Modeling Heterogeneous Vehicle Routing Problem with Strict Time Schedule

Vehicle Routing Problem with time windows (VRPTW) is a well known combinatorial optimization problem normally to be used for obtaining the optimal set of routes used by a fleet of vehicles in logistic system. In VRPTW it is assumed that the fleet of vehicles are all homogeny. In this paper we consider a variant of the VRPTW in which the assumption of homogeny is dropped. Now the problem is called Heterogeneous VRP (HVRP). As the logistic company has so many customers, it puts a very strict restriction in time delivery for each vehicle used. Regarding to the structure of the problem we use integer programming approach to model the problem. A feasible neighbourhood method is developed to solve the model

Thirty years of heterogeneous vehicle routing

It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems

Optimisation de tournées de service en temps réel

RÉSUMÉ : Les tournées de service concernent l'organisation de déplacement de personnels vers des clients afin d'effectuer différentes activités techniques ou commerciales. Ces tournées peuvent devoir répondre à des objectifs et faire face à des contraintes nombreuses et complexes. Lors de la planification et de l'exécution de tournées de service mono-période, les entreprises sont confrontées aux aléas des temps de service et de parcours. C'est pourquoi, dans cette thèse, nous nous intéressons à une variante du problème de tournées de service, dans laquelle les temps de parcours et de service sont stochastiques. Il s'agit du problème de tournées de service multi-dépôt, incluant fenêtres de temps, temps de service et de parcours stochastiques avec priorité entre les clients (distinction clients obligatoires / clients optionnels). Afin de résoudre cette problématique, nous proposons trois méthodes différentes. Dans la première méthode, nous construisons d'abord des routes contenant uniquement des clients obligatoires puis nous procédons à l'insertion des clients optionnels. La deuxième méthode est une méthode approchée basée sur la génération de colonnes consistant à générer un ensemble de routes de bonne qualité pour chaque véhicule puis à en sélectionner une par véhicule. La dernière méthode est un algorithme de branch and price basé sur la deuxième méthode. Le sous-problème consiste à générer des routes réalisables pour un véhicule donné, tandis que le problème maître permet de sélectionner des routes en s'assurant que la priorité des clients est respectée. Après chacune de ces méthodes, afin d'évaluer la qualité de ces solutions face aux aléas, nous utilisons un algorithme de programmation dynamique et procédons à un ensemble de simulations du déroulement des tournées en temps réel. Nous avons testé ces méthodes sur des problèmes dont les données sont issues du milieu industriel.Mots-clés : Tournées de véhicules, multi-dépôt, fenêtres de temps, temps de service stochastiques, temps de parcours stochastiques, priorité entre les clients.----------ABSTRACT : The field service routing problem consists in assigning the visits of technicians to clients in order to satisfy their requests for service activities such as maintenance. When planning service routes, companies have to face hazardous travel and service times. Therefore, in this thesis, we deal with a variant of the single-period field service routing problem in which travel and service times are stochastic. It is the field service routing problem with multiple depots, time windows, stochastic travel and service times and priority within customers (distinguishing mandatory and optional customers). To solve this problem, we propose three different methods. In the first one, we first build routes containing only mandatory customers and then, we insert optional customers in these routes. The second one is a heuristic method based on column generation consisting in generating a set of valuable routes for each vehicle and then in selecting one route per vehicle. The last method is a branch and price algorithm, based on the second method, in which the subproblem consists in finding feasible routes for a given vehicle, whereas the master problem consists in selecting routes while ensuring that customer's priority is respected. After each of these methods, in order to evaluate the quality of these solutions regarding stochasticity, we use a dynamic programming algorithm and we proceed to a set of simulations of the real-time execution of the service activities over the period. All our experimentations have been made on problems coming from realistic data. Keywords : Vehicle routing, multi-depot, time windows, stochastic service times, stochastic travel times, priority within customers

Algorithms for vehicle routing problems with heterogeneous fleet, flexible time windows and stochastic travel times

Orientador: Vinícius Amaral ArmentanoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho aborda três variantes multiatributo do problema de roteamento de veículos. A primeira apresenta frota heterogênea, janelas de tempo invioláveis e tempos de viagem determinísticos. Para resolvê-la, são propostos algoritmos ótimos baseados na decomposição de Benders. Estes algoritmos exploram a estrutura do problema em uma formulação de programação inteira mista, e três diferentes técnicas são desenvolvidas para acelerá-los. A segunda variante contempla os atributos de frota heterogênea, janelas de tempo flexíveis e tempos de viagem determinísticos. As janelas de tempo flexíveis permitem o início do serviço nos clientes com antecipação ou atraso limitados em relação às janelas de tempo invioláveis, com custos de penalidade. Este problema é resolvido por extensões dos algoritmos de Benders, que incluem novos algoritmos de programação dinâmica para a resolução de subproblemas com a estrutura do problema do caixeiro viajante com janelas de tempo flexíveis. A terceira variante apresenta frota heterogênea, janelas de tempo flexíveis e tempos de viagem estocásticos, sendo representada por uma formulação de programação estocástica inteira mista de dois estágios com recurso. Os tempos de viagem estocásticos são aproximados por um conjunto finito de cenários, gerados por um algoritmo que os descreve por meio da distribuição de probabilidade Burr tipo XII, e uma matheurística de busca local granular é sugerida para a resolução do problema. Extensivos testes computacionais são realizados em instâncias da literatura, e as vantagens das janelas de tempo flexíveis e dos tempos de viagem estocásticos são enfatizadasAbstract: This work addresses three multi-attribute variants of the vehicle routing problem. The first one presents a heterogeneous fleet, hard time windows and deterministic travel times. To solve this problem, optimal algorithms based on the Benders decomposition are proposed. Such algorithms exploit the structure of the problem in a mixed-integer programming formulation, and three algorithmic enhancements are developed to accelerate them. The second variant comprises a heterogeneous fleet, flexible time windows and deterministic travel times. The flexible time windows allow limited early and late servicing at customers with respect to their hard time windows, at the expense of penalty costs. This problem is solved by extensions of the Benders algorithms, which include novel dynamic programming algorithms for the subproblems with the special structure of the traveling salesman problem with flexible time windows. The third variant presents a heterogeneous fleet, flexible time windows and stochastic travel times, and is represented by a two-stage stochastic mixed-integer programming formulation with recourse. The stochastic travel times are approximated by a finite set of scenarios generated by an algorithm which describes them using the Burr type XII distribution, and a granular local search matheuristic is suggested to solve the problem. Extensive computational tests are performed on instances from the literature, and the advantages of flexible windows and stochastic travel times are stressed.DoutoradoAutomaçãoDoutor em Engenharia Elétrica141064/2015-3CNP