This dissertation introduces and analyzes the Bus Evacuation Problem (BEP), a unique Vehicle Routing Problem motivated both by its humanitarian significance and by the routing and scheduling challenges of planning transit-based, regional evacuations. First, a variant where evacuees arrive at constant, location-specific rates is introduced. In this problem, a fleet of capacitated buses must transport all evacuees to a depot/shelter such that the last scheduled pick-up and the end of the evacuee arrival process occurs at a location-specific time. The problem seeks to minimize their accumulated waiting time, restricts the number of pick-ups on each location, and exploits efficiencies from service choice and from allowing buses to unload evacuees at the depot multiple times. It is shown that, depending on the problem instance, increasing the maximum number of pick-ups allowed may reduce both the fleet size requirement and the evacuee waiting time, and that, past a certain threshold, there exist a range of values that guarantees an efficient usage of the available fleet and equitable reductions in waiting time across pick-up locations. Second, an extension of the Ritter (1967) Relaxation Algorithm, which explores the inherent structure of problems with complicating variables and constraints, such as the aforementioned BEP variant, is presented. The modified algorithm allows problems with linear, integer, or mixed-integer subproblems and with linear or quadratic objective functions to be solved to optimality. Empirical studies demonstrate the algorithm viability to solve large optimization problems. Finally, a two-stage stochastic formulation for the BEP is presented. Such variant assumes that all evacuees are at the pick-up locations at the onset of the evacuation, that the set of possible demands is provided, and, more importantly, that the actual demands become known once buses visit the pick-up locations for the first time. The effect of exploratory visits (sampling) and symmetry is explored, and the resulting insights used to develop an improved formulation for the problem. An iterative (dynamic) solution algorithm is proposed. / Ph. D.

Since Hurricane Katrina, extensive studies have been conducted aiming to optimize the transit vehicle routing in the event of an emergency evacuation. However, the vast majority of the studies focus on solving the deterministic vehicle routing problem that all the evacuation data are known in advance. These studies are generally not practical in dealing with real-world problems which involve considerable uncertainty in the evacuation data set. In this dissertation, a SmartEvac system is developed for dynamic vehicle routing optimization in emergency evacuation. The SmartEvac system is capable of processing dynamic evacuation data in real time, such as random pickup requests, travel time change, network interruptions. The objective is to minimize the total travel time for all transit vehicles. A column generation based online optimization model is integrated into the SmartEvac system. The optimization model is based on the following structure: a master problem model and a sub-problem model. The master problem model is used for routes selection from a restricted routes set while the sub-problem model is developed to progressively add new routes into the restricted routes set. The sub-problem is formulated as a shortest path problem with capacity constraint and is solved using a cycle elimination algorithm. When the evacuation data are updated, the SmartEvac system will reformulate the optimization model and generate a new routes set based on the existing routes set. The computational results on benchmark problems are compared to other studies in the literature. The SmartEvac system outperforms other approaches on most of the benchmark problems in terms of computation time and solution quality. CORSIM simulation is used as a test bed for the SmartEvac system. CORSIM Run-Time-Extension is developed for communications between the simulation and the SmartEvac system. A case study of the Hurricane Gustav emergency evacuation is conducted. Different scenarios corresponding to different situations that presented in the Hurricane Gustav emergency evacuation are proposed to evaluate the performance of the SmartEvac system in response to real-time data. The average processing time is 28.9 seconds and the maximum processing time is 171 seconds, which demonstrate the SmartEvac system’s capability of real-time vehicle routing optimization.

