In this paper,we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP).The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm out performs existing solution methods for the 2E-VRP and achieves excellent results on the LRP.

A novel Ant Colony Optimization (ACO) framework for a dynamic environment has been proposed in this study. This algorithm was developed to solve Dynamic Traveling Salesman Problems more efficiently than the current algorithms. Adaptive Large Neighborhood Search based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes in literature to maintain diversity via transferring knowledge to the pheromone trails from previous environments. Numerical results indicate that the proposed algorithm can handle dynamicity in the environment more efficiently compared to other immigrant-based ACOs available in the literature. A real-life case study for wildlife surveillance by unmanned aerial vehicles has also been developed and solved using the proposed algorithm.

Especialmente nas últimas quatro décadas, muitos estudos se voltaram às variáveis determinantes para a implementação efetiva de sistemas flexíveis de manufatura, tais como seu design, sequenciamento e controle. Neste ínterim, o manejo apropriado do conjunto de ferramentas necessárias para a fabricação de um respectivo lote de produtos foi destacado como fator crucial no desempenho do sistema de produção como um todo. Neste trabalho, abordamos a otimização do número de inserções e remoções de ferramentas no magazine de uma ou mais máquinas numericamente controladas, admitindo-se que uma parcela significativa do tempo de produção é dispensada com estas trocas de ferramentas. De forma mais precisa, a minimização do número de trocas de ferramentas consiste em determinar a ordem de processamento de um conjunto de tarefas, bem como o carregamento ótimo do(s) compartimento(s) de ferramentas da(s) máquina(s), a fim de que o número de trocas seja minimizado. Como demostrado na literatura, mesmo o caso restrito à existência de apenas uma máquina de manufatura (MTSP, do inglês Minimization of Tool Switches Problem) é um problema NP-difícil, o que pode justificar o fato observado de que a maioria dos métodos de solução existentes o abordam de maneira heurística. Consequentemente, concluímos que a extensão ao contexto de múltiplas máquinas é também um problema NP-difícil, intrinsecamente complicado de se resolver. Nosso objetivo consiste em estudar formas eficientes de otimizar o número de trocas de ferramentas em ambientes equipados com máquinas flexíveis de manufatura. Para tanto, abordamos o problema básico, MTSP, e duas de suas variantes, em níveis crescentes de abrangência, que consideram o sequenciamento de tarefas em um conjunto de: (i) máquinas paralelas e idênticas (IPMTC, do inglês Identical Parallel Machines problem with Tooling Constraints); e (ii) máquinas paralelas e idênticas inseridas em um ambiente do tipo job shop (JSSPTC, do inglês Job Shop Scheduling Problem with Tooling Constraints). Classificamos as principais contribuições desta tese com respeito a três aspectos. Primeiramente, empurramos as fronteiras da literatura do MTSP propondo formulações matemáticas para os problemas IPMTC e JSSPTC. Desenvolvemos, também, algoritmos baseados em diferentes técnicas de resolução, como redução de domínio, Path relinking, Adaptive large neighborhood search e a elaboração de regras de despacho. Por último, com o intuito de bem avaliar a eficiência e o alcance de nossos métodos, propomos três novos conjuntos de instâncias teste. Acreditamos, assim, que este trabalho contribui positivamente com pesquisas futuras em um cenário abrangente dentro da minimização das trocas de ferramentas em um sistema flexível de manufatura. / Several studies, especially in the last four decades, have focused on decisive elements for the effective implementation of flexible manufacturing systems, such as their design, scheduling and control. In the meantime, the appropriate management of the set of tools needed to manufacture a certain lot of products has been highlighted as a crucial factor in the performance of the production system as a whole. This work deals with the optimization of the number of insertions and removals from the magazine of one or more numerical controlled machines, assuming that a significant part of the production time is wasted with such tool switches. More precisely, the minimization of tool switches problem (MTSP) consists on determining the processing order of a set of jobs, as well as the optimal loading of the magazine(s) of the machine(s), so that the total number of switches is minimized. As formally demonstrated in the literature, the MTSP is a NP-hard problem even when considering the existence of only one manufacturing machine, which could justify the fact that most of the solution methods tackles it heuristically. We thus conclude that its extension to the case of multiples machines is also NP-hard and, therefore, a problem intrinsically difficult to solve. Our goal consists in studying efficient ways to optimize the number of tool switches in environments equipped with flexible manufacturing machines. For that, we address the basic problem, MTSP, and two MTSP variants, in increasing levels of reach, that consider the job sequencing in a set of: (i) identical parallel machines (Identical Parallel Machines problem with Tooling Constraints, IPMTC); and (ii) identical parallel machines inserted in a job shop environment (Job Shop Scheduling Problem with Tooling Constraints, JSSPTC). The main contributions of this thesis are classified according three aspects. First, we pushed the frontier of the MTSP literature by proposing mathematical formulations for IPMTC and JSSPTC. We also developed algorithms based on different solution techniques, such as domain reduction, Path Relinking, Adaptive Large Neighborhood Search and dispatching rules. Finally, to fully evaluate the effectiveness and limits of our methods, three new sets of benchmark instances were generated. We believe that this work contributes positively to the future of research in a broad scenario inside the minimization of tool switches in flexible manufacturing systems.

