Nesta tese, nós apresentamos uma compilação de três artigos de otimização aplicados no contexto de transporte urbano de ônibus. O principal objetivo foi estudar e implementar heurísticas com base em Pesquisa Operacional para otimizar problemas de (re)escalonamento de veículos off-line e on-line considerando várias garagens e frota heterogênea. No primeiro artigo, foi proposta uma abordagem heurística para o problema de escalonamento de veículos múltiplas garagens. Acreditamos que as principais contribuições são o método de geração de colunas para grandes instâncias e as técnicas de redução do espaço de estados para acelerar as soluções. No segundo artigo, adicionamos complexidade ao considerar a frota heterogênea, denotada como multiple depot vehicle type scheduling problem (MDVTSP). Embora a importância e a aplicabilidade do MDVTSP, formulações matemáticas e métodos de solução para isso ainda sejam relativamente inexplorados. A principal contribuição desse trabalho foi o método de geração de colunas para o problema com frota heterogênea, já que nenhuma outra proposta na literatura foi identificada no momento pelos autores. Na terceira parte desta tese, no entanto, nos concentramos no reescalonamento em tempo real para o caso de quebras definitivas de veículos. A principal contribuição é a abordagem eficiente do reescalonamento sob uma quebra. A abordagem com redução de espaço de estados, solução inicial e método de geração de colunas possibilitou uma ação realmente em tempo real. Em menos de cinco minutos, reescalonando todas as viagens restantes. / In this dissetation we presented a three articles compilation in urban bus transportation optimization. The main objective was to study and implement heuristic solutions method based on Operations Research to optimizing offline and online vehicle (re)scheduling problems considering multiple depots and heterogeneous fleet. In the first paper, a fast heuristic approach to deal with the multiple depot vehicle scheduling problem was proposed. We think the main contributions are the column generation framework for large instances and the state-space reduction techniques for accelerating the solutions. In the second paper, we added complexity when considering the heterogeneous fleet, denoted as "the multiple-depot vehicle-type scheduling problem" (MDVTSP). Although the MDVTSP importance and applicability, mathematical formulations and solution methods for it are still relatively unexplored. We think the main contribution is the column generation framework for instances with heterogeneous fleet since no other proposal in the literature has been identified at moment by the authors. In the third part of this dissertation, however, we focused on the real-time schedule recovery for the case of serious vehicle failures. Such vehicle breakdowns require that the remaining passengers from the disabled vehicle, and those expected to become part of the trip, to be picked up. In addition, since the disabled vehicle may have future trips assigned to it, the given schedule may be deteriorated to the extent where the fleet plan may need to be adjusted in real-time depending on the current state of what is certainly a dynamic system. Usually, without the help of a rescheduling algorithm, the dispatcher either cancels the trips that are initially scheduled to be implemented by the disabled vehicle (when there are upcoming future trips planned that could soon serve the expected demand for the canceled trips), or simply dispatches an available vehicle from a depot. In both cases, there may be considerable delays introduced. This manual approach may result in a poor solution. The implementation of new technologies (e.g., automatic vehicle locators, the global positioning system, geographical information systems, and wireless communication) in public transit systems makes it possible to implement real-time vehicle rescheduling algorithms at low cost. The main contribution is the efficient approach to rescheduling under a disruption. The approach with integrated state-space reduction, initial solution, and column generation framework enable a really real-time action. In less than five minutes rescheduling all trips remaining.

