Urban Logistics and Decarbonisation Practices in Ireland

We presented the gaps and opportunities that emerge from current decarbonisation initiatives implemented in Ireland and how decarbonisation of urban logistics can help Ireland reduce the environmental impact of the haulage industry as part of the Ten-year Strategy for the Haulage Sector

Modeling demand uncertainty in two-tier city logistics tactical planning

We consider the complex and not-yet-studied issue of building the tactical plan of a two-tiered City Logistics system while explicitly accounting for the uncertainty in the forecast demand. We describe and formally define the problem, and then propose a general modeling framework, which takes the form of a two-stage stochastic programming formulation, the first stage selecting the first-tier service network design and the general workloads of the inter-tier transfer facilities, while the second stage determines the actual vehicle routing on the second tier as well as some limited adjustments of the first-stage service design decisions. Four different strategies of adapting the plan to the observed demand are introduced together with the associated recourse formulations. These strategies are then experimentally compared through an evaluation procedure that, based on Monte Carlo principles, mimics the decision process of a priori planning followed by repetitively applying the adjusted plan to the periods of the planning horizon. The performances of the City Logistics system under the adjustment strategies are contrasted through performance measures relative to the costs of operating the system, including those of additional vehicle capacity and movements required when the plan does not provide sufficient transportation means, the utilization of the various types of vehicles, the intensity of the vehicle presence within the city, and the utilization of the inter-tier transfer facilities. The comparisons are discussed both based on the numerical figures obtained through simulation and from the point of view of managerial insights into the implication for managing City Logistics physical and human resources. The analysis emphasizes the interest of flexibility in managing resources and operations for the overall performance of the system, discusses the associated trade-offs, and underlines the benefits of consolidation in terms of system efficiency and impact on the city. The comparisons also show that even when demand variability and management constraints are explicitly taken into account, our approach is still able to build good tactical plans

Planification de réseaux hyperconnectés et mutualisés de transport urbain de marchandises