Especialmente nas últimas quatro décadas, muitos estudos se voltaram às variáveis determinantes para a implementação efetiva de sistemas flexíveis de manufatura, tais como seu design, sequenciamento e controle. Neste ínterim, o manejo apropriado do conjunto de ferramentas necessárias para a fabricação de um respectivo lote de produtos foi destacado como fator crucial no desempenho do sistema de produção como um todo. Neste trabalho, abordamos a otimização do número de inserções e remoções de ferramentas no magazine de uma ou mais máquinas numericamente controladas, admitindo-se que uma parcela significativa do tempo de produção é dispensada com estas trocas de ferramentas. De forma mais precisa, a minimização do número de trocas de ferramentas consiste em determinar a ordem de processamento de um conjunto de tarefas, bem como o carregamento ótimo do(s) compartimento(s) de ferramentas da(s) máquina(s), a fim de que o número de trocas seja minimizado. Como demostrado na literatura, mesmo o caso restrito à existência de apenas uma máquina de manufatura (MTSP, do inglês Minimization of Tool Switches Problem) é um problema NP-difícil, o que pode justificar o fato observado de que a maioria dos métodos de solução existentes o abordam de maneira heurística. Consequentemente, concluímos que a extensão ao contexto de múltiplas máquinas é também um problema NP-difícil, intrinsecamente complicado de se resolver. Nosso objetivo consiste em estudar formas eficientes de otimizar o número de trocas de ferramentas em ambientes equipados com máquinas flexíveis de manufatura. Para tanto, abordamos o problema básico, MTSP, e duas de suas variantes, em níveis crescentes de abrangência, que consideram o sequenciamento de tarefas em um conjunto de: (i) máquinas paralelas e idênticas (IPMTC, do inglês Identical Parallel Machines problem with Tooling Constraints); e (ii) máquinas paralelas e idênticas inseridas em um ambiente do tipo job shop (JSSPTC, do inglês Job Shop Scheduling Problem with Tooling Constraints). Classificamos as principais contribuições desta tese com respeito a três aspectos. Primeiramente, empurramos as fronteiras da literatura do MTSP propondo formulações matemáticas para os problemas IPMTC e JSSPTC. Desenvolvemos, também, algoritmos baseados em diferentes técnicas de resolução, como redução de domínio, Path relinking, Adaptive large neighborhood search e a elaboração de regras de despacho. Por último, com o intuito de bem avaliar a eficiência e o alcance de nossos métodos, propomos três novos conjuntos de instâncias teste. Acreditamos, assim, que este trabalho contribui positivamente com pesquisas futuras em um cenário abrangente dentro da minimização das trocas de ferramentas em um sistema flexível de manufatura. / Several studies, especially in the last four decades, have focused on decisive elements for the effective implementation of flexible manufacturing systems, such as their design, scheduling and control. In the meantime, the appropriate management of the set of tools needed to manufacture a certain lot of products has been highlighted as a crucial factor in the performance of the production system as a whole. This work deals with the optimization of the number of insertions and removals from the magazine of one or more numerical controlled machines, assuming that a significant part of the production time is wasted with such tool switches. More precisely, the minimization of tool switches problem (MTSP) consists on determining the processing order of a set of jobs, as well as the optimal loading of the magazine(s) of the machine(s), so that the total number of switches is minimized. As formally demonstrated in the literature, the MTSP is a NP-hard problem even when considering the existence of only one manufacturing machine, which could justify the fact that most of the solution methods tackles it heuristically. We thus conclude that its extension to the case of multiples machines is also NP-hard and, therefore, a problem intrinsically difficult to solve. Our goal consists in studying efficient ways to optimize the number of tool switches in environments equipped with flexible manufacturing machines. For that, we address the basic problem, MTSP, and two MTSP variants, in increasing levels of reach, that consider the job sequencing in a set of: (i) identical parallel machines (Identical Parallel Machines problem with Tooling Constraints, IPMTC); and (ii) identical parallel machines inserted in a job shop environment (Job Shop Scheduling Problem with Tooling Constraints, JSSPTC). The main contributions of this thesis are classified according three aspects. First, we pushed the frontier of the MTSP literature by proposing mathematical formulations for IPMTC and JSSPTC. We also developed algorithms based on different solution techniques, such as domain reduction, Path Relinking, Adaptive Large Neighborhood Search and dispatching rules. Finally, to fully evaluate the effectiveness and limits of our methods, three new sets of benchmark instances were generated. We believe that this work contributes positively to the future of research in a broad scenario inside the minimization of tool switches in flexible manufacturing systems.

