37 research outputs found
From metaheuristics to learnheuristics: Applications to logistics, finance, and computing
Un gran nombre de processos de presa de decisions en sectors estratĂšgics com el transport i la producciĂł representen problemes NP-difĂcils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurĂstiques sĂłn mĂštodes populars per a resoldre problemes d'optimitzaciĂł difĂcils en temps de cĂ lcul raonables. No obstant aixĂČ, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions sĂłn deterministes i conegudes. Aquests constitueixen supĂČsits forts que obliguen a treballar amb problemes simplificats. Com a conseqĂŒĂšncia, les solucions poden conduir a resultats pobres. Les simheurĂstiques integren la simulaciĂł a les metaheurĂstiques per resoldre problemes estocĂ stics d'una manera natural. AnĂ logament, les learnheurĂstiques combinen l'estadĂstica amb les metaheurĂstiques per fer front a problemes en entorns dinĂ mics, en quĂš els inputs poden dependre de l'estructura de la soluciĂł. En aquest context, les principals contribucions d'aquesta tesi sĂłn: el disseny de les learnheurĂstiques, una classificaciĂł dels treballs que combinen l'estadĂstica / l'aprenentatge automĂ tic i les metaheurĂstiques, i diverses aplicacions en transport, producciĂł, finances i computaciĂł.Un gran nĂșmero de procesos de toma de decisiones en sectores estratĂ©gicos como el transporte y la producciĂłn representan problemas NP-difĂciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurĂsticas son mĂ©todos populares para resolver problemas difĂciles de optimizaciĂłn de manera rĂĄpida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurĂsticas integran simulaciĂłn en metaheurĂsticas para resolver problemas estocĂĄsticos de una manera natural. De manera similar, las learnheurĂsticas combinan aprendizaje estadĂstico y metaheurĂsticas para abordar problemas en entornos dinĂĄmicos, donde los inputs pueden depender de la estructura de la soluciĂłn. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurĂsticas, una clasificaciĂłn de trabajos que combinan estadĂstica / aprendizaje automĂĄtico y metaheurĂsticas, y varias aplicaciones en transporte, producciĂłn, finanzas y computaciĂłn.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing
Traveling Salesman Problem
This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering
Approches générales de résolution pour les problÚmes multi-attributs de tournées de véhicules et confection d'horaires
ThÚse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de TroyesLe problÚme de tournées de véhicules (VRP) implique de planifier les itinéraires d'une flotte de véhicules afin de desservir un ensemble de clients à moindre coût. Ce problÚme d'optimisation combinatoire NP-difficile apparait dans de nombreux domaines d'application, notamment en logistique, télécommunications, robotique ou gestion de crise dans des contextes militaires et humanitaires. Ces applications amÚnent différents contraintes, objectifs et décisions supplémentaires ; des "attributs" qui viennent compléter les formulations classiques du problÚme. Les nombreux VRP Multi-Attributs (MAVRP) qui s'ensuivent sont le support d'une littérature considérable, mais qui manque de méthodes généralistes capables de traiter efficacement un éventail significatif de variantes. Par ailleurs, la résolution de problÚmes "riches", combinant de nombreux attributs, pose d'importantes difficultés méthodologiques.
Cette thĂšse contribue Ă relever ces dĂ©fis par le biais d'analyses structurelles des problĂšmes, de dĂ©veloppements de stratĂ©gies mĂ©taheuristiques, et de mĂ©thodes unifiĂ©es. Nous prĂ©sentons tout d'abord une Ă©tude transversale des concepts Ă succĂšs de 64 mĂ©ta-heuristiques pour 15 MAVRP afin d'en cerner les "stratĂ©gies gagnantes". Puis, nous analysons les problĂšmes et algorithmes d'ajustement d'horaires en prĂ©sence d'une sĂ©quence de tĂąches fixĂ©e, appelĂ©s problĂšmes de "timing". Ces mĂ©thodes, dĂ©veloppĂ©es indĂ©pendamment dans diffĂ©rents domaines de recherche liĂ©s au transport, ordonnancement, allocation de ressource et mĂȘme rĂ©gression isotonique, sont unifiĂ©s dans une revue multidisciplinaire.
