Road freight transportation is subject to several legal requirements having direct impact on the practical applicability of routes and schedules. The vast majority of vehicle routing literature, so far, has largely focused on physical constraints such as capacity limits, or customer requirements such as time windows for pickups and deliveries. This paper studies legal requirements for long-distance haulage in the European Union, identifies some major gaps in the current state-of-the-art in vehicle routing, presents approaches for overcoming this gap, and analyzes the impact of the legal requirements studied.

Transportations are paramount for a well-functioning society and necessary to secure essential products and maintain our standard of living. These are operations and activities related to distributing packages and goods and providing services by society such as waste collection, postal services, health care, or emergency response. Transports thus affect all of us – companies, the public sector, and individuals – in our daily lives. However, the transportation sector is also one of the most polluting sectors, and in recent years the number distributed goods has increased significantly. Based on the importance of transportation and logistics services and their impact on the economy, environment, and people’s lives, it is in everyone’s interest that these transports are as efficient as possible. The supply chain's underlying infrastructure affects the efficiency and design of distribution routes. As such infrastructure is associated with substantial capital investments, it is crucial to consider the network design and the location of various facilities. The supply chain network design in this context encompasses two major problem classes; the vehicle routing problem aiming to find optimal routes to serve a set of customers by a fleet of vehicles from a central facility, and the facility location problem aiming to find the optimal location for various facilities. Moreover, these problems are interconnected as the facilities affect the demand fulfillment and the vehicle routing. Such problems are often solved with optimization techniques within the field of Operations Research, which is concerned with the mathematical modeling and algorithmic solution of decision-making problems. The objective of this thesis is to contribute to the advancement of the field of supply chain network design, by;       I.  exploring and identifying opportunities and needs for modeling and solution approaches that cope with the increased complexity of real-life industrial applications in facility location and distribution systems of goods, and     II.  to propose new modeling approaches and solution methods that cope with such opportunities and needs.  The thesis is based on three appended papers. Paper A presents a network design modeling approach for a reverse supply chain of a newly introduced product with difficulties in demand estimations. Paper B is a literature review covering multi-objective location-routing problems; these are strategic models aiming to determine the location of facilities considering aspects of tour planning and multi-stop routes. An annotated review is presented based on the application area of the various models, and an analysis of objectives and solution approaches used. Paper C introduces the Hierarchical Multi-Switch Multi-Echelon VRP, which is a new variant of the vehicle routing problem based on a real-life operational problem originating from the policies of a Nordic distribution company. A mixed-integer formulation of the problem is proposed, and its relations to other previously stated VRP variants are analyzed and discussed.

The e-commerce market has developed massively since the 1990ies. In addition to a general change of shopping behavior, the COVID-pandemic increased the importance of online shopping. This study is about the transportation of e-commerce parcels on the last mile in urban areas, so called last-mile delivery. Special focus is put on innovative last mile solutions that reduce the externalities related to last-mile delivery. There are several factors that complicate the delivery on the last mile such as congestion, driving restrictions and meeting time-windows for customers. This study investigates to what extent the route planning for a fleet of vehicles can account for these various requirements and restrictions. The route planning was conducted in the GIS software ArcGIS Pro using the vehicle routing problem. The routes could be successfully planned and consider most of the relevant factors for last-mile delivery operations. The results indicate that traffic and congestion in cities can be accounted for which results in an average driving speed of 20km/h. The planned routes also indicate that not even 20% of the vehicle’s cargo capacity was used and that 60-65% of the total time is spent driving between orders. The study and its results are relevant to businesses and researchers in the field of last-mile delivery as the analysis of a real-world scenario highlights the possibilities and limitations of route planning on the last mile.

