Problèmes de tournées de véhicules avec contraintes de chargement

Cette thèse s’intéresse aux problèmes de tournées de véhicules où l’on retrouve des contraintes de chargement ayant un impact sur les séquences de livraisons permises. Plus particulièrement, les items placés dans l’espace de chargement d’un véhicule doivent être directement accessibles lors de leur livraison sans qu’il soit nécessaire de déplacer d’autres items. Ces problèmes sont rencontrés dans plusieurs entreprises de transport qui livrent de gros objets (meubles, électroménagers). Le premier article de cette thèse porte sur une méthode exacte pour un problème de confection d’une seule tournée où un véhicule, dont l’aire de chargement est divisée en un certain nombre de piles, doit effectuer des cueillettes et des livraisons respectant une contrainte de type dernier entré, premier sorti. Lors d’une collecte, les items recueillis doivent nécessairement être déposés sur le dessus de l’une des piles. Par ailleurs, lors d’une livraison, les items doivent nécessairement se trouver sur le dessus de l’une des piles. Une méthode de séparation et évaluation avec plans sécants est proposée pour résoudre ce problème. Le second article présente une méthode de résolution exacte, également de type séparation et évaluation avec plans sécants, pour un problème de tournées de véhicules avec chargement d’items rectangulaires en deux dimensions. L’aire de chargement des véhicules correspond aussi à un espace rectangulaire avec une orientation, puisque les items doivent être chargés et déchargés par l’un des côtés. Une contrainte impose que les items d’un client soient directement accessibles au moment de leur livraison. Le dernier article aborde une problème de tournées de véhicules avec chargement d’items rectangulaires, mais où les dimensions de certains items ne sont pas connus avec certitude lors de la planification des tournées. Il est toutefois possible d’associer une distribution de probabilités discrète sur les dimensions possibles de ces items. Le problème est résolu de manière exacte avec la méthode L-Shape en nombres entiers.

Graphical constraints: a graphical user interface for constraint problems

A constraint satisfaction problem is a classical artificial intelligence paradigm characterized by a set of variables (each variable with an associated domain of possible values), and a set of constraints that specify relations among subsets of these variables. Solutions are assignments of values to all variables that satisfy all the constraints. Many real world problems may be modelled by means of constraints. The range of problems that can use this representation is very diverse and embraces areas like resource allocation, scheduling, timetabling or vehicle routing. Constraint programming is a form of declarative programming in the sense that instead of specifying a sequence of steps to execute, it relies on properties of the solutions to be found, which are explicitly defined by constraints. The idea of constraint programming is to solve problems by stating constraints which must be satisfied by the solutions. Constraint programming is based on specialized constraint solvers that take advantage of constraints to search for solutions. The success and popularity of complex problem solving tools can be greatly enhanced by the availability of friendly user interfaces. User interfaces cover two fundamental areas: receiving information from the user and communicating it to the system; and getting information from the system and deliver it to the user. Despite its potential impact, adequate user interfaces are uncommon in constraint programming in general. The main goal of this project is to develop a graphical user interface that allows to, intuitively, represent constraint satisfaction problems. The idea is to visually represent the variables of the problem, their domains and the problem constraints and enable the user to interact with an adequate constraint solver to process the constraints and compute the solutions. Moreover, the graphical interface should be capable of configure the solver’s parameters and present solutions in an appealing interactive way. As a proof of concept, the developed application – GraphicalConstraints – focus on continuous constraint programming, which deals with real valued variables and numerical constraints (equations and inequalities). RealPaver, a state-of-the-art solver in continuous domains, was used in the application. The graphical interface supports all stages of constraint processing, from the design of the constraint network to the presentation of the end feasible space solutions as 2D or 3D boxes.

Um algoritmo híbrido para o problema de roteamento de veículos com frotas heterogêneas

This paper aims to propose a hybrid meta-heuristics for the Heterogeneous Fleet Vehicle Routing Problem (HVRP), which is a combinatorial optimization problem NP-hard, and is characterized by the use of a limited fleet consists of different vehicles with different capacities. The hybrid method developed makes use of a memetic algorithm associated with the component optimizer Vocabulary Building. The resulting hybrid meta-heuristic was implemented in the programming language C + + and computational experiments generated good results in relation to meta-heuristic applied in isolation, proving the efficiency of the proposed method.