Un algorithme gĂ©nĂ©tique hybride efficace est ensuite proposĂ©, combinant l'exploration large des mĂ©thodes Ă©volutionnaires, les capacitĂ©s d'amĂ©lioration agressive des mĂ©taheuristiques Ă voisinage, et une Ă©valuation bi-critĂšre des solutions considĂ©rant coĂ»t et contribution Ă la diversitĂ© de la population. Les meilleures solutions connues de la littĂ©rature sont retrouvĂ©es ou amĂ©liorĂ©es pour le VRP classique ainsi que des variantes avec multiples dĂ©pĂŽts et pĂ©riodes. La mĂ©thode est Ă©tendue aux VRP avec contraintes de fenĂȘtres de temps, durĂ©e de route, et horaires de conducteurs. Ces applications mettent en jeu de nouvelles mĂ©thodes d'Ă©valuation efficaces de contraintes temporelles relaxĂ©es, des phases de dĂ©composition, et des recherches arborescentes pour l'insertion des pauses des conducteurs. Un algorithme de gestion implicite du placement des dĂ©pĂŽts au cours de recherches locales, par programmation dynamique, est aussi proposĂ©. Des Ă©tudes expĂ©rimentales approfondies dĂ©montrent la contribution notable des nouvelles stratĂ©gies au sein de plusieurs cadres mĂ©ta-heuristiques.
Afin de traiter la variété des attributs, un cadre de résolution heuristique modulaire est présenté ainsi qu'un algorithme génétique hybride unifié (UHGS). Les attributs sont gérés par des composants élémentaires adaptatifs. Des expérimentations sur 26 variantes du VRP et 39 groupes d'instances démontrent la performance remarquable de UHGS qui, avec une unique implémentation et paramétrage, égalise ou surpasse les nombreux algorithmes dédiés, issus de plus de 180 articles, révélant ainsi que la généralité ne s'obtient pas forcément aux dépends de l'efficacité pour cette classe de problÚmes. Enfin, pour traiter les problÚmes riches, UHGS est étendu au sein d'un cadre de résolution parallÚle coopératif à base de décomposition, d'intégration de solutions partielles, et de recherche guidée.
L'ensemble de ces travaux permet de jeter un nouveau regard sur les MAVRP et les problÚmes de timing, leur résolution par des méthodes méta-heuristiques, ainsi que les méthodes généralistes pour l'optimisation combinatoire.The Vehicle Routing Problem (VRP) involves designing least cost delivery routes to service a geographically-dispersed set of customers while taking into account vehicle-capacity constraints. This NP-hard combinatorial optimization problem is linked with multiple applications in logistics, telecommunications, robotics, crisis management in military and humanitarian frameworks, among others. Practical routing applications are usually quite distinct from the academic cases, encompassing additional sets of specific constraints, objectives and decisions which breed further new problem variants. The resulting "Multi-Attribute" Vehicle Routing Problems (MAVRP) are the support of a vast literature which, however, lacks unified methods capable of addressing multiple MAVRP. In addition, some "rich" VRPs, i.e. those that involve several attributes, may be difficult to address because of the wide array of combined and possibly antagonistic decisions they require.
This thesis contributes to address these challenges by means of problem structure analysis, new metaheuristics and unified method developments. The "winning strategies" of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a unifying review. Another analysis is targeted on "timing" problems and algorithms for adjusting the execution dates of a given sequence of tasks. Such methods, independently studied in different research domains related to routing, scheduling, resource allocation, and even isotonic regression are here surveyed in a multidisciplinary review.
A Hybrid Genetic Search with Advanced Diversity Control (HGSADC) is then introduced, which combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and a bi-criteria evaluation of solutions based on cost and diversity measures. Results of remarkable quality are achieved on classic benchmark instances of the capacitated VRP, the multi-depot VRP, and the periodic VRP. Further extensions of the method to VRP variants with constraints on time windows, limited route duration, and truck drivers' statutory pauses are also proposed.
New route and neighborhood evaluation procedures are introduced to manage penalized infeasible solutions w.r.t. to time-window and duration constraints. Tree-search procedures are used for drivers' rest scheduling, as well as advanced search limitation strategies, memories and decomposition phases. A dynamic programming-based neighborhood search is introduced to optimally select the depot, vehicle type, and first customer visited in the route during local searches. The notable contribution of these new methodological elements is assessed within two different metaheuristic frameworks.
To further advance general-purpose MAVRP methods, we introduce a new component-based heuristic resolution framework and a Unified Hybrid Genetic Search (UHGS), which relies on modular self-adaptive components for addressing problem specifics. Computational experiments demonstrate the groundbreaking performance of UHGS. With a single implementation, unique parameter setting and termination criterion, this algorithm matches or outperforms all current problem-tailored methods from more than 180 articles, on 26 vehicle routing variants and 39 benchmark sets. To address rich problems, UHGS was included in a new parallel cooperative solution framework called "Integrative Cooperative Search (ICS)", based on problem decompositions, partial solutions integration, and global search guidance.
This compendium of results provides a novel view on a wide range of MAVRP and timing problems, on efficient heuristic searches, and on general-purpose solution methods for combinatorial optimization problems
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Nature inspired computational intelligence for financial contagion modelling
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the âtransmissionâ of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Tradersâ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial marketâs parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market