Unternehmen der Transportbranche müssen gerade im operativen Tagesgeschäft bei der Tourenplanung und Transportdisposition Planungsprobleme lösen, die ein hohes Maß an Dynamik aufweisen. Speziell die Inputfaktoren der Tourenplanung sind größtenteils dynamisch und stochastisch. Aus Sicht des Autors kann die Qualität von Tourplanungsergebnissen durch die zeitnahe Berücksichtigung unvorhergesehener Ereignisse nachhaltig verbessert werden. Jedoch findet diese zunehmend erfolgskritische Funktionalität in der Literatur bisher nur unzureichend Beachtung, obwohl das Tourenplanungsproblem (Vehicle Routing Problem (VRP)) eines der wichtigsten und am meisten erforschten kombinatorischen Optimierungsprobleme ist. Verfahren für kapazitierte dynamische Tourenplanungsproblemstellungen sind in der Literatur kaum zu finden. Speziell im Bereich der Algorithmen, die eine große Lösungsgeschwindigkeit, eine leichte Verständlichkeit, eine aus praktischer Sicht akzeptable Lösungsgüte aufweisen und die Möglichkeit besitzen, die aktuellen Routenpläne der Fahrzeuge ausgehend von der momentanen geographischen Position real-time zu verändern, besteht Forschungsbedarf. Die Arbeit geht daher der Forschungsfrage nach, wie ein Verfahren für die dynamische Tourenplanung zu konstruieren ist, welches das kapazitierte dynamische Tourenplanungsproblem mit der Möglichkeit der Änderung des aktuellen Fahrzeugzuges unter Einhaltung sehr kurzer Rechenzeiten bei größtmöglicher Verständlichkeit löst. Durch die genannten Kriterien wird im Rahmen der Arbeit der Schwerpunkt auf die Modifikation von klassischen heuristischen Verfahren für die Lösung von dynamischen Tourenplanungsproblemen gelegt. Die Arbeit befasst sich sowohl mit dem Gesamtkonzept zur Disposition dynamischer Kunden als auch mit konkreten Modellen und Verfahren zur Lösung von Subproblemen innerhalb des Gesamtkonzeptes. Ferner erfolgt die Präsentation von umfangreichen Simulationsergebnissen, die auf der durchgeführten softwaretechnischen Implementierung der entwickelten Verfahren basieren. Die gute Anwendbarkeit der neuen Verfahren in der Praxis wird gezeigt. Zwecks der möglichst ganzheitlichen Betrachtung des Themengebietes erfolgt in der Arbeit zum einen sowohl die Erörterung von quantitativen als auch von qualitativen Aspekten der dynamischen Tourenplanung und zum anderen die Analyse von Schnittstellen zwischen der dynamischen Tourenplanung und eng damit verbundenen Bereichen wie Flottenmanagement oder Auftragseingang bzw. -disposition. Hierzu werden die Informationsflüsse zwischen den beteiligten Elementen im Rahmen des dynamischen Dispositionsprozesses aufgezeigt, telematische Komponenten zur Unterstützung des Informationsmanagements und der Informationsübertragung vorgestellt sowie die benötigten Inputdaten erläutert. Den Schwerpunkt der Arbeit stellt jedoch die Entwicklung von neuen quantitativen Methoden zur dynamischen Tourenplanung dar.

A Roteirização de Veículos desempenha papel fundamental nos processos modernos de distribuição de produtos e realização de serviços. A atual disseminação de recursos de tecnologia de informação e comunicação, de forma confiável e economicamente acessível, permite trabalhar com informações em tempo real e melhoram os padrões de nível de serviço associados. O presente trabalho apresenta uma solução para roteirização de veículos cujas equipes de bordo realizam serviços que justificam seu deslocamento, uma vez que as demandas estão geograficamente dispersas. Tais demandas são, em parte, conhecidas antes do despacho (permitem programação antecipada) dos veículos e suas equipes; outra parte surge durante a jornada de trabalho. Como exemplos podem-se citar os casos de serviços de montagem e manutenção de instalações, equipamentos, engenharia e inspeção de tráfego, policiamento etc. Trata-se da aplicação da roteirização parcialmente dinâmica, conforme Larsen (2000), cujas bases foram definidas por Psaraftis (1988,1995), Bertsimas et al (1993) no problema DTRP (Dynamic Travelling Repairman Problem). A função objetivo apresenta uma combinação de minimização dos custos de deslocamento, para os pedidos de serviços conhecidos antes da saída dos veículos e de minimização do tempo de resposta (chegada no local do cliente ou da ocorrência) para os casos de pedidos imediatos ou emergenciais. A solução do problema envolve um modelo computacional de testes e avaliação, heurística de Clarke e Wright (1964) para formação das rotas estáticas, no Método Húngaro (Kuhn, 1955) para designar o veículo que resulta no menor tempo de resposta no atendimento a um pedido emergencial e a heurística de Clarke e Wright modificada na otimização do restante dos pedidos quando o veículo voltar a sua rota original. O modelo computacional foi testado em uma empresa de manutenção de elevadores na cidade de São Paulo, Brasil, onde demonstrou resultados comparativamente melhores em relação ao sistema de roteirização utilizado atualmente pela empresa. / The Vehicle Routing Problem plays a critical role on modern processes related to physical distribution of goods and services. The present expansion of information and communication technology in a reliable, economic and accessible way allows real time information and requires the utilization of appropriate tools for real time decisions resulting in significant improvements in quality and service level related to dynamic vehicle routing. A dynamic routing problem is