44 research outputs found

    A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

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    This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

    Meta-heuristic Solution Methods for Rich Vehicle Routing Problems

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    Le problème de tournées de véhicules (VRP), introduit par Dantzig and Ramser en 1959, est devenu l'un des problèmes les plus étudiés en recherche opérationnelle, et ce, en raison de son intérêt méthodologique et de ses retombées pratiques dans de nombreux domaines tels que le transport, la logistique, les télécommunications et la production. L'objectif général du VRP est d'optimiser l'utilisation des ressources de transport afin de répondre aux besoins des clients tout en respectant les contraintes découlant des exigences du contexte d’application. Les applications réelles du VRP doivent tenir compte d’une grande variété de contraintes et plus ces contraintes sont nombreuse, plus le problème est difficile à résoudre. Les VRPs qui tiennent compte de l’ensemble de ces contraintes rencontrées en pratique et qui se rapprochent des applications réelles forment la classe des problèmes ‘riches’ de tournées de véhicules. Résoudre ces problèmes de manière efficiente pose des défis considérables pour la communauté de chercheurs qui se penchent sur les VRPs. Cette thèse, composée de deux parties, explore certaines extensions du VRP vers ces problèmes. La première partie de cette thèse porte sur le VRP périodique avec des contraintes de fenêtres de temps (PVRPTW). Celui-ci est une extension du VRP classique avec fenêtres de temps (VRPTW) puisqu’il considère un horizon de planification de plusieurs jours pendant lesquels les clients n'ont généralement pas besoin d’être desservi à tous les jours, mais plutôt peuvent être visités selon un certain nombre de combinaisons possibles de jours de livraison. Cette généralisation étend l'éventail d'applications de ce problème à diverses activités de distributions commerciales, telle la collecte des déchets, le balayage des rues, la distribution de produits alimentaires, la livraison du courrier, etc. La principale contribution scientifique de la première partie de cette thèse est le développement d'une méta-heuristique hybride dans la quelle un ensemble de procédures de recherche locales et de méta-heuristiques basées sur les principes de voisinages coopèrent avec un algorithme génétique afin d’améliorer la qualité des solutions et de promouvoir la diversité de la population. Les résultats obtenus montrent que la méthode proposée est très performante et donne de nouvelles meilleures solutions pour certains grands exemplaires du problème. La deuxième partie de cette étude a pour but de présenter, modéliser et résoudre deux problèmes riches de tournées de véhicules, qui sont des extensions du VRPTW en ce sens qu'ils incluent des demandes dépendantes du temps de ramassage et de livraison avec des restrictions au niveau de la synchronization temporelle. Ces problèmes sont connus respectivement sous le nom de Time-dependent Multi-zone Multi-Trip Vehicle Routing Problem with Time Windows (TMZT-VRPTW) et de Multi-zone Mult-Trip Pickup and Delivery Problem with Time Windows and Synchronization (MZT-PDTWS). Ces deux problèmes proviennent de la planification des opérations de systèmes logistiques urbains à deux niveaux. La difficulté de ces problèmes réside dans la manipulation de deux ensembles entrelacés de décisions: la composante des tournées de véhicules qui vise à déterminer les séquences de clients visités par chaque véhicule, et la composante de planification qui vise à faciliter l'arrivée des véhicules selon des restrictions au niveau de la synchronisation temporelle. Auparavant, ces questions ont été abordées séparément. La combinaison de ces types de décisions dans une seule formulation mathématique et dans une même méthode de résolution devrait donc donner de meilleurs résultats que de considérer ces décisions séparément. Dans cette étude, nous proposons des solutions heuristiques qui tiennent compte de ces deux types de décisions simultanément, et ce, d'une manière complète et efficace. Les résultats de tests expérimentaux confirment la performance de la méthode proposée lorsqu’on la compare aux autres méthodes présentées dans la littérature. En effet, la méthode développée propose des solutions nécessitant moins de véhicules et engendrant de moindres frais de déplacement pour effectuer efficacement la même quantité de travail. Dans le contexte des systèmes logistiques urbains, nos résultats impliquent une réduction de la présence de véhicules dans les rues de la ville et, par conséquent, de leur impact négatif sur la congestion et sur l’environnement.For more than half of century, since the paper of Dantzig and Ramser (1959) was introduced, the Vehicle Routing Problem (VRP) has been one of the most extensively studied problems in operations research due to its methodological interest and practical relevance in many fields such as transportation, logistics, telecommunications, and production. The general goal of the VRP is to optimize the use of transportation resources to service customers with respect to side-constraints deriving from real-world applications. The practical applications of the VRP may have a variety of constraints, and obviously, the larger the set of constraints that need to be considered, i.e., corresponding to `richer' VRPs, the more difficult the task of problem solving. The needs to study closer representations of actual applications and methodologies producing high-quality solutions quickly to larger-sized application problems have increased steadily, providing significant challenges for the VRP research community. This dissertation explores these extensional issues of the VRP. The first part of the dissertation addresses the Periodic Vehicle Routing Problem with Time Windows (PVRPTW) which generalizes the classical Vehicle Routing Problem with Time Windows (VRPTW) by extending the planning horizon to several days where customers generally do not require delivery on every day, but rather according to one of a limited number of possible combinations of visit days. This generalization extends the scope of applications to many commercial distribution activities such as waste collection, street sweeping, grocery distribution, mail delivery, etc. The major contribution of this part is the development of a population-based hybrid meta-heuristic in which a set of local search procedures and neighborhood-based meta-heuristics cooperate with the genetic algorithm population evolution mechanism to enhance the solution quality as well as to promote diversity of the genetic algorithm population. The results show that the proposed methodology is highly competitive, providing new best solutions in some large instances. The second part of the dissertation aims to present, model and solve two rich vehicle routing problems which further extend the VRPTW with time-dependent demands of pickup and delivery, and hard time synchronization restrictions. They are called Time-dependent Multi-zone Multi-Trip Vehicle Routing Problem with Time Windows (TMZT-VRPTW), and Multi-zone Mult-Trip Pickup and Delivery Problem with Time Windows and Synchronization (MZT-PDTWS), respectively. These two problems originate from planning the operations of two-tiered City Logistics systems. The difficulty of these problems lies in handling two intertwined sets of decisions: the routing component which aims to determine the sequences of customers visited by each vehicle, and the scheduling component which consists in planning arrivals of vehicles at facilities within hard time synchronization restrictions. Previously, these issues have been addressed separately. Combining these decisions into one formulation and solution method should yield better results. In this dissertation we propose meta-heuristics that address the two decisions simultaneously, in a comprehensive and efficient way. Experiments confirm the good performance of the proposed methodology compared to the literature, providing system managers with solution requiring less vehicles and travel costs to perform efficiently the same amount of work. In the context of City Logistics systems, our results indicate a reduction in the presence of vehicles on the streets of the city and, thus, in their negative impact on congestion and environment