Metaheurísticas evolutivas para o problema de roteamento de unidades móveis de pistoneio

This paper presents metaheuristic strategies based on the framework of evolutionary algorithms (Genetic and Memetic) with the addition of Technical Vocabulary Building for solving the Problem of Optimizing the Use of Multiple Mobile Units Recovery of Oil (MRO units). Because it is an NP-hard problem, a mathematical model is formulated for the problem, allowing the construction of test instances that are used to validate the evolutionary metaheuristics developed

Desenvolvimento de um modelo computacional para a ampliação do atendimento do Programa de Acessibilidade Especial Porta a Porta - PRAE

Worldwide, the demand for transportation services for persons with disabilities, the elderly, and persons with reduced mobility have increased in recent years. The population is aging, governments need to adapt to this reality, and this fact could mean business opportunities for companies. Within this context is inserted the Programa de Acessibilidade Especial porta a porta PRAE, a door to door public transportation service from the city of Natal-RN in Brazil. The research presented in this dissertation seeks to develop a programming model which can assist the process of decision making of managers of the shuttle. To that end, it was created an algorithm based on methods of generating approximate solutions known as heuristics. The purpose of the model is to increase the number of people served by the PRAE, given the available fleet, generating optimized schedules routes. The PRAE is a problem of vehicle routing and scheduling of dial-a-ride - DARP, the most complex type among the routing problems. The validation of the method of resolution was made by comparing the results derived by the model and the currently programming method. It is expected that the model is able to increase the current capacity of the service requests of transport

Um estudo algorítmico da programação da intervenção de sondas de produção

This work approaches the Scheduling Workover Rigs Problem (SWRP) to maintain the wells of an oil field, although difficult to resolve, is extremely important economical, technical and environmental. A mathematical formulation of this problem is presented, where an algorithmic approach was developed. The problem can be considered to find the best scheduling service to the wells by the workover rigs, taking into account the minimization of the composition related to the costs of the workover rigs and the total loss of oil suffered by the wells. This problem is similar to the Vehicle Routing Problem (VRP), which is classified as belonging to the NP-hard class. The goal of this research is to develop an algorithmic approach to solve the SWRP, using the fundamentals of metaheuristics like Memetic Algorithm and GRASP. Instances are generated for the tests to analyze the computational performance of the approaches mentioned above, using data that are close to reality. Thereafter, is performed a comparison of performance and quality of the results obtained by each one of techniques used

Um estudo algorítmico de problemas logísticos na indústria de petróleo e gás natural

This work consists on the study of two important problems arising from the operations of petroleum and natural gas industries. The first problem the pipe dimensioning problem on constrained gas distribution networks consists in finding the least cost combination of diameters from a discrete set of commercially available ones for the pipes of a given gas network, such that it respects minimum pressure requirements at each demand node and upstream pipe conditions. On its turn, the second problem the piston pump unit routing problem comes from the need of defining the piston pump unit routes for visiting a number of non-emergent wells in on-shore fields, i.e., wells which don t have enough pressure to make the oil emerge to surface. The periodic version of this problem takes into account the wells re-filling equation to provide a more accurate planning in the long term. Besides the mathematical formulation of both problems, an exact algorithm and a taboo search were developed for the solution of the first problem and a theoretical limit and a ProtoGene transgenetic algorithm were developed for the solution of the second problem. The main concepts of the metaheuristics are presented along with the details of their application to the cited problems. The obtained results for both applications are promising when compared to theoretical limits and alternate solutions, either relative to the quality of the solutions or to associated running time

Uma proposta de resolução para problemas de roteirização de veículos baseada no algoritmo genético

The vehicle routing problem is to nd a better route to meet a set of customers who are geographically dispersed using vehicles that are a central repository to which they return after serving customers. These customers have a demand that must be met. Such problems have a wide practical application among them we can mention: school transport, distribution of newspapers, garbage collection, among others. Because it is a classic problem as NP-hard, these problems have aroused interest in the search for viable methods of resolution. In this paper we use the Genetic Algorithm as a resolution