Diese Dissertation verknüpft eisenbahnbetriebswissenschaftliche Grundlagen mit Methoden aus den Disziplinen Unternehmensforschung (Operations Research) und Maschinelles Lernen. Gegenstand ist die auf den mittelfristigen Zeithorizont bezogene Ressourcenplanung von Knoten des Schienengüterverkehrs, die sogenannte taktische Planung. Diese spielt eine wesentliche Rolle für eine wirtschaftliche und qualitativ hochwertige Betriebsdurchführung. Knoten des Schienengüterverkehrs stellen neuralgische Punkte in der Transportkette von Waren auf der Schiene dar. Sie dienen der Durchführung einer Vielzahl unterschiedlicher betrieblicher Prozesse zur Sicherstellung eines definierten Outputs an Zügen in Abhängigkeit eines jeweils gegebenen Inputs. Die Bereitstellung eines zu den Betriebsanforderungen passenden Ressourcengerüsts ist Teil der taktischen Planung und hat wesentlichen Einfluss auf die Qualität der Prozesse in den Knoten, im Speziellen, sowie auf die vor- und nachgelagerte Transportdurchführung im Allgemeinen. Die Bemessung des notwendigen Personals, der Betriebsmittel und der Infrastruktur für einen Betriebstag, die sogenannte Ressourcendimensionierung, ist in der Praxis geprägt durch einen erheblichen manuellen Aufwand sowie eine große Abhängigkeit von der Datenqualität. Vor diesem Hintergrund und zur Überwindung dieser Nachteile schlägt diese Dissertation ein neues Verfahren zur Ressourcendimensionierung vor. Exemplarisch wird der Fokus auf die großen Knoten des Einzelwagenverkehrs gelegt, die sogenannten Rangierbahnhöfe. In diesen werden Eingangszüge zerlegt, Güterwagen entsprechend ihrer Ausgangsrichtung sortiert und gesammelt, sowie neue Ausgangszüge gebildet und bereitgestellt. Nach dem Stand der Technik werden für die Ressourcendimensionierung mehrere Monate bis wenige Wochen vor der Betriebsdurchführung Rangierarbeitspläne erstellt. Diese umfassen einen detaillierten Arbeitsfolgenplan inklusive Terminierung von Prozessen sowie deren Ressourcenbelegung. Die Rangierarbeitspläne bilden die Grundlage für die Ressourcenanforderung. Aufgrund sich ändernder Nebenbedingungen vor dem Betriebstag und dem stochastischen Charakter der Betriebsprozesse sowohl im Netz als auch in den Knoten können die in der taktischen Planung erstellten Rangierarbeitspläne nur begrenzt für die Durchführung verwendet werden. Als Beispiele sollen das Einlegen von Sonderzügen, Unregelmäßigkeiten bei den Transporten und Witterungsauswirkungen angeführt werden. Der betriebene Planungsaufwand begründet sich in den komplexen Zusammenhängen zwischen den Betriebsprozessen und der größtenteils fehlenden EDV-Unterstützung, was eine Ermittlung der Ressourcendimensionierung bisher erschwert. Die Folge ist eine Diskrepanz zwischen der Datenqualität als Eingangsgröße für die Planung und der Präzision des Rangierarbeitsplans als Ausgangsgröße, was als Konsequenz eine Scheingenauigkeit der Planung und unter Umständen eine Über- oder Unterdimensionierung der Ressourcen mit sich bringt. Das zeigt, dass die Planung verkürzt werden muss und neue Hilfsmittel erforderlich sind. Motiviert durch diese Diskrepanz und den neuen Möglichkeiten, die die Methoden aus den Bereichen des Operations Research und des Maschinellen Lernens bieten, stellt diese Dissertation ein neues Planungsverfahren Parabola bereit. Parabola ermittelt mit geringerem Planungsaufwand und hoher Qualität relevante Kenngrößen für die Ressourcendimensionierung in Knoten des Schienengüterverkehrs. Dies beschleunigt den taktischen Planungsprozess, reduziert Scheingenauigkeiten bei der Ressourcendimensionierung vor der Betriebsdurchführung und orientiert sich daran, wann welche Entscheidungen zuverlässig und genau zu treffen sind. Folglich wird die Detailtiefe der Planung mit der Zuverlässigkeit der Daten in Einklang gebracht. Das in der Dissertation bereitgestellte Planungsverfahren Parabola analysiert eine ausreichend große Anzahl errechneter Rangierarbeitspläne und / oder historischer Betriebsdaten. Das dabei trainierte Regressionsmodell wird anschließend zur Bestimmung des Ressourcengerüsts genutzt. Die Kalibrierung der Regressionsmodelle erfordert hinreichend viele Rangierarbeitspläne. Für deren Erzeugung wird exemplarisch am Beispiel von Rangierbahnhöfen in dieser Dissertation ein ganzheitliches mathematisches lineares Programm entwickelt, das erstmalig sämtliche für die taktische Planung eines Rangierbahnhofs relevanten Entscheidungsprobleme vom Zugeingang bis zum Zugausgang abbildet. Dieses beinhaltet die Definition der Verknüpfung zwischen Eingangs- und Ausgangszügen, sogenannter Wagenübergänge, sowie die Terminierung sämtlicher Betriebsprozesse mit ihrer Zuweisung zu örtlichen Mitarbeitern, Betriebsmitteln und Infrastruktur. Die bestehenden mathematischen Modelle in der bisherigen Literatur beschränken sich lediglich auf Teile dieses Problems. Es folgt die systematische Erzeugung von Problemstellungen, sogenannten Instanzen, zur Generierung eines repräsentativen Testpools. Die Instanzen dieses NP-schweren Problems sind für generische, exakte Lösungsverfahren in akzeptabler Zeit nicht zuverlässig lösbar. Daher wird eine maßgeschneiderte Metaheuristik, konkret ein Verfahren der Klasse Adaptive Large Neighborhood Search (ALNS), entwickelt. Diese bewegt sich durch den Lösungsraum, indem schrittweise mittels mehrerer miteinander konkurrierender Subheuristiken eine vorher gefundene Lösung erst zerstört und anschließend wieder repariert wird. Durch unterschiedliche Charakteristika der Subheuristiken und einer statistischen Auswertung ihres jeweiligen Beitrags zum Lösungsfortschritt, gelingt es der ALNS, sich an das Stadium der Lösungssuche und an die jeweilige Problemstruktur anzupassen. Die in dieser Dissertation entwickelte ALNS erzeugt für realistische Instanzen eines Betriebstages Lösungen in hoher Qualität binnen weniger Minuten Rechenzeit. Basierend auf den erzeugten Rangierarbeitsplänen wurden für die Entwicklung des Planungsverfahrens insgesamt fünf Regressionstechniken getestet, die die Ausgangsgrößen der Pläne – Bedarf an Lokomotiven, Personal und Infrastruktur – prognostizieren. Die vielversprechendsten Ergebnisse werden durch die Methoden Tree Boosting sowie Random Forest erzielt, die in über 90 % der Fälle den Ressourcenbedarf für Personale und Lokomotiven exakt und für Infrastruktur mit einer Toleranz von einem Gleis je Gleisgruppe prognostizieren. Damit ist dieses Regressionsmodell nach ausreichender Kalibrierung entsprechend örtlicher Randbedingungen geeignet, komplexere Planungsverfahren zu ersetzen. Die Regressionsmodelle ermöglichen die Abstrahierung von Mengengerüsten und Leistungsverhalten von Knoten des Schienengüterverkehrs. Daher ist beispielsweise ein konkreter Fahrplan von und zu den Knoten nicht mehr notwendige Voraussetzung für die taktische Planung in Rangierbahnhöfen. Da das Regressionsverfahren aus vielen Rangierarbeitsplänen lernt, verringert sich die Abhängigkeit von einzelnen Instanzen. Durch die Kenntnis von vielen anderen Plänen können robustere Ressourcengerüste prognostiziert werden. Neben dem in dieser Dissertation ausgearbeiteten Anwendungsfall in der taktischen Planung in Knoten des Schienengüterverkehrs, eröffnet das vorgeschlagene neue Planungsverfahren Parabola eine Vielzahl an weiteren