RÉSUMÉ : Les secteurs de la logistique et du transport jouent un rôle essentiel dans les économies modernes, puisqu’ils représentent les différentes possibilités d’amélioration de la compétitivité des pays et ils imposent d’importants défis sociaux et environnementaux. En 2015, les coûts de logistique et de transport ont représenté respectivement 7,85% et 10% des PIB des États-Unis et de l’Union européenne. Pour la même année, ces industries ont contribué pour 5,5% aux émissions mondiales de GES ([Crainic et Montreuil, 2016]; [Fontaine et al., 2017]). Ces résultats découlent des nouvelles tendances du marché et de l’émergence des exigences (urbanisation, commerce électronique, etc.) et des systèmes logistiques et de transport de nature « complexe » (par exemple, plusieurs acteurs ayant différents objectifs, incertitudes, etc.). Pour faire face aux problèmes mentionnés, les réorganisations des systèmes logistiques et du transport actuels doivent encore être étudiées, planifiées, testées et évaluées. Au cours des dernières années, les cadres théoriques de la logistique urbaine (CL) et de l’Internet Physique (PI) ont pris de l’ampleur dans le monde de la recherche scientifique. L’objectif principal de la Logistique Urbaine est de réduire les impacts négatifs des mouvements de véhicules de fret en termes de congestion, de mobilité et d’impacts environnementaux, sans pénaliser les différentes activités sociales et économiques ([Taniguchi et Thompson, 2002]; [Taniguchi, 2014]). Plus précisément, il vise, tout d’abord, à réduire et contrôler la présence de véhicules de fret dans les zones urbaines. Deuxièmement, améliorer l’efficacité des mouvements de marchandises et réduire les impacts sur l’environnement, notamment en minimisant le trafic à vide des véhicules de fret sur les routes urbaines ([Benjelloun et Crainic, 2008]; [Dablanc, 2007]). L’Internet Physique (PI) est un nouveau concept de transport de marchandises et de logistique visant à améliorer l’efficacité économique, environnementale et sociale et la durabilité de la manière dont les objets physiques sont déplacés, stockés, réalisés, fournis et utilisés dans le monde entier ([Montreuil et al., 2013] ; [Montreuil et al., 2012]). Utilisant les mêmes concepts de l’Internet Numérique et de la même manière que les paquets de données transitent dans les réseaux Internet numériques, l’idée de PI est d’acheminer les marchandises encapsulées dans des conteneurs modulaires via un réseau global, interconnecté et ouvert ([Montreuil, 2009]; [Sarraj et al., 2012]). Le concept de PI est de plus en plus présent dans la recherche et les applications récentes qui ont démontré de vrais gains potentiels dans le transport de marchandises interurbain, les chaînes d’approvisionnement et la logistique ([Ballot et al., 2014]; [Sarraj et al., 2014]). Plusieurs concepts tels que la coopération, la consolidation, la manière de mettre en œuvre les activités de transport et de stockage de marchandises, sont des concepts-clés à la fois pour la logistique urbaine et l’Internet physique. Ces systèmes de transport sont complémentaires, puisque la logistique urbaine fournit les derniers segments de la logistique interconnectée et des réseaux de transport Internet physique. Malgré l’importance de ces concepts, [Crainic et Montreuil, 2016] ont affirmé qu’aucune étude n’avait exploré les liens et les synergies entre ces systèmes avancés de transport de marchandises et de logistique. De plus, à notre connaissance, aucune méthode de planification, de modélisation ou d’optimisation n’a été développée pour ce type de réseaux hyperconnectés. On vise à combler ces lacunes en introduisant l’idée des systèmes de la Logistique Urbaine Hyperconnectée et Mutualisée (HCL) "Hyperconnected City Logistics (HCL)". On discute des concepts-clés, des avantages potentiels et des défis en termes de recherches et de développements de la logistique urbaine hyperconnectée. Notre principal problème de recherche est le développement des modèles d’optimisation afin de mettre en place une planification d’un réseau HCL. On évalue les avantages et les enjeux de l’introduction du concept de coopération entre de nombreux acteurs logistiques, en particulier dans le cadre du partage des ressources dans un système de la logistique urbaine hyperconnectée. Dans ce mémoire, on propose des décisions tactiques liées à la conception et à la gestion du réseau de services. Dans notre modèle, on modélise les différents types de ressources, comme la taille des flottes et la capacité des satellites et des centres de distribution. De plus, notre modèle est multimodal puisqu’on considère plusieurs modes de transport comme les camions et les trams. On introduit, également, les concepts de coopération et de partage des ressources à la formulation classique de problème de conception de réseaux. Ce problème consiste à satisfaire la demande, tout en respectant les contraintes et les exigences de la mutualisation et du système HCL. L’objectif vise à minimiser les coûts de sélection et d’exploitation d’un service et les coûts d’affectations de la coalition considérée. Enfin, on effectue une série d’expériences numériques afin d’évaluer, d’une part, la performance du modèle et de l’approche proposée et, d’autre part, l’impact de l’adaptation de l’approche mutualisée et la multimodalité dans les modèles de planification tactique proposés dans le cadre d’un réseau HCL mutualisé. On a conclu que l’approche mutualisée et la multimodalité donnent plus de flexibilité et de meilleurs résultats pour les réseaux HCL. Les solutions obtenues ont validé les modèles de planification et les hypothèses proposés.