Nesta tese, nós apresentamos uma compilação de três artigos de otimização aplicados no contexto de transporte urbano de ônibus. O principal objetivo foi estudar e implementar heurísticas com base em Pesquisa Operacional para otimizar problemas de (re)escalonamento de veículos off-line e on-line considerando várias garagens e frota heterogênea. No primeiro artigo, foi proposta uma abordagem heurística para o problema de escalonamento de veículos múltiplas garagens. Acreditamos que as principais contribuições são o método de geração de colunas para grandes instâncias e as técnicas de redução do espaço de estados para acelerar as soluções. No segundo artigo, adicionamos complexidade ao considerar a frota heterogênea, denotada como multiple depot vehicle type scheduling problem (MDVTSP). Embora a importância e a aplicabilidade do MDVTSP, formulações matemáticas e métodos de solução para isso ainda sejam relativamente inexplorados. A principal contribuição desse trabalho foi o método de geração de colunas para o problema com frota heterogênea, já que nenhuma outra proposta na literatura foi identificada no momento pelos autores. Na terceira parte desta tese, no entanto, nos concentramos no reescalonamento em tempo real para o caso de quebras definitivas de veículos. A principal contribuição é a abordagem eficiente do reescalonamento sob uma quebra. A abordagem com redução de espaço de estados, solução inicial e método de geração de colunas possibilitou uma ação realmente em tempo real. Em menos de cinco minutos, reescalonando todas as viagens restantes. / In this dissetation we presented a three articles compilation in urban bus transportation optimization. The main objective was to study and implement heuristic solutions method based on Operations Research to optimizing offline and online vehicle (re)scheduling problems considering multiple depots and heterogeneous fleet. In the first paper, a fast heuristic approach to deal with the multiple depot vehicle scheduling problem was proposed. We think the main contributions are the column generation framework for large instances and the state-space reduction techniques for accelerating the solutions. In the second paper, we added complexity when considering the heterogeneous fleet, denoted as "the multiple-depot vehicle-type scheduling problem" (MDVTSP). Although the MDVTSP importance and applicability, mathematical formulations and solution methods for it are still relatively unexplored. We think the main contribution is the column generation framework for instances with heterogeneous fleet since no other proposal in the literature has been identified at moment by the authors. In the third part of this dissertation, however, we focused on the real-time schedule recovery for the case of serious vehicle failures. Such vehicle breakdowns require that the remaining passengers from the disabled vehicle, and those expected to become part of the trip, to be picked up. In addition, since the disabled vehicle may have future trips assigned to it, the given schedule may be deteriorated to the extent where the fleet plan may need to be adjusted in real-time depending on the current state of what is certainly a dynamic system. Usually, without the help of a rescheduling algorithm, the dispatcher either cancels the trips that are initially scheduled to be implemented by the disabled vehicle (when there are upcoming future trips planned that could soon serve the expected demand for the canceled trips), or simply dispatches an available vehicle from a depot. In both cases, there may be considerable delays introduced. This manual approach may result in a poor solution. The implementation of new technologies (e.g., automatic vehicle locators, the global positioning system, geographical information systems, and wireless communication) in public transit systems makes it possible to implement real-time vehicle rescheduling algorithms at low cost. The main contribution is the efficient approach to rescheduling under a disruption. The approach with integrated state-space reduction, initial solution, and column generation framework enable a really real-time action. In less than five minutes rescheduling all trips remaining.

Nesta tese, nós apresentamos uma compilação de três artigos de otimização aplicados no contexto de transporte urbano de ônibus. O principal objetivo foi estudar e implementar heurísticas com base em Pesquisa Operacional para otimizar problemas de (re)escalonamento de veículos off-line e on-line considerando várias garagens e frota heterogênea. No primeiro artigo, foi proposta uma abordagem heurística para o problema de escalonamento de veículos múltiplas garagens. Acreditamos que as principais contribuições são o método de geração de colunas para grandes instâncias e as técnicas de redução do espaço de estados para acelerar as soluções. No segundo artigo, adicionamos complexidade ao considerar a frota heterogênea, denotada como multiple depot vehicle type scheduling problem (MDVTSP). Embora a importância e a aplicabilidade do MDVTSP, formulações matemáticas e métodos de solução para isso ainda sejam relativamente inexplorados. A principal contribuição desse trabalho foi o método de geração de colunas para o problema com frota heterogênea, já que nenhuma outra proposta na literatura foi identificada no momento pelos autores. Na terceira parte desta tese, no entanto, nos concentramos no reescalonamento em tempo real para o caso de quebras definitivas de veículos. A principal contribuição é a abordagem eficiente do reescalonamento sob uma quebra. A abordagem com redução de espaço de estados, solução inicial e método de geração de colunas possibilitou uma ação realmente em tempo real. Em menos de cinco minutos, reescalonando todas as viagens