Einsatzfeldern. Die Interpretation des trainierten Regressionsmodells erlaubt das tiefgründige Verständnis des Verhaltens von Knoten des Schienengüterverkehrs. Dies ermöglicht ein besseres Verstehen der Engpässe in diesen Knoten sowie die Identifikation relevanter Treiber der Ressourcendimensionierung. Weiter können diese Modelle bei der Erstellung von netzweiten Leistungsanforderungen Berücksichtigung finden. Mit der in dieser Dissertation erfolgten Bereitstellung von Parabola wird durch Nutzung neuartiger Methoden aus dem Operations Research und Maschinellen Lernen das Instrumentarium der eisenbahnbetriebswissenschaftlichen Verfahren und Modelle sinnvoll erweitert. / This dissertation combines the knowledge of railway operations management with methods from operations research and machine learning. It focuses on rail freight facilities, especially their resource planning at a tactical level. The resource planning plays a crucial role for economical operations at high quality. The rail freight facilities represent neuralgic points in the transport chain of goods by rail. Their task is to carry out a multitude of different operational processes to ensure a defined output of trains, depending on a given input. Providing resource requirements appropriate to the amount of work has a significant impact on the quality of the processes in the facilities in particular and on the up- and downstream transport performance in general. The correct dimensioning of resource requirements, which include the necessary staff, locomotives, and infrastructure for an operating day, is characterized by a considerable manual effort and a large dependency on the data accuracy. Against this background and to overcome these drawbacks, this dissertation proposes a new method for resource requirements. The focus is on the large facilities of single wagonload traffic, the so-called classification yards, in which inbound trains are disassembled, railcars are classified according to their outbound direction, and new outbound trains are formed. Nowadays, shunting work plans are created several months to a few weeks before operations. These operating plans comprise a detailed work sequence plan, including process scheduling, and resource allocation. The operating plans form the basis for resource requirements. Due to the changing constraints prior to operations, e.g., the addition of special trains, and the stochastic nature of the operational processes, for instance caused by weather conditions, shunting work plans can only be used for execution to a limited extent. This effort is made for planning due to the complex interdependencies between the operational processes and the predominant lack of IT support, which makes it difficult to determine resource requirements. The result is a discrepancy between the accuracy of the data as an input variable and the precision of the shunting work plan as an output variable. This leads to an illusory precision of the planning and possibly to an oversizing or undersizing of the resources. Hence, planning must be shortened and new tools are required. Motivated by this discrepancy and the new possibilities offered by methods from the _elds of operations research and machine learning, this dissertation provides a new planning method Parabola. Parabola determines with less planning effort and at high quality relevant parameters for resource requirements in rail freight facilities. This accelerates the planning process, reduces illusory precision before operations are carried out and enables decision-making with sufficient reliability due to the data accuracy. Consequently, the level of detail of the planning is harmonized with the reliability of the data. The planning procedure Parabola involves the analysis of numerous calculated operating plans and / or historical operating data. This trains a regression model that can then be used to determine the resource requirements. The calibration of the regression models requires many operating plans. For their generation, an integrated mathematical linear program is developed in this dissertation using the example of classification yards; for the first time, one program covers all relevant decision problems of tactical planning in a classification yard, from the train arrival to the train departure. This includes the definition of the connection between inbound and outbound trains, so-called railcar interchanges, as well as the scheduling of all operational processes with their assignment to local staff, locomotives, and infrastructure. All existing mathematical models in the literature are limited to parts of the problem. Thereafter follows a systematic generation of a test pool of problems named instances. The instances of this NP-hard problem cannot be reliably solved within an acceptable time frame with general-purpose solvers. Therefore, a tailored metaheuristic, namely an adaptive large neighborhood search (ALNS), is developed. It moves through the solution space by first destroying and then repairing a solution stepwise. Several competing subheuristics are available for this purpose. The ALNS combines multiple subheuristics, which have different characteristics and contribute to the solution progress, as determined by statistical evaluation. Consequently, the ALNS successfully adapts to the progress of the solution and to the problem structure. The ALNS, which is developed in this dissertation, generates high-quality solutions for realistic instances of an operating day in a few minutes of computing time. Based on the generated operating plans, five regression methods predicting the output variables of the operating plans – demand for locomotives, staff, and infrastructure – are tested. The most promising results are achieved by the methods tree boosting and random forest, which predict the resource requirements in over 90% of the cases for staff and locomotives accurately and for infrastructure with a tolerance of one track per bowl. Thus, a regression model can replace the more complex planning procedures after sufficient calibration according to local restrictions. The regression models allow the abstraction of quantity structures and performance behavior. Hence, for example, a dedicated timetable is no longer a prerequisite for tactical planning in classification yards. Since regression methods learn from many operating plans, the dependence on individual instances is reduced. By knowing many other plans, the regression model can predict robust resource requirements. In addition to the use case in tactical planning in rail freight facilities, the proposed new planning method Parabola opens a multitude of further _elds of application. By interpreting the trained regression model, the behavior of rail freight facilities can be understood in depth. Under certain circumstances, this allows a better understanding of the bottlenecks in these facilities and the relevant drivers of resource dimensioning. Furthermore, these models have potential applications in the design of network-wide performance requirements. By providing Parabola in this dissertation, the toolbox of railroad management science procedures and models is sensibly extended by using novel methods from operations research and machine learning.