presented, in which vehicles serve geographic dispersed service demands that justify their movement in a fixed area. Such service demands are partially known before vehicles dispatching (allowing prior programming) whilst others are known during the work journey. As examples, one can mention cases concerning installation and maintenance of utilities, equipment, engineering and surveillance services that refer to applications of Partially Dynamic Routing according to Larsen (2000), the groundings of which were defined by Psaraftis (1988,1995), Bertsimas et al (1993) in the Dynamic Travelling Repairman Problem (DTRP). The objective function is a combination of the minimization of movement costs to serve the prior demands and the minimization of time to reach (time to response) Dynamic-or-emergency-demand sites. The proposed solution involves a computational model for testing and evaluating a set of heuristics and methods comprising the Clarke and Wright (1964) Heuristic to compose the static routes, the Hungarian Method (Kuhn, 1955) to assign vehicles to the dynamic demands that produces the lowest response time and, finally, a Clarke and Wright Modified Heuristic used to optimize the remainder of the route when each diverted vehicle returns to its static route. The Computational Model was applied to a lift maintenance company located in the city of São Paulo (Brazil) demonstrating better results as compared to the present routing system.

A Roteirização de Veículos desempenha papel fundamental nos processos modernos de distribuição de produtos e realização de serviços. A atual disseminação de recursos de tecnologia de informação e comunicação, de forma confiável e economicamente acessível, permite trabalhar com informações em tempo real e melhoram os padrões de nível de serviço associados. O presente trabalho apresenta uma solução para roteirização de veículos cujas equipes de bordo realizam serviços que justificam seu deslocamento, uma vez que as demandas estão geograficamente dispersas. Tais demandas são, em parte, conhecidas antes do despacho (permitem programação antecipada) dos veículos e suas equipes; outra parte surge durante a jornada de trabalho. Como exemplos podem-se citar os casos de serviços de montagem e manutenção de instalações, equipamentos, engenharia e inspeção de tráfego, policiamento etc. Trata-se da aplicação da roteirização parcialmente dinâmica, conforme Larsen (2000), cujas bases foram definidas por Psaraftis (1988,1995), Bertsimas et al (1993) no problema DTRP (Dynamic Travelling Repairman Problem). A função objetivo apresenta uma combinação de minimização dos custos de deslocamento, para os pedidos de serviços conhecidos antes da saída dos veículos e de minimização do tempo de resposta (chegada no local do cliente ou da ocorrência) para os casos de pedidos imediatos ou emergenciais. A solução do problema envolve um modelo computacional de testes e avaliação, heurística de Clarke e Wright (1964) para formação das rotas estáticas, no Método Húngaro (Kuhn, 1955) para designar o veículo que resulta no menor tempo de resposta no atendimento a um pedido emergencial e a heurística de Clarke e Wright modificada na otimização do restante dos pedidos quando o veículo voltar a sua rota original. O modelo computacional foi testado em uma empresa de manutenção de elevadores na cidade de São Paulo, Brasil, onde demonstrou resultados comparativamente melhores em relação ao sistema de roteirização utilizado atualmente pela empresa. / The Vehicle Routing Problem plays a critical role on modern processes related to physical distribution of goods and services. The present expansion of information and communication technology in a reliable, economic and accessible way allows real time information and requires the utilization of appropriate tools for real time decisions resulting in significant improvements in quality and service level related to dynamic vehicle routing. A dynamic routing problem is presented, in which vehicles serve geographic dispersed service demands that justify their movement in a fixed area. Such service demands are partially known before vehicles dispatching (allowing prior programming) whilst others are known during the work journey. As examples, one can mention cases concerning installation and maintenance of utilities, equipment, engineering and surveillance services that refer to applications of Partially Dynamic Routing according to Larsen (2000), the groundings of which were defined by Psaraftis (1988,1995), Bertsimas et al (1993) in the Dynamic Travelling Repairman Problem (DTRP). The objective function is a combination of the minimization of movement costs to serve the prior demands and the minimization of time to reach (time to response) Dynamic-or-emergency-demand sites. The proposed solution involves a computational model for testing and evaluating a set of heuristics and methods comprising the Clarke and Wright (1964) Heuristic to compose the static routes, the Hungarian Method (Kuhn, 1955) to assign vehicles to the dynamic demands that produces the lowest response time and, finally, a Clarke and Wright Modified Heuristic used to optimize the remainder of the route when each diverted vehicle returns to its static route. The Computational Model was applied to a lift maintenance company located in the city of São Paulo (Brazil) demonstrating better results as compared to the present routing system.