----------ABSTRACT : Transport and logistics become increasingly important in the development, organization and operation of our society. Recently, the intensity of logistic activities has grown strongly in terms of volume since most of our activities require the movement of people and goods, that must be efficient and at minimum cost. However, these requirements can only be achieved with efficient infrastructure, services and logistics and transport activities. More specifically, the transportation of goods is an important factor for most economic and social activities in urban life [OECD, 2003]. In fact, the transport of goods in the city constitutes from 15% to 20% of all vehicle trips. This complexity is amplified by the increase of population and urbanization. In 2014, 54% of the world’s population was living in urban areas. The [Unies, 2004] are expecting a raise of 66% until 2050 and 85% until 2100 [OECD, 2003]. It results an increase in, both, demands and complexity of the distribution networks have increased. Therfore, ransportation industry becomes a source of various kinds of nuisances such as: noise, congestion, pollution, etc. In order to solve these problems, new paradigms have emerged, we are specifically interested in City Logistics (CL) Physical Internet (PI). The main objective of Urban Logistics is to reduce negative impacts of freight vehicle movements in terms of congestion, mobility and environmental impacts, without penalizing the different social and economic activities ([Taniguchi et Thompson, 2002]; [Taniguchi, 2014]). More specifically, it aims, first of all, to reduce and control the presence of freight vehicles in urban areas. Secondly, to improve the efficiency of goods movements and to reduce environmental impacts, especially by minimizing the empty traffic of freight vehicles in urban roads ([Benjelloun et Crainic, 2008]; [Dablanc, 2007]). The Physical Internet (PI), is a new concept for freight transportation and logistics aiming to improve the economic, environmental and social efficiency and sustainability of the manner that physical objects are moved, stored, realized, supplied and used around the world ([Montreuil et al., 2013]; [Montreuil et al., 2012]). Using the same concepts of the Digital Internet and in the same way that data packets transit in digital Internet networks, the idea of PI is to route goods whitch are encapsulated in modular containers through a global, interconnected and open network ([Montreuil, 2009]; [Sarraj et al., 2012]). The concept of PI is increasingly present in research and recent applications that have demonstrated a real potential gains in interurbain freight transportation, supply chains, and logistics ([Ballot et al., 2014]; [Sarraj et al., 2014]). Several concepts such as cooperation, consolidation, the way of implementing the activities of transport and storage of goods, are key concepts for both City Logistics and Physical Internet. This transport systems are complementary, since City Logistics provides the final segments of interconnected logistics and Physical Internet transportation networks. Despite the importance of these concepts, [Crainic et Montreuil, 2016] have claimed that no study has explored the links and synergies between these advanced systems of freight transport and logistics. Moreover, to the best of our knowledge, no planning, modeling or optimization methods have been developed for this type of hyperconnected networks. We aim to fill these gaps by introducing the Hyperconnected Urban Logistic Systems idea "Hyperconnected City Logistics (HCL)". We will discuss key concepts, potential benefits and challenges in term of research and development of the Hyperconnected City Logistics. Our main research problem is the development of optimization models in order to set up an hyperconnected urban network planning. We will propose tactical decisions related to the design and management of the hyperconnected service network. We evaluate how an Hyperconnected City Logistics system can be profitable when introducing the concept of cooperation between many logistic actors especially under the sharing of resources. Further, we model the resources, like feet size and satellite capacity, in our model. We also consider in our problem setting not only trucks but also other transportation mode for example Trams. We, also, introduce a new Integer Programming formulation for the problem. This formulations benefits from the fact that, compared to classical network design formulations, we introduce cooperation and ressouces sharing