Progetto e sviluppo di un algoritmo per la pianificazione ottimizzata della distribuzione con viaggi sincronizzati

Il lavoro di tesi svolto riguarda la progettazione e lo sviluppo di un algoritmo per la pianificazione ottimizzata della distribuzione con viaggi sincronizzati; il metodo sviluppato è un algoritmo mateuristico. I metodi mateuristici nascono dall’integrazione di algoritmi esatti, utilizzati all’interno di un framework metaeuristico, scelto come paradigma di soluzione del problema. La combinazione di componenti esatte e algoritmi metaeuristici ha lo scopo di sfruttare i vantaggi di entrambi gli approcci: grazie all'uso di componenti esatte, è possibile operare in modo efficace e di concentrarsi su alcuni dei vincoli del problema, mentre, con l'utilizzo di un framework metaeuristico, si può efficacemente esplorare grandi aree dello spazio di ricerca in tempi accettabili. Il problema analizzato nel lavoro di tesi è un problema di trasporto, ovvero il Vehicle Routing Problem con finestre temporali e vincoli di sincronizzazione a coppie (VRPTWPS). Il problema richiede di individuare un piano di organizzazione ottimizzato per i viaggi di consegna merci presso un insieme di clienti; ogni cliente richiede che la consegna avvenga all’interno di orari predefiniti; un sottoinsieme di essi richiede, inoltre, che la consegna venga effettuata con la presenza di esattamente due addetti. La presenza di quest’ultimo vincolo richiede, dunque, che due incaricati, indipendentemente dai viaggi di visita che questi effettuano, si incontrino presso uno stesso cliente nello stesso istante. Il vincolo di sincronizzazione rende il problema difficile da risolvere in maniera ottimizzata con i tradizionali metodi di ricerca locale; da ciò nasce l’uso dei metodi mateuristici per la risoluzione ottimizzata del problema. Grazie all’utilizzo di algoritmi esatti, i metodi mateuristici riescono a trattare in maniera più efficace alcuni vincoli dei problemi da risolvere.

Decomposition and reformulation of integer linear programming problems

This thesis deals with an investigation of Decomposition and Reformulation to solve Integer Linear Programming Problems. This method is often a very successful approach computationally, producing high-quality solutions for well-structured combinatorial optimization problems like vehicle routing, cutting stock, p-median and generalized assignment . However, until now the method has always been tailored to the specific problem under investigation. The principal innovation of this thesis is to develop a new framework able to apply this concept to a generic MIP problem. The new approach is thus capable of auto-decomposition and autoreformulation of the input problem applicable as a resolving black box algorithm and works as a complement and alternative to the normal resolving techniques. The idea of Decomposing and Reformulating (usually called in literature Dantzig and Wolfe Decomposition DWD) is, given a MIP, to convexify one (or more) subset(s) of constraints (slaves) and working on the partially convexified polyhedron(s) obtained. For a given MIP several decompositions can be defined depending from what sets of constraints we want to convexify. In this thesis we mainly reformulate MIPs using two sets of variables: the original variables and the extended variables (representing the exponential extreme points). The master constraints consist of the original constraints not included in any slaves plus the convexity constraint(s) and the linking constraints(ensuring that each original variable can be viewed as linear combination of extreme points of the slaves). The solution procedure consists of iteratively solving the reformulated MIP (master) and checking (pricing) if a variable of reduced costs exists, and in which case adding it to the master and solving it again (columns generation), or otherwise stopping the procedure. The advantage of using DWD is that the reformulated relaxation gives bounds stronger than the original LP relaxation, in addition it can be incorporated in a Branch and bound scheme (Branch and Price) in order to solve the problem to optimality. If the computational time for the pricing problem is reasonable this leads in practice to a stronger speed up in the solution time, specially when the convex hull of the slaves is easy to compute, usually because of its special structure.

Progettazione e sviluppo di software di supporto alla logistica dei trasporti.