This dissertation integrates the strengths of individual emergent delivery technologies with package characteristics, and rural community needs to meet the demand for equitable, accessible, and inclusive rural delivery that is also cost-effective. To find ways to meet the package delivery service needs in rural areas and to fill research gaps in rural package delivery modeling, this study introduced a novel model known as the Parallel Scheduling Vehicle Routing Problem (PSVRP) in an endeavor to revolutionize package delivery by enhancing its efficiency, accessibility, and cost-effectiveness. The PSVRP represents a state-of-the-art approach to vehicle routing problems, incorporating a diversified fleet of innovative delivery modes. The multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems works in unison to minimize operational costs in various settings. A solution methodology that implemented the Adaptive Large Neighborhood Search (ALNS) algorithm was designed to solve the PSVRP in this research to produce optimal or near-optimal solutions. A variety of scenarios in a rural setting that include different quantities of customers to deliver to and different package weights are tested to evaluate if a multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems can provide cost-effective, low emissions, and efficient rural delivery services from local stores. Different fleet combinations are compared to demonstrate the best combined fleet for rural package delivery. It was found that implementation of electric vans, ADVs, drones, and truck-drone systems does decrease rural package delivery cost, but it does not yet decrease cost enough for the return on investment to be high enough for industry to implement the technology. Additionally, it was found that electric technologies do significantly decrease emissions of package delivery in rural areas. However, without a carbon tax or regulation mandating reduced carbon emissions, it is unlikely that the delivery industry will quickly embrace these new delivery modes. This dissertation not only advances academic understanding and practical applications in vehicle routing problems but also contributes to social equity by researching methods to improve delivery services in underserved rural communities. The PSVRP model could benefit transportation professionals considering technology-enabled rural delivery, developing rural delivery plans, looking for cost-effective rural delivery solutions, implementing a heterogeneous fleet to optimize rural delivery, or planning to reduce rural delivery emissions. It is anticipated that these innovations will spur further research and investment into rural delivery optimization, fostering a more inclusive and accessible package delivery service landscape. / Doctor of Philosophy / This dissertation integrates the strengths of individual emergent delivery technologies with package characteristics, and rural community needs to meet the demand for equitable, accessible, and inclusive rural delivery that is also cost-effective. To find ways to meet the package delivery service needs in rural areas and to fill research gaps in rural package delivery modeling, this study introduced a novel model known as the Parallel Scheduling Vehicle Routing Problem (PSVRP) in an endeavor to revolutionize package delivery by enhancing its efficiency, accessibility, and cost-effectiveness. The PSVRP represents a state-of-the-art approach to vehicle routing problems, incorporating a diversified fleet of innovative delivery modes. The multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems works in unison to minimize operational costs in various settings. A solution methodology that implemented the Adaptive Large Neighborhood Search (ALNS) algorithm was designed to solve the PSVRP in this research to produce optimal or near-optimal solutions. A variety of scenarios in a rural setting that include different quantities of customers to deliver to and different package weights are tested to evaluate if a multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems can provide cost-effective, low emissions, and efficient rural delivery services from local stores. Different fleet combinations are compared to demonstrate the best combined fleet for rural package delivery. It was found that implementation of electric vans, ADVs, drones, and truck-drone systems does decrease rural package delivery cost, but it does not yet decrease cost enough for the return on investment to be high enough for industry to implement the technology. Additionally, it was found that electric technologies do significantly decrease emissions of package delivery in rural areas. However, without a carbon tax or regulation mandating reduced carbon emissions, it is unlikely that the delivery industry will quickly embrace these new delivery modes. This dissertation not only advances academic understanding and practical applications in vehicle routing problems but also contributes to social equity by researching methods to improve delivery services in underserved rural communities. The PSVRP model could benefit transportation professionals considering technology-enabled rural delivery, developing rural delivery plans, looking for cost-effective rural delivery solutions, implementing a heterogeneous fleet to optimize rural delivery, or planning to reduce rural delivery emissions. It is anticipated that these innovations will spur further research and investment into rural delivery optimization, fostering a more inclusive and accessible package delivery service landscape.