Adoption of electric vehicles has been restrained by the availability of charging stations and consumer fear of being stranded with a depleted battery, far from the nearest charger. In many areas of the world, charging stations are now widely available and the transition from vehicles with internal combustion engines is accelerating, though still in a fairly early stage. For electric vehicle drivers in those areas, anxiety that they will not be able to find a charger (“range anxiety”) is subsiding. However, differences in charging speed and pricing between stations and different outlets at the same station can be large. Total trip duration can vary significantly based on the charging outlet selected. Prior research has developed methods for helping all drivers find the fastest route and for electric vehicle drivers to ensure that they are able to complete their trip. Additional research has explored other complexities of route selection for electric vehicles such as how to select optimal stations for charging based on the total trip duration, including driving and charging time. Pricing for recharging electric vehicles at public chargers is more complex and diverse than for gas filling stations due to the differences in charging rates and the relatively low competition. This research investigates those differences. Using design science research methodology, a method is presented for determining which charging stops result in the lowest possible charging cost for a given route. The method is demonstrated through experiment with random routes within Sweden. The experimental results show that the average cost savings as compared to the duration-optimal route is 15% and 139 SEK per additional hour of trip time. One possible direction for future work is to improve the performance of the algorithm for use in real-time consumer route planning applications.

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[pt] Nos últimos anos as empresas varejistas começaram a investir em modelos menores de lojas, voltando-se para o atendimento de conveniência, que busca atender os novos hábitos de consumo da população. No entanto, esse modelo de loja representa um grande desafio logístico para as empresas, uma vez que por serem pequenas essas lojas normalmente não possuem espaço para estoque, necessitando de abastecimentos frequentes. A logística urbana apresenta alguns obstáculos como as restrições de circulação por tipo de veículo e as janelas de tempo, tornando a roteirização adequada fundamental para entregar o produto correto, no momento correto e na quantidade correta, garantindo assim a redução de veículos, distância percorrida e custos logísticos. Desta forma, este trabalho busca contribuir para o reabastecimento ótimo de lojas de conveniência localizadas em grandes centros urbanos através da otimização de rotas, considerando restrições de circulação e janelas de tempo disponíveis para abastecimento. Para isto, um estudo de caso foi feito em uma empresa do setor varejista utilizando uma ferramenta de código aberto baseada no Excel para resolver a variante do problema de roteirização de veículos. A ferramenta utilizada para resolver casos reais da empresa em estudo, considera o planejamento médio da demanda diária assim como a capacidade e disponibilidade dos veículos, tempos de descarga e restrições de horário para recebimento e circulação de veículos. Os resultados do estudo para os cenários discutidos apresentam uma redução de até 32 por cento no frete da empresa, além de oportunidades de ganhos operacionais frente à atual operação da empresa. / [en] In recent years retail companies have begun to invest in smaller store models, turning to the convenience service, which seeks to meet the new consumption habits of the population. However, this store model represents a major logistical challenge for companies, once they are small, usually do not have space for inventory and need frequent replenishments. City Logistics presents some obstacles such as traffic restrictions by vehicle type and time windows, making proper routing essential to deliver the right product at the right time and in the right amount, thus ensuring vehicle reduction, distance traveled and logistics costs. In this way, the objective of this paper is to contribute to the optimal replenishment of convenience stores located in large urban centers through an optimization of the routes, considering the traffic restrictions and the time windows available for supply. For this, a case study was done in a retail company using an Excel open source tool to solve the variant of the vehicle routing problem. The tool is used to solve real cases of the company under study, considering the average planning of daily demand as well as the capacity and availability of vehicles, unloading times and time constraints for receiving and circulating vehicles. The study results for the scenarios discussed present a reduction of up to 32 per cent in the company s freight as well as opportunities for operating gains against the company s current operation.

Goal number 12 of the Sustainable Development Goals (SDGs) by the United Nations (UN)emphasises on how the increase in the world population is hampering the achievement ofenvironmental sustainability. The diminishing resources of nature draw attention to the factthat more innovative methods need to salvage the environment and the planet at large.Textiles or clothing are one of the essential things that every human owns and with a globalpopulation of over 7 billion, there is an urgency to find ways to reduce its production, due tothe number of resources used and the amount of emissions the textile/clothing industryproduces.The Swedish Red Cross is a charitable organisation that deals with second-hand clothing toraise profits for its activities while contributing to environmental sustainability. Their secondhandretail shops give customers value for their money while extending the lifecycle of clothes.Due to their non-profitable nature puts much pressure on their finances. The need for revenuecalls for finding more environmentally sustainable ways of making a profit out of the clothesthey receive while evaluating the transportation options to reduce cost.Literature review and route optimisation in ArcGIS are used in the analysis. Driving timeanalysis is carried out to link stores to the closest depots to provide graphical and efficientmeans of reaching all the stores. The review of literature offers insights for environmentallysustainable ways of making a profit from second-hand clothes.The outcome provides results which will help generate more profit as well as cut down themajor contributor to the high transportation cost by more than 50%. Moreover, a suggestion ismade for a new time frame and a new travel mode to improve the coordination between thestores and the depots