restantes. / In this dissetation we presented a three articles compilation in urban bus transportation optimization. The main objective was to study and implement heuristic solutions method based on Operations Research to optimizing offline and online vehicle (re)scheduling problems considering multiple depots and heterogeneous fleet. In the first paper, a fast heuristic approach to deal with the multiple depot vehicle scheduling problem was proposed. We think the main contributions are the column generation framework for large instances and the state-space reduction techniques for accelerating the solutions. In the second paper, we added complexity when considering the heterogeneous fleet, denoted as "the multiple-depot vehicle-type scheduling problem" (MDVTSP). Although the MDVTSP importance and applicability, mathematical formulations and solution methods for it are still relatively unexplored. We think the main contribution is the column generation framework for instances with heterogeneous fleet since no other proposal in the literature has been identified at moment by the authors. In the third part of this dissertation, however, we focused on the real-time schedule recovery for the case of serious vehicle failures. Such vehicle breakdowns require that the remaining passengers from the disabled vehicle, and those expected to become part of the trip, to be picked up. In addition, since the disabled vehicle may have future trips assigned to it, the given schedule may be deteriorated to the extent where the fleet plan may need to be adjusted in real-time depending on the current state of what is certainly a dynamic system. Usually, without the help of a rescheduling algorithm, the dispatcher either cancels the trips that are initially scheduled to be implemented by the disabled vehicle (when there are upcoming future trips planned that could soon serve the expected demand for the canceled trips), or simply dispatches an available vehicle from a depot. In both cases, there may be considerable delays introduced. This manual approach may result in a poor solution. The implementation of new technologies (e.g., automatic vehicle locators, the global positioning system, geographical information systems, and wireless communication) in public transit systems makes it possible to implement real-time vehicle rescheduling algorithms at low cost. The main contribution is the efficient approach to rescheduling under a disruption. The approach with integrated state-space reduction, initial solution, and column generation framework enable a really real-time action. In less than five minutes rescheduling all trips remaining.

The master's thesis deals with the new modification of vehicle routing problem -- 3PL vehicle routing problem with heterogeneous fleet and split delivery. In addition to classical vehicle routing problem, we consider a heterogeneous suppliers fleet and also external carrier, which charges a fixed value per unit of transported goods. The reader is first introduces to vehicle routing problem, its history and possible solutions. Furthermore, the reader is acquainted with logistics and logistics providers. In the main part of this thesis is described 3PL vehicle routing problem and its mathematical model. At first we try to get optimal solution via CPLEX solver. But since this is an NP-hard task, heuristic method is proposed (in two variants) for solving this problem. The heuristic is then tested on the selected test tasks. Results obtained using the proposed heuristics are compared with the optimal solution. Even larger problems are then solved using this heuristics. In the end other modifications and possible improvements of this heuristic method are proposed.

A comprehensive study is presented of the Capacitated Multi DepotGreen Vehicle Routing Problem (CMDGVRP) applied to a heterogeneous fleet of electronic waste collecting vehicles with two objectives: to reduce the total fuel consumption of the vehicles (environmental sustainability) and to limit the continuous drive-time of the drivers (social sustainability). Research has been limited from this aspect, and in this study, the focus is on the practical application of pickup and delivery of electronic waste. The study also presents results for the online dynamic routing variant of this problem, where traffic congestion appears mid-route. A detailed analysis and parameter optimization has been done for Simulated Annealing, Genetic algorithm(GA), along with more advanced variants like Non-dominated Sorting GA (NSGA II), NSGA III, UNSGA III, and Indicator-Based Selection Evolutionary Algorithm (IBEA). Additionally, the Gini index is used to create a multi-objective model, which is novel in the context of CMDGVRP to the best of our knowledge. The use of the Gini index in the field of CMDGVRP shows excellent potential in balancing environmental, economic, and social sustainability. An extension of the CMDGVRP is introduced where vehicles can visit dropoff locations mid-route and then continue with a new route. This implementation is novel to our knowledge and is named Drop-and-continue. It is shown to increase the performance on large datasets. Results are presented from realistic simulation studies on a public dataset, with varying route lengths and vehicle fleet sizes, along with a real-world dataset from a waste collection company in Sweden. The results show that the optimal choice of algorithm depends on the dataset size and if there is a maximum budget of evaluations or computation time. Realistic problems are solved in a matter of a few seconds, given that they are initiated well. Simulated Annealing and Genetic algorithm prove to be very competitive in the case of large problems and limited computation time budget.