Unternehmen der Transportbranche müssen gerade im operativen Tagesgeschäft bei der Tourenplanung und Transportdisposition Planungsprobleme lösen, die ein hohes Maß an Dynamik aufweisen. Speziell die Inputfaktoren der Tourenplanung sind größtenteils dynamisch und stochastisch. Aus Sicht des Autors kann die Qualität von Tourplanungsergebnissen durch die zeitnahe Berücksichtigung unvorhergesehener Ereignisse nachhaltig verbessert werden. Jedoch findet diese zunehmend erfolgskritische Funktionalität in der Literatur bisher nur unzureichend Beachtung, obwohl das Tourenplanungsproblem (Vehicle Routing Problem (VRP)) eines der wichtigsten und am meisten erforschten kombinatorischen Optimierungsprobleme ist. Verfahren für kapazitierte dynamische Tourenplanungsproblemstellungen sind in der Literatur kaum zu finden. Speziell im Bereich der Algorithmen, die eine große Lösungsgeschwindigkeit, eine leichte Verständlichkeit, eine aus praktischer Sicht akzeptable Lösungsgüte aufweisen und die Möglichkeit besitzen, die aktuellen Routenpläne der Fahrzeuge ausgehend von der momentanen geographischen Position real-time zu verändern, besteht Forschungsbedarf. Die Arbeit geht daher der Forschungsfrage nach, wie ein Verfahren für die dynamische Tourenplanung zu konstruieren ist, welches das kapazitierte dynamische Tourenplanungsproblem mit der Möglichkeit der Änderung des aktuellen Fahrzeugzuges unter Einhaltung sehr kurzer Rechenzeiten bei größtmöglicher Verständlichkeit löst. Durch die genannten Kriterien wird im Rahmen der Arbeit der Schwerpunkt auf die Modifikation von klassischen heuristischen Verfahren für die Lösung von dynamischen Tourenplanungsproblemen gelegt. Die Arbeit befasst sich sowohl mit dem Gesamtkonzept zur Disposition dynamischer Kunden als auch mit konkreten Modellen und Verfahren zur Lösung von Subproblemen innerhalb des Gesamtkonzeptes. Ferner erfolgt die Präsentation von umfangreichen Simulationsergebnissen, die auf der durchgeführten softwaretechnischen Implementierung der entwickelten Verfahren basieren. Die gute Anwendbarkeit der neuen Verfahren in der Praxis wird gezeigt. Zwecks der möglichst ganzheitlichen Betrachtung des Themengebietes erfolgt in der Arbeit zum einen sowohl die Erörterung von quantitativen als auch von qualitativen Aspekten der dynamischen Tourenplanung und zum anderen die Analyse von Schnittstellen zwischen der dynamischen Tourenplanung und eng damit verbundenen Bereichen wie Flottenmanagement oder Auftragseingang bzw. -disposition. Hierzu werden die Informationsflüsse zwischen den beteiligten Elementen im Rahmen des dynamischen Dispositionsprozesses aufgezeigt, telematische Komponenten zur Unterstützung des Informationsmanagements und der Informationsübertragung vorgestellt sowie die benötigten Inputdaten erläutert. Den Schwerpunkt der Arbeit stellt jedoch die Entwicklung von neuen quantitativen Methoden zur dynamischen Tourenplanung dar.