Uno dei problemi più diffusi, nell'ambito della logistica, è rappresentato dai costi di trasporto. La gestione dei flussi merci, l'approvvigionamento dei clienti, e la relativa pianifcazione della movimentazione dei veicoli, hanno incidenze notevoli sui costi di gestione aziendali, i quali vengono stimati mediamente nel 45% dei costi logistici. A ragione di questo, sono sempre di più le aziende che ricorrono all'impiego di uffici dedicati alla pianifcazione delle consegne e la gestione dei trasporti in generale. Sebbene le voci di bilancio relative al trasporto raggiungano cifre rilevanti, fno al 4% del fatturato annuo, il tema della pianifcazione viene spesso sottovalutato. Infatti la soluzione a problemi di pianifcazione e monitoraggio dei costi, è spesso demandata a procedure manuali senza supporto informatico. Nasce da qui l'esigenza di proporre uno strumento informatico che supporti gli addetti preposti alla pianifcazione, sviluppando un sistema che copra esigenze di pianifcazione dei viaggi, controllo e consuntivazione dei costi di trasporto, e monitoraggio dei mezzi in tempo reale. La proposta di Gesp srl, Geographic Information Systems, azienda italiana che opera da anni nel campo delle applicazioni software geo-spaziali, prende il nome di Nuovo Sistema Trasporti, o più semplicemente, NST. In quest'ambito prende corpo questa tesi, la quale si pone l'obiettivo di illustrare le fasi di nascita, analisi, progettazione e sviluppo di un software generico per il supporto alla logistica. Saranno così analizzati: le problematiche affrontate nella fase di defnizione, e kick-off (avvio), del progetto, il problema del routing, o Vehicle Routing Problem e le tecniche di Ricerca Operativa che vengono applicate per la sua risoluzione; le moderne metodologie di gestione e sviluppo di un software; l'architettura e le tecnologie impiegate per la distribuzione dell'applicativo.

Many-core Algorithms for Combinatorial Optimization

Combinatorial Optimization is becoming ever more crucial, in these days. From natural sciences to economics, passing through urban centers administration and personnel management, methodologies and algorithms with a strong theoretical background and a consolidated real-word effectiveness is more and more requested, in order to find, quickly, good solutions to complex strategical problems. Resource optimization is, nowadays, a fundamental ground for building the basements of successful projects. From the theoretical point of view, Combinatorial Optimization rests on stable and strong foundations, that allow researchers to face ever more challenging problems. However, from the application point of view, it seems that the rate of theoretical developments cannot cope with that enjoyed by modern hardware technologies, especially with reference to the one of processors industry. In this work we propose new parallel algorithms, designed for exploiting the new parallel architectures available on the market. We found that, exposing the inherent parallelism of some resolution techniques (like Dynamic Programming), the computational benefits are remarkable, lowering the execution times by more than an order of magnitude, and allowing to address instances with dimensions not possible before. We approached four Combinatorial Optimization’s notable problems: Packing Problem, Vehicle Routing Problem, Single Source Shortest Path Problem and a Network Design problem. For each of these problems we propose a collection of effective parallel solution algorithms, either for solving the full problem (Guillotine Cuts and SSSPP) or for enhancing a fundamental part of the solution method (VRP and ND). We endorse our claim by presenting computational results for all problems, either on standard benchmarks from the literature or, when possible, on data from real-world applications, where speed-ups of one order of magnitude are usually attained, not uncommonly scaling up to 40 X factors.

Contributions to exact approaches in combinatorial optimization

The focus of this thesis is to contribute to the development of new, exact solution approaches to different combinatorial optimization problems. In particular, we derive dedicated algorithms for a special class of Traveling Tournament Problems (TTPs), the Dial-A-Ride Problem (DARP), and the Vehicle Routing Problem with Time Windows and Temporal Synchronized Pickup and Delivery (VRPTWTSPD). Furthermore, we extend the concept of using dual-optimal inequalities for stabilized Column Generation (CG) and detail its application to improved CG algorithms for the cutting stock problem, the bin packing problem, the vertex coloring problem, and the bin packing problem with conflicts. In all approaches, we make use of some knowledge about the structure of the problem at hand to individualize and enhance existing algorithms. Specifically, we utilize knowledge about the input data (TTP), problem-specific constraints (DARP and VRPTWTSPD), and the dual solution space (stabilized CG). Extensive computational results proving the usefulness of the proposed methods are reported.