De nombreux problèmes pratiques qui se posent dans dans le domaine de la logistique, peuvent être modélisés comme des problèmes de tournées de véhicules. De façon générale, cette famille de problèmes implique la conception de routes, débutant et se terminant à un dépôt, qui sont utilisées pour distribuer des biens à un nombre de clients géographiquement dispersé dans un contexte où les coûts associés aux routes sont minimisés. Selon le type de problème, un ou plusieurs dépôts peuvent-être présents. Les problèmes de tournées de véhicules sont parmi les problèmes combinatoires les plus difficiles à résoudre. Dans cette thèse, nous étudions un problème d’optimisation combinatoire, appartenant aux classes des problèmes de tournées de véhicules, qui est liée au contexte des réseaux de transport. Nous introduisons un nouveau problème qui est principalement inspiré des activités de collecte de lait des fermes de production, et de la redistribution du produit collecté aux usines de transformation, pour la province de Québec. Deux variantes de ce problème sont considérées. La première, vise la conception d’un plan tactique de routage pour le problème de la collecte-redistribution de lait sur un horizon donné, en supposant que le niveau de la production au cours de l’horizon est fixé. La deuxième variante, vise à fournir un plan plus précis en tenant compte de la variation potentielle de niveau de production pouvant survenir au cours de l’horizon considéré. Dans la première partie de cette thèse, nous décrivons un algorithme exact pour la première variante du problème qui se caractérise par la présence de fenêtres de temps, plusieurs dépôts, et une flotte hétérogène de véhicules, et dont l’objectif est de minimiser le coût de routage. À cette fin, le problème est modélisé comme un problème multi-attributs de tournées de véhicules. L’algorithme exact est basé sur la génération de colonnes impliquant un algorithme de plus court chemin élémentaire avec contraintes de ressources. Dans la deuxième partie, nous concevons un algorithme exact pour résoudre la deuxième variante du problème. À cette fin, le problème est modélisé comme un problème de tournées de véhicules multi-périodes prenant en compte explicitement les variations potentielles du niveau de production sur un horizon donné. De nouvelles stratégies sont proposées pour résoudre le problème de plus court chemin élémentaire avec contraintes de ressources, impliquant dans ce cas une structure particulière étant donné la caractéristique multi-périodes du problème général. Pour résoudre des instances de taille réaliste dans des temps de calcul raisonnables, une approche de résolution de nature heuristique est requise. La troisième partie propose un algorithme de recherche adaptative à grands voisinages où de nombreuses nouvelles stratégies d’exploration et d’exploitation sont proposées pour améliorer la performances de l’algorithme proposé en termes de la qualité de la solution obtenue et du temps de calcul nécessaire. / Many practical problems arising in real-world applications in the field of logistics can be modeled as vehicle routing problems (VRP). In broad terms, VRPs deal with designing optimal routes for delivering goods or services to a number of geographically scattered customers in a context in which, routing costs are minimized. Depending on the type of problem, one or several depots may be present. Routing problems are among the most difficult combinatorial optimization problems. In this dissertation we study a special combinatorial optimization problem, belonging to the class of the vehicle routing problem that is strongly linked to the context of the transportation networks. We introduce a new problem setting, which is mainly inspired by the activities of collecting milk from production farms and distributing the collected product to processing plants in Quebec. Two different variants of this problem setting are considered. The first variant seeks a tactical routing plan for the milk collection-distribution problem over a given planning horizon assuming that the production level over the considered horizon is fixed. The second variant aims to provide a more accurate plan by taking into account potential variations in terms of production level, which may occur during the course of a horizon. This thesis is cast into three main parts, as follows: In the first part, we describe an exact algorithm for the first variant of the problem, which is characterized by the presence of time windows, multiple depots, and a heterogeneous fleet of vehicles, where the objective is to minimize the routing cost. To this end, the problem is modeled as a multi-attribute vehicle routing problem. The exact algorithm proposed is based on the column generation approach, coupled with an elementary shortest path algorithm with resource constraints. In the second part, we design an exact framework to address the second variant of the problem. To this end, the problem is modeled as a multi-period vehicle routing problem, which explicitly takes into account potential production level variations over a horizon. New strategies are proposed to tackle the particular structure of the multi-period elementary shortest path algorithm with resource constraints. To solve realistic instances of the second variant of the problem in reasonable computation times, a heuristic approach is required. In the third part of this thesis, we propose an adaptive large neighborhood search, where various new exploration and exploitation strategies are proposed to improve the performance of the algorithm in terms of solution quality and computational efficiency.