A concern of logistics management is the correct and efficient use of the available fleet. The central focus of fleet management is determining the routes that will be used in customer service and the efficient allocation of available resources (vehicles). The correct fleet management can generate a competitive advantage. There is a problem in the Operations Research dedicated to working this type of situation, the Vehicle Routing Problem (VRP). The VRP tries to generate the most economical route to efficient use of the available fleet. The case study discussed in this work was a particular situation VRP where there is a heterogeneous fleet and where the collections and deliveries of passengers are carried at separate times. To solve this problem we designed a Genetic Algorithm. Additionally, three different crossover operators were tested in the search for better results. At the end of the study, the Genetic Algorithm was capable of solving the problem in a short time and finding the most economical way to generate routes, using efficiently the fleet and fulfilling all requests. / Uma das preocupaÃÃes da gestÃo logÃstica à a correta e eficiente utilizaÃÃo da frota disponÃvel. O foco central da gestÃo da frota està em determinar as rotas que serÃo utilizadas no atendimento aos clientes e a alocaÃÃo eficiente dos recursos (veÃculos) disponÃveis. A gestÃo correta da frota pode gerar um diferencial competitivo. Existe na Pesquisa Operacional um problema dedicado a trabalhar este tipo de situaÃÃo, denominado Problema de Roteamento de VeÃculos (PRV). O PRV procura gerar a rota mais econÃmica com utilizaÃÃo eficiente da frota disponÃvel. No estudo de caso, realizado neste trabalho, foi abordada uma situaÃÃo particular do PRV onde hà uma frota heterogÃnea e as coletas e entregas de passageiros sÃo realizadas em momentos separados. Para a resoluÃÃo deste problema foi desenvolvido e implementado um Algoritmo GenÃtico (AG). Adicionalmente, trÃs operadores de cruzamento diferentes foram testados na busca dos melhores resultados encontrados pelo AG. Ao final, o Algoritmo GenÃtico conseguiu se mostrar capaz de resolver o problema em tempo hÃbil e de maneira a gerar rotas mais econÃmicas, utilizando eficientemente a frota e atendendo todas as solicitaÃÃes.

O tema de roteamento de veículos é de grande importância na literatura e tem sido amplamente estudada pela sua importância para muitas indústrias. Com a evolução na literatura, mais características foram adicionadas para torná-lo mais próximo de situações reais. Alinhado com esta tendência, este trabalho aborda o problema de roteamento de veículos quando há a terceirização da frota que realiza as entregas. Uma forma de cobrança do frete é por meio de custos escalonados, que são calculados de acordo com o tipo de veículo e a distância percorrida, com valores fixos para cada faixa de distância. Embora seja uma forma comum de trabalho na indústria, nenhum trabalho focado nesta característica foi encontrado na literatura. Este problema é o problema de roteamento de veículos com frota mista, janelas de tempo e custos escalonados (FSMVRPTWSC). Ao abordar este problema, este trabalho apresenta um modelo de programação linear inteira mista que é avaliado em um cenário real da indústria. Além disso, três heurísticas de inserção sequencial são propostas para lidar com problemas maiores. Estes métodos são examinados por meio de testes computacionais em 168 problemas de referência gerados para este problema. Os experimentos numéricos mostram que os métodos são robustos e eficientes, apresentando um bom desempenho em conjuntos de problemas com diversas características. / The theme of vehicle routing is of great importance in the literature and has been widely studied for its relevance to many industries and, throughout the literature, more characteristics have been added to make it closer to real situations. Aligned with this trend, this paper addresses the vehicle routing problem when there is outsourcing of the fleet that delivers goods. One form of freight charging is by scaled costs, which are calculated according to the type of vehicle and the distance traveled, with fixed values for each distance range. Though it is a common form of work in the industry, no work focused on this characteristic was found in the literature. This problem is the fleet size and mix vehicle routing problem with time windows and scaled costs (FSMVRPTWSC). In approaching this problem, this paper presents a mixed integer linear programming model that is evaluated under a real situation scenario. Furthermore, three sequential insertion heuristics are proposed in order to deal with larger problems. These methods are examined through a computational comparative study in 168 benchmark problems generated for this problem. The numerical experiments show that the methods are robust and efficient, performing well in different problem sets.