Learning-Based Matheuristic Solution Methods for Stochastic Network Design

Cette dissertation consiste en trois études, chacune constituant un article de recherche. Dans tous les trois articles, nous considérons le problème de conception de réseaux multiproduits, avec coût fixe, capacité et des demandes stochastiques en tant que programmes stochastiques en deux étapes. Dans un tel contexte, les décisions de conception sont prises dans la première étape avant que la demande réelle ne soit réalisée, tandis que les décisions de flux de la deuxième étape ajustent la solution de la première étape à la réalisation de la demande observée. Nous considérons l’incertitude de la demande comme un nombre fini de scénarios discrets, ce qui est une approche courante dans la littérature. En utilisant l’ensemble de scénarios, le problème mixte en nombre entier (MIP) résultant, appelé formulation étendue (FE), est extrêmement difficile à résoudre, sauf dans des cas triviaux. Cette thèse vise à faire progresser le corpus de connaissances en développant des algorithmes efficaces intégrant des mécanismes d’apprentissage en matheuristique, capables de traiter efficacement des problèmes stochastiques de conception pour des réseaux de grande taille. Le premier article, s’intitulé "A Learning-Based Matheuristc for Stochastic Multicommodity Network Design". Nous introduisons et décrivons formellement un nouveau mécanisme d’apprentissage basé sur l’optimisation pour extraire des informations concernant la structure de la solution du problème stochastique à partir de solutions obtenues avec des combinaisons particulières de scénarios. Nous proposons ensuite une matheuristique "Learn&Optimize", qui utilise les méthodes d’apprentissage pour déduire un ensemble de variables de conception prometteuses, en conjonction avec un solveur MIP de pointe pour résoudre un problème réduit. Le deuxième article, s’intitulé "A Reduced-Cost-Based Restriction and Refinement Matheuristic for Stochastic Network Design". Nous étudions comment concevoir efficacement des mécanismes d’apprentissage basés sur l’information duale afin de guider la détermination des variables dans le contexte de la conception de réseaux stochastiques. Ce travail examine les coûts réduits associés aux variables hors base dans les solutions déterministes pour guider la sélection des variables dans la formulation stochastique. Nous proposons plusieurs stratégies pour extraire des informations sur les coûts réduits afin de fixer un ensemble approprié de variables dans le modèle restreint. Nous proposons ensuite une approche matheuristique utilisant des techniques itératives de réduction des problèmes. Le troisième article, s’intitulé "An Integrated Learning and Progressive Hedging Method to Solve Stochastic Network Design". Ici, notre objectif principal est de concevoir une méthode de résolution capable de gérer un grand nombre de scénarios. Nous nous appuyons sur l’algorithme Progressive Hedging (PHA), ou les scénarios sont regroupés en sous-problèmes. Nous intégrons des methodes d’apprentissage au sein de PHA pour traiter une grand nombre de scénarios. Dans notre approche, les mécanismes d’apprentissage developpés dans le premier article de cette thèse sont adaptés pour résoudre les sous-problèmes multi-scénarios. Nous introduisons une nouvelle solution de référence à chaque étape d’agrégation de notre ILPH en exploitant les informations collectées à partir des sous problèmes et nous utilisons ces informations pour mettre à jour les pénalités dans PHA. Par conséquent, PHA est guidé par les informations locales fournies par la procédure d’apprentissage, résultant en une approche intégrée capable de traiter des instances complexes et de grande taille. Dans les trois articles, nous montrons, au moyen de campagnes expérimentales approfondies, l’intérêt des approches proposées en termes de temps de calcul et de qualité des solutions produites, en particulier pour traiter des cas très difficiles avec un grand nombre de scénarios.This dissertation consists of three studies, each of which constitutes a self-contained research article. In all of the three articles, we consider the multi-commodity capacitated fixed-charge network design problem with uncertain demands as a two-stage stochastic program. In such setting, design decisions are made in the first stage before the actual demand is realized, while second-stage flow-routing decisions adjust the first-stage solution to the observed demand realization. We consider the demand uncertainty as a finite number of discrete scenarios, which is a common approach in the literature. By using the scenario set, the resulting large-scale mixed integer program (MIP) problem, referred to as the extensive form (EF), is extremely hard to solve exactly in all but trivial cases. This dissertation is aimed at advancing the body of knowledge by developing efficient algorithms incorporating learning mechanisms in matheuristics, which are able to handle large scale instances of stochastic network design problems efficiently. In the first article, we propose a novel Learning-Based Matheuristic for Stochastic Network Design Problems. We introduce and formally describe a new optimizationbased learning mechanism to extract information regarding the solution structure of a stochastic problem out of the solutions of particular combinations of scenarios. We subsequently propose the Learn&Optimize matheuristic, which makes use of the learning methods in inferring a set of promising design variables, in conjunction with a state-ofthe- art MIP solver to address a reduced problem. In the second article, we introduce a Reduced-Cost-Based Restriction and Refinement Matheuristic. We study on how to efficiently design learning mechanisms based on dual information as a means of guiding variable fixing in the context of stochastic network design. The present work investigates how the reduced cost associated with non-basic variables in deterministic solutions can be leveraged to guide variable selection within stochastic formulations. We specifically propose several strategies to extract reduced cost information so as to effectively identify an appropriate set of fixed variables within a restricted model. We then propose a matheuristic approach using problem reduction techniques iteratively (i.e., defining and exploring restricted region of global solutions, as guided by applicable dual information). Finally, in the third article, our main goal is to design a solution method that is able to manage a large number of scenarios. We rely on the progressive hedging algorithm (PHA) where the scenarios are grouped in subproblems. We propose a two phase integrated learning and progressive hedging (ILPH) approach to deal with a large number of scenarios. Within our proposed approach, the learning mechanisms from the first study of this dissertation have been adapted as an efficient heuristic method to address the multi-scenario subproblems within each iteration of PHA.We introduce a new reference point within each aggregation step of our proposed ILPH by exploiting the information garnered from subproblems, and using this information to update the penalties. Consequently, the ILPH is governed and guided by the local information provided by the learning procedure, resulting in an integrated approach capable of handling very large and complex instances. In all of the three mentioned articles, we show, by means of extensive experimental campaigns, the interest of the proposed approaches in terms of computation time and solution quality, especially in dealing with very difficult instances with a large number of scenarios

