciplines as career options, and provide a professional development opportunity for teachers. Advisory assistance for the program was gained through Kelvin Grove State College, Queensland Studies Authority and QMI/Manufacturing Skills Queensland Manager, Manufacturing & Engineering Gateway Schools Project.

Models of collective cell motion for cell populations with different aspect ratio : diffusion, proliferation and travelling waves

Continuum, partial differential equation models are often used to describe the collective motion of cell populations, with various types of motility represented by the choice of diffusion coefficient, and cell proliferation captured by the source terms. Previously, the choice of diffusion coefficient has been largely arbitrary, with the decision to choose a particular linear or nonlinear form generally based on calibration arguments rather than making any physical connection with the underlying individual-level properties of the cell motility mechanism. In this work we provide a new link between individual-level models, which account for important cell properties such as varying cell shape and volume exclusion, and population-level partial differential equation models. We work in an exclusion process framework, considering aligned, elongated cells that may occupy more than one lattice site, in order to represent populations of agents with different sizes. Three different idealizations of the individual-level mechanism are proposed, and these are connected to three different partial differential equations, each with a different diffusion coefficient; one linear, one nonlinear and degenerate and one nonlinear and nondegenerate. We test the ability of these three models to predict the population level response of a cell spreading problem for both proliferative and nonproliferative cases. We also explore the potential of our models to predict long time travelling wave invasion rates and extend our results to two dimensional spreading and invasion. Our results show that each model can accurately predict density data for nonproliferative systems, but that only one does so for proliferative systems. Hence great care must be taken to predict density data for with varying cell shape.