Esta pesquisa trata da aplicação de meta-heurísticas baseadas em busca em vizinhança variável em problemas de operação de transportes. Desta forma, buscou-se encontrar problemas complexos durante a operação de sistemas de transportes, nas grandes cidades, que possam ser resolvidos com a aplicação de meta-heurística baseada em busca em vizinhança variável. Este trabalho aborda dois diferentes problemas de planejamento e operação de transportes. O primeiro problema abordado neste trabalho é o Problema de Programação da Tabela de Horários, de Veículos e de Tripulantes de Ônibus, no qual as viagens que comporão a tabela de horários, os veículos que executarão as viagens e as tripulações que operarão os veículos são alocadas simultaneamente e de maneira integrada. O segundo problema a ser abordado é o problema de distribuição física, o qual envolve o agrupamento e a alocação de entregas a uma frota de veículos visando minimizar o frete total. Uma abordagem para a modelagem matemática deste problema é modelar como um problema de bin-packing, com bins de tamanho variável unidimensional (do inglês Variable Sized Bin-Packing Problem - VSBPP), ou seja, uma generalização do tradicional problema de bin-packing no qual bins (veículos) de diferentes capacidades e custos estão disponíveis para a alocação de um conjunto de objetos (cargas), de modo que o custo total dos bins (veículos) utilizados seja mínimo. A outra abordagem proposta para o problema de distribuição física é modelar o problema como um problema de bin-packing, com bins de tamanho variável bidimensional (do inglês Bidimensional Variable Sized Bin-Packing Problem BiD-VSBPP). Assim sendo, trata-se de uma expansão do problema de bin-packing com bins de tamanho variável unidimensional (VSBPP), no qual bins (veículos) de diferentes capacidades (capacidade volumétrica e capacidade de carga) e custos estão disponíveis para a alocação de um conjunto de objetos (cargas), os quais possuem as dimensões peso e volume, de modo que o custo total dos bins (veículos) utilizados seja mínimo. Durante a realização deste trabalho, foi desenvolvido um programa computacional em C++, o qual implementa a meta-heurística Busca em Vizinhança Variável (VNS) e duas meta-heurísticas baseadas em VNS. São apresentados resultados de experimentos computacionais com dados reais e dados benchmarking. Os resultados obtidos comprovam a eficácia das meta-heurísticas propostas. / This work approaches variable neighborhood search meta-heuristic applicate on transport operation problems. This way, we sought find complex transport operation problems in large cities that can be solved with the variable neighborhood search meta-heuristic application. This work approaches two different transport planning and operation problems. The first problem approached in this paper is the Bus Timetable Vehicle Crew Scheduling Problem, in which timetabling, bus and crew schedules are simultaneously determined in an integrated approach. The second problem to be approached is the physical distribution problem which comprises grouping and assigning deliveries to a heterogeneous fleet of vehicles aiming to minimize the total freight cost. The problem can be mathematical modeled as one-dimensional Variable Sized Bin-Packing Problem (VSBPP), a generalization of the traditional bin-packing problem, in which bins (vehicles) with different sizes and costs are available for the assignment of the objects (deliveries) such that the total cost of the used bins (vehicles) is minimized. Another proposed approach to the problem of physical distribution is model as two dimensional Variable Sized Bin-Packing Problem (BiD-VSBPP). Therefore, it is an expansion of the bin-packing problem with bins variable-length-dimensional (VSBPP), in which bins (vehicle) of different capacity (capacity and load carrying capacity) and costs are available for allocation a set of objects (loads), which have the dimensions weight and volume, so that minimized the total cost of bins (vehicle). In this work, was developed a C++ software implemented, which was implemented a meta-heuristic Variable Neighborhood Search (VNS) and two others meta-heuristics based on VNS. Computational results for real-world problems and benchmarking problems are presented, showing the effectiveness of these proposed meta-heuristics.