Service Network Design for Parcel Trucking

We develop a large-scale package express service network design methods using integer programming optimization models specified on flat network models that capture important timing constraints to ensure that package flows meet service constraints. In the first part, we focus on shuttle activities and develop optimization technology for the design of shuttle services using novel rate-based models to determine package flow paths as well as vehicle routes. A computational study using data from a large Chinese package company demonstrates that the technology produces a cost-effective service network design for shuttle schedules with excellent on-time performance. The second part presents a strategic hub selection problem developing a cost-effective greedy heuristic approach that solves tractable integer programming models to add a single intermediate hub on each iteration. A computational study shows that the greedy approach selects geographically-distributed and cost-effective hubs for package transfer, and moreover, the heuristic outperforms the full optimization model by a 20% gap difference for the relevant test instances. In the last part, we develop a new approach for solving the flow planning problem of service network design for large-scale networks with timing constraints. We introduce a so-called generalized in-tree, referred to as GIT, which has useful operational benefits. We demonstrate, via a computational study, that imposing a discretized GIT structure that groups remaining times into fixed-width buckets of 2 hours or 4 hours leads to solutions that are only 2% to 4% more costly than those that do not require GIT structure but significantly simpler to operationalize.Ph.D

The future of last-mile delivery: a scenario thinking approach

`Last mile' in cities is not merely a logistics problem, but also a significant urban planning challenge. Last mile is the final leg of the supply chain that involves high-frequency, low-volume, and short-haul distribution of goods to end consumers. This last leg is the most important, yet the least efficient part of supply chain. With rapid growth of online retail transactions and increased supply chain complexity of the globalised production networks, the size, scale and scope of last mile-driven logistics problems are most likely to escalate in the immediate future. Transport infrastructure and planning controls are among the key factors that contribute to the severity of last mile delivery (LMD) problems in large cities. However, interrelationships and interdependencies between last mile logistics and transport networks and urban planning controls are neither theoretically evaluated nor empirically tested in the extant literature. This thesis aims to measure and map the potential transportation network impedance to last mile delivery, build future last mile delivery scenarios and formulate a strategic framework to mitigate the risk of last mile delivery failure within Metropolitan Melbourne. A five-stage Scenario Thinking method was applied to understand and analyse the provision of last mile delivery and the associated `critical uncertainties'. A Geographic Information System (GIS) was used to compute and visualise the potential transportation network impedance to last mile delivery within the Metropolitan Melbourne. Spatial data representing the key constraints of transportation network and planning controls were used to compute the potential transportations network impedance to last mile delivery. A Scenario Thinking (ST) workshop was conducted with 14 participants who represent three major stakeholders, namely operators, administrators and users.  This was designed to discuss and identify key planning and transportation constraints contributing to last mile challenges and to formulate future possible and plausible last mile delivery scenarios. Thirty-four major issues that underpin last mile logistics were identified, which were clustered into six thematic dimensions through an iterative consultation process.  These include: i) Freight Infrastructure; ii) Infrastructure Supply; iii) Landuse Intensity; iv) Infrastructure Sharing; v) Intersection Controls and vi) Human Behaviour.  