[pt] O problema de Districting-and-routing é um problema estratégico no qual porções geográficas devem ser agregadas em regiões de entrega, e cada região de entrega possui um custo de roteamento estimado. Seu objetivo é de minimizar esses custos, além de garantir a divisão da região em distritos. A simulação para obter uma boa aproximação é muito custosa computacionalmente, enquanto mecanismos como buscas locais exigem que esse cálculo seja feito de forma muito eficiente, tornando essa estratégia de aproximação inviável para uma solução metaheurística. Grande parte das soluções existentes para esse problema utilizam de formulas de aproximação contínua para mensurar os custos de roteamento, funções essas que são rápidas de serem calculadas porém cometem erros significativos. Em contraste, propomos uma Rede Neural em Grafo (Graph Neural Network - GNN) que é usada como oráculo por um algoritmo de otimização. Nossos experimentos computacionais executados com dados de cidades do Reino Unido mostram que a GNN é capaz de produzir previsões de custos mais precisas em tempo computacional aceitável. O uso desse estimator na busca local impacta positivamente a qualidade das soluções, levando a uma economia de 10,35 por cento no custo de entrega estimado em relação a função Beardwood, que é comumente usada nesse cenários, e ganhos similares em comparação com outros métodos de aproximação. / [en] The districting-and-routing problem is a strategic problem in which basic geographical units (e.g., zip codes) should be aggregated into delivery regions, and each delivery region is characterized by a routing cost estimated over an extended planning horizon. The objective is to minimize the expected routing costs while ensuring regional separability through the definition of the districts. Repeatedly simulating routing costs on a set of scenarios while searching for good districts can be computationally intensive, so existing solution approaches for this problem rely on approximation functions. In contrast, we propose to rely on a graph neural network (GNN) trained on a set of demand scenarios, which is then used within an optimization approach to infer routing costs while solving the districting problem. Our computational experiments on various metropolitan areas show that the GNN produces accurate cost predictions. Moreover, using this better estimator during the search positively impacts the quality of the districting solutions and leads to 10.35 percent delivery-cost savings over the commonly-used Beardwood estimator and similar gains compared to other approximation methods.

Driven by recent technical advances, vehicle-based transportation systems in intralogistics are currently shifting from automated guided vehicles (AGVs) to autonomous mobile robots (AMRs). Unlike AGVs, AMRs are not bound to a physical or virtual track and autonomously determine their movements. While the increase in freedom for routing leads to improvements in terms of system flexibility and scalability, it also poses new challenges in terms of coordination and thus the high-performance routing behavior of an entire fleet. Accepted and widely used algorithms in the AGV field are often barely applicable to large fleets of free-ranging AMRs, while typical algorithms from the field of mobile robotics usually focus on different objectives. This thesis presents a novel concept for determining global routes, the so-called lanemap, that enables the synchronized movements of multiple free-ranging vehicles in arbitrary layouts without increasing calculation effort during online operations. The basic idea consists in creating a lanemap offline that provides the AMRs with a set of suggested global lanes from various starting positions to different destinations. On the one hand, the application of a lanemap lowers each AMRs’ individual degree of freedom. However, coordinating lanes sensibly beforehand enables short travel distances as well as a low probability of routing conflicts, which improves the performance of the entire AMR fleet on the other hand. Since the lanemap only provides an offline calculated global route as suggestion, the concept can be combined with any established online approach, such as a local conflict avoidance/resolution approach, or with in-depth coordination of all vehicle movements. This thesis presents a theoretical mathematical model and a practically applicable heuristic approach for the creation of a lanemap. As proof of concept, simulation experiments show that the heuristic is generally capable of creating lanemaps for all different kinds of layouts. Furthermore, the concept allows for the determination of customized routes for a specific fleet size and an anticipated transportation demand. Therefore, once system requirements are known, a beneficial set of lanemaps for typical constellations can be calculated in advance and integrated into routing as needed.

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.