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-01-31T14:48:27Z No. of bitstreams: 1 Roberto da Silva Araujo_.pdf: 903723 bytes, checksum: ae702a685139c36126c0bfef5f707282 (MD5) / Made available in DSpace on 2018-01-31T14:48:27Z (GMT). No. of bitstreams: 1 Roberto da Silva Araujo_.pdf: 903723 bytes, checksum: ae702a685139c36126c0bfef5f707282 (MD5) Previous issue date: 2017-10-31 / Nenhuma / Neste trabalho é apresentado um Algoritmo Híbrido (AH) aplicado ao Problema de Roteamento de Veículos com Múltiplos Depósitos e Frota Heterogênea (PRVMDFH). Um conjunto de clientes é atendido por um número fixo de veículos de diferentes tamanhos, por múltiplos depósitos, sujeito a restrições da capacidade do veículo. As meta-heurísticas utilizadas na construção do AH são o Algoritmo Genético (AG) e a Busca Tabu (BT). O AG usa operadores de cruzamento Mapeado Parcialmente (PMX), Cromossomo de Duas Partes (TCX) e de Ordem (OX), o operador de mutação Troca e o Algoritmo de Busca Local (ABL). A BT usa os métodos de Troca, Retirada e Inserção e Deslocamento. São analisadas as políticas de diversificação no ABL e a intensificação com as estratégias de geração de vizinhança na BT. São utilizados conjuntos de dados padrões de problemas testes, para executar os algoritmos propostos. Os resultados obtidos, comparados a outros autores, apresentaram boas soluções para diferentes tamanhos de problemas testes. / This paper presents a Hybrid Algorithm (AH) applied to the Problem of Vehicle Routing with Multiple-Deposit and Heterogeneous Fleet (MDFHPRV). A number of customers are served with a fixed number of vehicles of different sizes by multi-depot, subject to capacity constraints of the vehicle. The meta-heuristics used in AH construction are Genetic Algorithm (GA) and Tabu Search (BT). The AG uses Partial Mapped Crossover (PMX), Two Part Chromosome Crossover (TCX) and Order Crossover (OX) operators, the Exchange mutation operator, and the Local Search Algorithm (ABL). BT uses the Exchange, Retrieve, and Insertion and Displacement methods. Diversification policies was analyzed in the ABL and the intensification with the neighborhood generation strategies in BT. Standard sets of test problems are used to execute the proposed algorithms. The obtained results, compared to other authors, presented good solutions for different sizes of test problems.

O tema de roteamento de veículos é de grande importância na literatura e tem sido amplamente estudada pela sua importância para muitas indústrias. Com a evolução na literatura, mais características foram adicionadas para torná-lo mais próximo de situações reais. Alinhado com esta tendência, este trabalho aborda o problema de roteamento de veículos quando há a terceirização da frota que realiza as entregas. Uma forma de cobrança do frete é por meio de custos escalonados, que são calculados de acordo com o tipo de veículo e a distância percorrida, com valores fixos para cada faixa de distância. Embora seja uma forma comum de trabalho na indústria, nenhum trabalho focado nesta característica foi encontrado na literatura. Este problema é o problema de roteamento de veículos com frota mista, janelas de tempo e custos escalonados (FSMVRPTWSC). Ao abordar este problema, este trabalho apresenta um modelo de programação linear inteira mista que é avaliado em um cenário real da indústria. Além disso, três heurísticas de inserção sequencial são propostas para lidar com problemas maiores. Estes métodos são examinados por meio de testes computacionais em 168 problemas de referência gerados para este problema. Os experimentos numéricos mostram que os métodos são robustos e eficientes, apresentando um bom desempenho em conjuntos de problemas com diversas características. / The theme of vehicle routing is of great importance in the literature and has been widely studied for its relevance to many industries and, throughout the literature, more characteristics have been added to make it closer to real situations. Aligned with this trend, this paper addresses the vehicle routing problem when there is outsourcing of the fleet that delivers goods. One form of freight charging is by scaled costs, which are calculated according to the type of vehicle and the distance traveled, with fixed values for each distance range. Though it is a common form of work in the industry, no work focused on this characteristic was found in the literature. This problem is the fleet size and mix vehicle routing problem with time windows and scaled costs (FSMVRPTWSC). In approaching this problem, this paper presents a mixed integer linear programming model that is evaluated under a real situation scenario. Furthermore, three sequential insertion heuristics are proposed in order to deal with larger problems. These methods are examined through a computational comparative study in 168 benchmark problems generated for this problem. The numerical experiments show that the methods are robust and efficient, performing well in different problem sets.