Infrastructure Supply and Landuse Intensity were found to represent higher uncertainty and higher impact on city logistics provisions. Hence, they were used to build future LMD scenarios. Spatial data of seventeen mappable constraints, including traffic count, population density, zoning, proximity to Melbourne CBD and activity centres, intersection constraint, speed limit, number of lanes, toll, railway boom gate, traffic lights and trams routes, were standardised and aggregated using a composite index technique. The generated LMD impedance index was then mapped using an overlay function to estimate the potential hindrance to last mile delivery as imposed by built and regulatory environment. The mapped outputs illustrate significant spatial variations in LMD impedance levels across different parts of Metropolitan Melbourne. Impedance to last mile reduces with increased distance from the Central Business District, found to be high within the designated activity centres and varies across inner, middle and outer rings.   The results reveal that the future outcomes of last-mile delivery is dependent upon which scenario eventuates out of the four scenarios formulated using Infrastructure Supply and Landuse Intensity. The best-best scenario characterises an increase in infrastructure usage, decrease in road congestion, lower delivery cost, and increased last mile delivery productivity and efficiency; whilst the worst-worst scenario represents a decrease in the usage of logistics infrastructure, transport delay and congestion, increased last-mile delivery cost, loss of work productivity, reduced deliveries per day and higher environmental impacts. Further analysis indicates the relative positioning of major stakeholders based on an interest-power matrix. Last mile logistics stakeholders such as Federal, State and Local Governments, and Transurban with high-power tend to exhibit low-interest in provisioning last-mile logistics; while those with high interest such as Drivers and Business owners have low power to influence any positive change to enhance the efficiency of last-mile delivery. Stakeholders with high-power and high-interest were identified to include Public Transport Victoria, VicRoads, Trader Association, Port Authority who could assist in policy-making to help improve last-mile delivery efficiency and curtail carbon footprint within an acceptable level. In this study, a strategic framework is developed to support the formulation of key objectives and future-oriented actions. Five strategies along with key actions were proposed to tackle the LMD challenges associated in Melbourne. These include: land-use zoning, last mile corridor, distribution network strategies, multimodal use strategy and stakeholder engagement strategy.  The land use zoning strategy can be implemented to geographically demarcate last-mile delivery zones to improve the efficiency of last-mile delivery to retail businesses within the localised area.  The last mile corridor strategy would expedite last-mile delivery along the main arterial networks through the development of linear freight routes to improve last-mile connectivity between key business hubs.  This geo-targeted strategy will help reduce the environmental footprint of last-mile delivery, ease traffic bottlenecks and potential conflict between last-mile delivery trucks and commuters by confining truck movements to designated routes. The distribution network strategy would promote consolidation of goods through a holistic integration of people, facilities and transportation infrastructure as a single unified city logistics network to support the development of Urban Distribution and Consolidation Centres in vicinity to the Activity Centres. The Multi modal use promotes LMD using trucks and train, trams or bicycles as an integrated system. The stakeholder engagement strategy places a greater emphasis on the shift in interest of stakeholders with power to cause positive change through advocating for the implementation of effective policies and regulations. Overall, this thesis is the first study that applied the Scenario Thinking method along with GIS to construct, measure and map the potential last-mile delivery impedance. Scenarios were formulated to provide improved understanding of future last-mile delivery. Strategies were recommended to help develop operational plans and deploy future investment to tackle the challenges associated with the worst/worst scenario.  The mapped outputs improve the future understanding of the complex interactions between transportation infrastructure, planning controls and last-mile delivery. This understanding and proposed strategies in turn would help enhance the efficiency of last-mile delivery in the context of large cities