Cette thèse porte sur les problèmes de tournées de véhicules avec fenêtres de temps où un gain est associé à chaque client et où l'objectif est de maximiser la somme des gains recueillis moins les coûts de transport. De plus, un même véhicule peut effectuer plusieurs tournées durant l'horizon de planification. Ce problème a été relativement peu étudié en dépit de son importance en pratique. Par exemple, dans le domaine de la livraison de denrées périssables, plusieurs tournées de courte durée doivent être combinées afin de former des journées complètes de travail. Nous croyons que ce type de problème aura une importance de plus en plus grande dans le futur avec l'avènement du commerce électronique, comme les épiceries électroniques, où les clients peuvent commander des produits par internet pour la livraison à domicile. Dans le premier chapitre de cette thèse, nous présentons d'abord une revue de la littérature consacrée aux problèmes de tournées de véhicules avec gains ainsi qu'aux problèmes permettant une réutilisation des véhicules. Nous présentons les méthodologies générales adoptées pour les résoudre, soit les méthodes exactes, les méthodes heuristiques et les méta-heuristiques. Nous discutons enfin des problèmes de tournées dynamiques où certaines données sur le problème ne sont pas connues à l'avance. Dans le second chapitre, nous décrivons un algorithme exact pour résoudre un problème de tournées avec fenêtres de temps et réutilisation de véhicules où l'objectif premier est de maximiser le nombre de clients desservis. Pour ce faire, le problème est modélisé comme un problème de tournées avec gains. L'algorithme exact est basé sur une méthode de génération de colonnes couplée avec un algorithme de plus court chemin élémentaire avec contraintes de ressources. Pour résoudre des instances de taille réaliste dans des temps de calcul raisonnables, une approche de résolution de nature heuristique est requise. Le troisième chapitre propose donc une méthode de recherche adaptative à grand voisinage qui exploite les différents niveaux hiérarchiques du problème (soit les journées complètes de travail des véhicules, les routes qui composent ces journées et les clients qui composent les routes). Dans le quatrième chapitre, qui traite du cas dynamique, une stratégie d'acceptation et de refus des nouvelles requêtes de service est proposée, basée sur une anticipation des requêtes à venir. L'approche repose sur la génération de scénarios pour différentes réalisations possibles des requêtes futures. Le coût d'opportunité de servir une nouvelle requête est basé sur une évaluation des scénarios avec et sans cette nouvelle requête. Enfin, le dernier chapitre résume les contributions de cette thèse et propose quelques avenues de recherche future. / This thesis studies vehicle routing problems with time windows, where a gain is associated with each customer and where the objective is to maximize the total gain collected minus the routing costs. Furthermore. the same vehicle might be assigned to different routes during the planning horizon. This problem has received little attention in the literature in spite of its importance in practice. For example, in the home delivery of perishable goods (like food), routes of short duration must be combined to form complete workdays. We believe that this type of problem will become increasingly important in the future with the advent of electronic services, like e-groceries, where customers can order goods through the Internet and get these goods delivered at home. In the first chapter of this thesis, we present a review of vehicle routing problems with gains, as well as vehicle routing problems with multiple use of vehicles. We discuss the general classes of problem-solving approaches for these problems, namely, exact methods, heuristics and metaheuristics. We also introduce dynamic vehicle routing problems, where new information is revealed as the routes are executed. In the second chapter, we describe an exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles, where the first objective is to maximize the number of served customers. To this end, the problem is modeled as a vehicle routing problem with gains. The exact algorithm is based on column generation, coupled with an elementary shortest path algorithm with resource constraints. To solve realistic instances in reasonable computation times, a heuristic approach is required. The third chapter proposes an adaptative large neighborhood search where the various hierarchical levels of the problem are exploited (i.e., complete vehicle workdays, routes within workdays and customers within routes). The fourth chapter deals with the dynamic case. In this chapter, a strategy for accepting or rejecting new customer requests is proposed. This strategy is based on the generation of multiple scenarios for different realizations of the requests in the future. An opportunity cost for serving a new request is then computed, based on an evaluation of the scenarios with and without the new request. Finally, the last chapter summarizes the contributions of this thesis and proposes future research avenues.

De nombreux problèmes pratiques qui se posent dans dans le domaine de la logistique, peuvent être modélisés comme des problèmes de tournées de véhicules. De façon générale, cette famille de problèmes implique la conception de routes, débutant et se terminant à un dépôt, qui sont utilisées pour distribuer des biens à un nombre de clients géographiquement dispersé dans un contexte où les coûts associés aux routes sont minimisés. Selon le type de problème, un ou plusieurs dépôts peuvent-être présents. Les problèmes de tournées de véhicules sont parmi les problèmes combinatoires les plus difficiles à résoudre. Dans cette thèse, nous étudions un problème d’optimisation combinatoire, appartenant aux classes des problèmes de tournées de véhicules, qui est liée au contexte des réseaux de transport. Nous introduisons un nouveau problème qui est principalement inspiré des activités de collecte de lait des fermes de production, et de la redistribution du produit collecté aux usines de transformation, pour la province de Québec. Deux variantes de ce problème sont considérées. La première, vise la conception d’un plan tactique de routage pour le problème de la collecte-redistribution de lait sur un horizon donné, en supposant que le niveau de la production au cours de l’horizon est fixé. La deuxième variante, vise à fournir un plan plus précis en tenant compte de la variation potentielle de niveau de production pouvant survenir au cours de l’horizon considéré. Dans la première partie de cette thèse, nous décrivons un algorithme exact pour la première variante du problème qui se caractérise par la présence de fenêtres de temps, plusieurs dépôts, et une flotte hétérogène de véhicules, et dont l’objectif est de minimiser le coût de routage. À cette fin, le problème est modélisé comme un problème multi-attributs de tournées de véhicules. L’algorithme exact est basé sur la génération de colonnes impliquant un algorithme de plus court chemin élémentaire avec contraintes de ressources. Dans la deuxième partie, nous concevons un algorithme exact pour résoudre la deuxième variante du problème. À cette fin, le problème est modélisé comme un problème de tournées de véhicules multi-périodes prenant en compte explicitement les variations potentielles du niveau de production sur un horizon donné. De nouvelles stratégies sont proposées pour résoudre le problème de plus court chemin élémentaire avec contraintes de ressources, impliquant dans ce cas une structure particulière étant donné la caractéristique multi-périodes du problème général. Pour résoudre des instances de taille réaliste dans des temps de calcul raisonnables, une approche de résolution de nature heuristique est requise. La troisième partie propose un algorithme de recherche adaptative à grands voisinages où de nombreuses nouvelles stratégies d’exploration et d’exploitation sont proposées pour améliorer la performances de l’algorithme proposé en termes de la qualité de la solution obtenue et du temps de calcul nécessaire. / Many practical problems arising in real-world applications in the field of logistics can be modeled as vehicle routing problems (VRP). In broad terms, VRPs deal with designing optimal routes for delivering goods or services to a number of geographically scattered customers in a context in which, routing costs are minimized. Depending on the type of problem, one or several depots may be present. Routing problems are among the most difficult combinatorial optimization problems. In this dissertation we study a special combinatorial optimization problem, belonging to the class of the vehicle routing problem that is strongly linked to the context of the transportation networks. We introduce a new problem setting, which is mainly inspired by the activities of collecting milk from production farms and distributing the collected product to processing plants in Quebec. Two different variants of this problem setting are considered. The first variant seeks a tactical routing plan for the milk collection-distribution problem over a given planning horizon assuming that the production level over the considered horizon is fixed. The second variant aims to provide a more accurate plan by taking into account potential variations in terms of production level, which may occur during the course of a horizon. This thesis is cast into three main parts, as follows: In the first part, we describe an exact algorithm for the first variant of the problem, which is characterized by the presence of time windows, multiple depots, and a heterogeneous fleet of vehicles, where the objective is to minimize the routing cost. To this end, the problem is modeled as a multi-attribute vehicle routing problem. The exact algorithm proposed is based on the column generation approach, coupled with an elementary shortest path algorithm with resource constraints. In the second part, we design an exact framework to address the second variant of the problem. To this end, the problem is modeled as a multi-period vehicle routing problem, which explicitly takes into account potential production level variations over a horizon. New strategies are proposed to tackle the particular structure of the multi-period elementary shortest path algorithm with resource constraints. To solve realistic instances of the second variant of the problem in reasonable computation times, a heuristic approach is required. In the third part of this thesis, we propose an adaptive large neighborhood search, where various new exploration and exploitation strategies are proposed to improve the performance of the algorithm in terms of solution quality and computational efficiency.