    A DECOMPOSITION-BASED HEURISTIC ALGORITHM FOR PARALLEL BATCH PROCESSING PROBLEM WITH TIME WINDOW CONSTRAINT

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    This study considers a parallel batch processing problem to minimize the makespan under constraints of arbitrary lot sizes, start time window and incompatible families. We first formulate the problem with a mixed-integer programming model. Due to the NP-hardness of the problem, we develop a decomposition-based heuristic to obtain a near-optimal solution for large-scale problems when computational time is a concern. A two-dimensional saving function is introduced to quantify the value of time and capacity space wasted. Computational experiments show that the proposed heuristic performs well and can deal with large-scale problems efficiently within a reasonable computational time. For the small-size problems, the percentage of achieving optimal solutions by the DH is 94.17%, which indicates that the proposed heuristic is very good in solving small-size problems. For large-scale problems, our proposed heuristic outperforms an existing heuristic from the literature in terms of solution quality

    Operations Research for Planning and Managing City Logistics Systems

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    The chapter presents the Operations Research models and methods to plan and manage City Logistics systems, in particular their supply components. It presents the main planning issues and challenges, and reviews the proposed methodologies. The chapter concludes with a discussion on perspectives for City Logistics and decision-support methodological developments

    Topics in logistics

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    Heuristic approaches for the Vehicle Routing Problem with Heterogeneous Fleet and Real Life Attributes

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    The Vehicle Routing Problem with all its variants and richness is still one of the most challenging Combinatorial Optimization problems in the Management Science / Operations Research arena since its introduction in the 1950s. In this research we introduce a real life Vehicle Routing Problem, inspired by the Gas Delivery industry in the UK. It has various real life attributes which have not been researched in the past, such as demand-dependant service times, light load requirements and allowable overtime coupled with unlimited vehicle fleet. A Mixed Integer formulation of the problem is presented and the problem is solved to optimality, reporting optimal solutions and lower and upper bounds. After solving the real life routing problem, both optimally and heuristically some interesting observations and practical implications are reported, relating to better fleet utilization and better working time utilization. We design three initial solution methods, namely the Adapted Sweep, the Adapted Nearest Neighbour and the Parallel Clustering method. They are motivated by the real attributes of the Vehicle Routing Problem under research and show a very good performance in terms of reaching a good initial solution quality as compared to other famous initial solution methods in the literature. Moreover, the Adapted Sweep and the Adapted Nearest Neighbour have computational times of less than one second. Two new hybrid metaheuristic methods are designed in order to address the real life Vehicle Routing Problem. A Population Variable Neighbourhood Search with Adaptive Memory Procedure is the first method, which aims to incorporate and hybridize the learning principles of Adaptive Memory into a method which does not make use of memory structures in its original form, namely the Variable Neighbourhood Search. Moreover, we use a Population version of the Variable Neighbourhood Search in order to provide diversification to the method and to aid the learning aspect of the method. The Population Variable Neighbourhood Search with Adaptive Memory Procedure was tested extensively on the real life Vehicle Routing Problem, as well as relevant literature benchmark instances and it was found to perform well in comparison with the optimal solutions we generated. Moreover, the method shows a good performance on the benchmark instances with less than 1% deviation from the Best Known Solutions in the literature. We later extend the Population Variable Neighbourhood Search with Adaptive Memory Procedure (PVNS_AMP) and hybridize it with aspects from Tabu Search in order to create the second new hybrid metaheuristic method, namely the Population Variable Neighbourhood Search with Adaptive Memory Procedure and Tabu Search principles (TS_PVNS_AMP). The TS_PVNS_AMP was found to have better performance on the RVRP without overtime, and superior performance on the RVRP with overtime as compared to the PVNS_AMP. Moreover, the TS_PVNS_AMP showed a better performance than the PVNS_AMP on the relevant literature benchmark instances reaching Best Known Solutions in the literature with less than 0.5 % deviation from the Best Known Solutions on average. We have also tested our proposed algorithms on other VRP problems, such as the Heterogeneous Fleet VRP with imposed fleet and the School Bus Routing Problem. We have done this experimentation in order to test the generalizability of the methods and their flexibility in addressing other problems from the Vehicle Routing family. Our methodology showed good performance on the literature benchmarks for both problems in terms of solution quality and computational time, as well as a good degree of flexibility in terms of finding good heuristic solutions

    Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

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    242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro
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