Arquitectura de supercomputación en la nube para la resolución de problemas de optimización combinatoria

La tesis está focalizada en la resolución de problemas de optimización combinatoria, haciendo uso de las opciones tecnológicas actuales que ofrecen las tecnologías de la información y las comunicaciones, y la investigación operativa. Los problemas de optimización combinatoria se resuelven en general mediante programación lineal y metaheurísticas. La aplicación de las técnicas de resolución de los problemas de optimización combinatoria requiere de una elevada carga computacional, y los algoritmos deben diseñarse, por un lado pensando en la efectividad para encontrar buenas soluciones del problema, y por otro lado, pensando en un uso adecuado de los recursos informáticos disponibles. La programación lineal y las metaheurísticas son técnicas de resolución genéricas, que se pueden aplicar a diferentes problemas, partiendo de una base común que se particulariza para cada problema concreto. En el campo del desarrollo de software, los frameworks cumplen esa función de comenzar un proyecto con el trabajo general ya disponible, con la opción de cambiar o extender ese comportamiento base o genérico, para construir el sistema concreto, lo que permite reducir el tiempo de desarrollo, y amplía las posibilidades de éxito del proyecto. En esta tesis se han desarrollado dos frameworks de desarrollo. El framework ILP permite modelar y resolver problemas de programación lineal, de forma independiente al software de resolución de programación lineal que se utilice. El framework LME permite resolver problemas de optimización combinatoria mediante metaheurísticas. Tradicionalmente, las aplicaciones de resolución de problemas de optimización combinatoria son aplicaciones de escritorio que permiten gestionar toda la información de entrada del problema y resuelven el problema en local, con los recursos hardware disponibles. Recientemente ha aparecido un nuevo paradigma de despliegue y uso de aplicaciones que permite compartir recursos informáticos especializados por Internet. Esta nueva forma de uso de recursos informáticos es la computación en la nube, que presenta el modelo de software como servicio (SaaS). En esta tesis se ha construido una plataforma SaaS, para la resolución de problemas de optimización combinatoria, que se despliega sobre arquitecturas compuestas por procesadores multi-núcleo y tarjetas gráficas, y dispone de algoritmos de resolución basados en frameworks de programación lineal y metaheurísticas. Toda la infraestructura es independiente del problema de optimización combinatoria a resolver, y se han desarrollado tres problemas que están totalmente integrados en la plataforma SaaS. Estos problemas se han seleccionado por su importancia práctica. Uno de los problemas tratados en la tesis, es el problema de rutas de vehículos (VRP), que consiste en calcular las rutas de menor coste de una flota de vehículos, que reparte mercancías a todos los clientes. Se ha partido de la versión más clásica del problema y se han hecho estudios en dos direcciones. Por un lado se ha cuantificado el aumento en la velocidad de ejecución de la resolución del problema en tarjetas gráficas. Por otro lado, se ha estudiado el impacto en la velocidad de ejecución y en la calidad de soluciones, en la resolución por la metaheurística de colonias de hormigas (ACO), cuando se introduce la programación lineal para optimizar las rutas individuales de cada vehículo. Este problema se ha desarrollado con los frameworks ILP y LME, y está disponible en la plataforma SaaS. Otro de los problemas tratados en la tesis, es el problema de asignación de flotas (FAP), que consiste en crear las rutas de menor coste para la flota de vehículos de una empresa de transporte de viajeros. Se ha definido un nuevo modelo de problema, que engloba características de problemas presentados en la literatura, y añade nuevas características, lo que permite modelar los requerimientos de las empresas de transporte de viajeros actuales. Este nuevo modelo resuelve de forma integrada el problema de definir los horarios de los trayectos, el problema de asignación del tipo de vehículo, y el problema de crear las rotaciones de los vehículos. Se ha creado un modelo de programación lineal para el problema, y se ha resuelto por programación lineal y por colonias de hormigas (ACO). Este problema se ha desarrollado con los frameworks ILP y LME, y está disponible en la plataforma SaaS. El último problema tratado en la tesis es el problema de planificación táctica de personal (TWFP), que consiste en definir la configuración de una plantilla de trabajadores de menor coste, para cubrir una demanda de carga de trabajo variable. Se ha definido un modelo de problema muy flexible en la definición de contratos, que permite el uso del modelo en diversos sectores productivos. Se ha definido un modelo