Cette thèse porte sur les problèmes de tournées de véhicules avec fenêtres de temps où un gain est associé à chaque client et où l'objectif est de maximiser la somme des gains recueillis moins les coûts de transport. De plus, un même véhicule peut effectuer plusieurs tournées durant l'horizon de planification. Ce problème a été relativement peu étudié en dépit de son importance en pratique. Par exemple, dans le domaine de la livraison de denrées périssables, plusieurs tournées de courte durée doivent être combinées afin de former des journées complètes de travail. Nous croyons que ce type de problème aura une importance de plus en plus grande dans le futur avec l'avènement du commerce électronique, comme les épiceries électroniques, où les clients peuvent commander des produits par internet pour la livraison à domicile. Dans le premier chapitre de cette thèse, nous présentons d'abord une revue de la littérature consacrée aux problèmes de tournées de véhicules avec gains ainsi qu'aux problèmes permettant une réutilisation des véhicules. Nous présentons les méthodologies générales adoptées pour les résoudre, soit les méthodes exactes, les méthodes heuristiques et les méta-heuristiques. Nous discutons enfin des problèmes de tournées dynamiques où certaines données sur le problème ne sont pas connues à l'avance. Dans le second chapitre, nous décrivons un algorithme exact pour résoudre un problème de tournées avec fenêtres de temps et réutilisation de véhicules où l'objectif premier est de maximiser le nombre de clients desservis. Pour ce faire, le problème est modélisé comme un problème de tournées avec gains. L'algorithme exact est basé sur une méthode de génération de colonnes couplée avec un algorithme de plus court chemin élémentaire avec contraintes de ressources. Pour résoudre des instances de taille réaliste dans des temps de calcul raisonnables, une approche de résolution de nature heuristique est requise. Le troisième chapitre propose donc une méthode de recherche adaptative à grand voisinage qui exploite les différents niveaux hiérarchiques du problème (soit les journées complètes de travail des véhicules, les routes qui composent ces journées et les clients qui composent les routes). Dans le quatrième chapitre, qui traite du cas dynamique, une stratégie d'acceptation et de refus des nouvelles requêtes de service est proposée, basée sur une anticipation des requêtes à venir. L'approche repose sur la génération de scénarios pour différentes réalisations possibles des requêtes futures. Le coût d'opportunité de servir une nouvelle requête est basé sur une évaluation des scénarios avec et sans cette nouvelle requête. Enfin, le dernier chapitre résume les contributions de cette thèse et propose quelques avenues de recherche future. / This thesis studies vehicle routing problems with time windows, where a gain is associated with each customer and where the objective is to maximize the total gain collected minus the routing costs. Furthermore. the same vehicle might be assigned to different routes during the planning horizon. This problem has received little attention in the literature in spite of its importance in practice. For example, in the home delivery of perishable goods (like food), routes of short duration must be combined to form complete workdays. We believe that this type of problem will become increasingly important in the future with the advent of electronic services, like e-groceries, where customers can order goods through the Internet and get these goods delivered at home. In the first chapter of this thesis, we present a review of vehicle routing problems with gains, as well as vehicle routing problems with multiple use of vehicles. We discuss the general classes of problem-solving approaches for these problems, namely, exact methods, heuristics and metaheuristics. We also introduce dynamic vehicle routing problems, where new information is revealed as the routes are executed. In the second chapter, we describe an exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles, where the first objective is to maximize the number of served customers. To this end, the problem is modeled as a vehicle routing problem with gains. The exact algorithm is based on column generation, coupled with an elementary shortest path algorithm with resource constraints. To solve realistic instances in reasonable computation times, a heuristic approach is required. The third chapter proposes an adaptative large neighborhood search where the various hierarchical levels of the problem are exploited (i.e., complete vehicle workdays, routes within workdays and customers within routes). The fourth chapter deals with the dynamic case. In this chapter, a strategy for accepting or rejecting new customer requests is proposed. This strategy is based on the generation of multiple scenarios for different realizations of the requests in the future. An opportunity cost for serving a new request is then computed, based on an evaluation of the scenarios with and without the new request. Finally, the last chapter summarizes the contributions of this thesis and proposes future research avenues.