matemático de programación lineal para representar el problema. Se han definido una serie de casos de uso, que muestran la versatilidad del modelo de problema, y permiten simular el proceso de toma de decisiones de la configuración de una plantilla de trabajadores, cuantificando económicamente cada decisión que se toma. Este problema se ha desarrollado con el framework ILP, y está disponible en la plataforma SaaS. ABSTRACT The thesis is focused on solving combinatorial optimization problems, using current technology options offered by information technology and communications, and operations research. Combinatorial optimization problems are solved in general by linear programming and metaheuristics. The application of these techniques for solving combinatorial optimization problems requires a high computational load, and algorithms are designed, on the one hand thinking to find good solutions to the problem, and on the other hand, thinking about proper use of the available computing resources. Linear programming and metaheuristic are generic resolution techniques, which can be applied to different problems, beginning with a common base that is particularized for each specific problem. In the field of software development, frameworks fulfill this function that allows you to start a project with the overall work already available, with the option to change or extend the behavior or generic basis, to build the concrete system, thus reducing the time development, and expanding the possibilities of success of the project. In this thesis, two development frameworks have been designed and developed. The ILP framework allows to modeling and solving linear programming problems, regardless of the linear programming solver used. The LME framework is designed for solving combinatorial optimization problems using metaheuristics. Traditionally, applications for solving combinatorial optimization problems are desktop applications that allow the user to manage all the information input of the problem and solve the problem locally, using the available hardware resources. Recently, a new deployment paradigm has appeared, that lets to share hardware and software resources by the Internet. This new use of computer resources is cloud computing, which presents the model of software as a service (SaaS). In this thesis, a SaaS platform has been built for solving combinatorial optimization problems, which is deployed on architectures, composed of multi-core processors and graphics cards, and has algorithms based on metaheuristics and linear programming frameworks. The SaaS infrastructure is independent of the combinatorial optimization problem to solve, and three problems are fully integrated into the SaaS platform. These problems have been selected for their practical importance. One of the problems discussed in the thesis, is the vehicle routing problem (VRP), which goal is to calculate the least cost of a fleet of vehicles, which distributes goods to all customers. The VRP has been studied in two directions. On one hand, it has been quantified the increase in execution speed when the problem is solved on graphics cards. On the other hand, it has been studied the impact on execution speed and quality of solutions, when the problem is solved by ant colony optimization (ACO) metaheuristic, and linear programming is introduced to optimize the individual routes of each vehicle. This problem has been developed with the ILP and LME frameworks, and is available in the SaaS platform. Another problem addressed in the thesis, is the fleet assignment problem (FAP), which goal is to create lower cost routes for a fleet of a passenger transport company. It has been defined a new model of problem, which includes features of problems presented in the literature, and adds new features, allowing modeling the business requirements of today's transport companies. This new integrated model solves the problem of defining the flights timetable, the problem of assigning the type of vehicle, and the problem of creating aircraft rotations. The problem has been solved by linear programming and ACO. This problem has been developed with the ILP and LME frameworks, and is available in the SaaS platform. The last problem discussed in the thesis is the tactical planning staff problem (TWFP), which is to define the staff of lower cost, to cover a given work load. It has been defined a very rich problem model in the definition of contracts, allowing the use of the model in various productive sectors. It has been defined a linear programming mathematical model to represent the problem. Some use cases has been defined, to show the versatility of the model problem, and to simulate the decision making process of setting up a staff, economically quantifying every decision that is made. This problem has been developed with the ILP framework, and is available in the SaaS platform.