The Evolution of Computer-Aided Road Design Systems

In order to locate a path between two known locations on a ground surface, a large number of alternative paths should be evaluated considering physical, economical, and environmental factors. Optimization techniques can be used to search for a path that minimizes the total costs while satisfying the design and environmental constraints. These techniques can result in considerable time savings in forest road design. Initially, these optimization techniques have been applied to highway design and recently, they have been applied to forest road design. This paper describes the evolution of the optimal route location systems used in both highway and forest road design based on ten criteria. The paper concludes by describing some of the unsolved problems in forest road design

Smart Algorithms for Hierarchical Clustering in Optical Network

Network design process is a very important in order to balance between the investment in the network and the supervises offered to the network user, taking into consideration, both minimizing the network investment cost, on the other hand, maximizing the quality of service offered to the customers as well.Partitioning the network to smaller sub-networks called clusters is required during the design process inorder to ease studying the whole network and achieve the design process as a trade-off between several atrtributes such as quality of service, reliability,cost, and management. Under CANON, a large scale optical network is partitioned into a number of geographically limited areas taking into account many different criteria like administrative domains, topological characteristics, traffic patterns, legacy infrastructure etc. An important consideration is that each of these clusters is comprised of a group of nodes in geographical proximity. The clusters can coincide with administrative domains but there could be many cases where two or more clusters belong to the same administrative domain. Therefore, in the most general case the partitioning into specific clusters can be either a off-line or a on-line process. In this work only the off-line case is considered. In this Study, we look at the problem of designing efficient 2- level Hierarchical Optical Networks (HON), in the context of network costs optimization. 2-level HON paradigm only have local rings to connect disjoint sets of nodes and a global sub mesh to interconnect all the local rings. We present an Hierarchical algorithm that is based on two phases. We present results for scenarios containing a set of real optical topologies

Solution techniques for a crane sequencing problem

In shipyards and power plants, relocating resources (items) from existing positions to newly assigned locations are costly and may represent a significant portion of the overall project budget. Since the crane is the most popular material handling equipment for relocating bulky items, it is essential to develop a good crane route to ensure efficient utilization and lower cost. In this research, minimizing the total travel and loading/unloading costs for the crane to relocate resources in multiple time periods is defined as the crane sequencing problem (CSP). In other words, the objective of the CSP is to find routes such that the cost of crane travel and resource loading/unloading is minimized. However, the CSP considers the capacities of locations and intermediate drops (i.e., preemptions) during a multiple period planning horizon. Therefore, the CSP is a unique problem with many applications and is computationally intractable. A mathematical model is developed to obtain optimal solutions for small size problems. Since large size CSPs are computationally intractable, construction algorithms as well as improvement heuristics (e.g., simulated annealing, hybrid ant systems and tabu search heuristics) are proposed to solve the CSPs. Two sets of test problems with different problem sizes are generated to test the proposed heuristics. In other words, extensive computational experiments are conducted to evaluate the performances of the proposed heuristics

Time-dependent routing : models, algorithms, and the value of information

Le problème de tournées de véhicules (Vehicle routing problem - VRP), introduit il y a plus de 60 ans, demeure au cœur des systèmes de transport. Après des décennies de développement, le VRP, par son ensemble très riche de variantes, représente l'un des problèmes les plus étudiés dans la littérature. Pourtant, en raison du manque de données, deux hypothèses importantes font que le VRP ne s'adapte pas efficacement au trafic et à la congestion, deux éléments importants pour modéliser de façon réelle des problèmes pratiques. Une première hypothèse considère que la vitesse de déplacement est constante dans le temps. La seconde, considère que chaque paire de nœuds (clients) n'est reliée que par un arc, ignorant le réseau routier implicite (sous-jacent). La congestion de la circulation est l'un des plus grands défis des systèmes de transport. Ces systèmes étant directement affectés par la congestion, l'ensemble de la chaîne d'approvisionnement doit s'adapter à ce facteur, ce qui n'est pas simple. La croissance continue du fret au cours des dernières années aggrave encore la situation et une attention renouvelée à la mobilité, à l'environnement et à la logistique urbaine a mis en lumière ces questions. Récemment, les avancées technologiques en communication et en acquisition de données en temps réel ont permis de collecter plusieurs informations sur les véhicules telles que leur localisation, leur accélération, leur vitesse, leur décélération, etc. Ainsi, nous pouvons remettre en question la façon dont nous définissons, modélisons et résolvons les problèmes de transport. Ceci nous permet de surmonter les deux hypothèses mentionnées en intégrant non seulement les informations relatives à la congestion, mais aussi en considérant l'ensemble du réseau routier. Dans cette thèse nous considérons l'ensemble du réseau routier sous-jacent, ce qui signifie que nous avons les nœuds clients mais également tous les nœuds intermédiaires qui constituent ce réseau. Ensuite, nous modélisons le temps de trajet de chaque route individuellement au cours de la journée. En divisant une journée en petits intervalles, jusqu'à une précision de l'ordre de la seconde, nous prenons en considération des informations précises sur le trafic. Il en résulte un nouveau problème appelé le problème de tournées de véhicules à plus court chemin avec dépendance du temps (Time-dependant shortest path vehicle routing problem - TD-SPVRP), dans lequel nous combinons le problème du plus court chemin avec dépendance du temps et le VRP avec dépendance du temps, créant ainsi un problème plus général et très complexe. Le TD-SPVRP est plus proche des conditions réelles et il constitue le sujet du chapitre 2 où nous le formulons comme un modèle de programmation linéaire en nombres entiers mixtes et concevons une heuristique rapide et efficace pour le résoudre. Nous testons le modèle ainsi que l'heuristique sur des instances générées à partir de données réelles de circulation sur le réseau routier de la ville de Québec, Canada. Les résultats montrent que l'heuristique fournit des solutions de haute qualité avec un écart moyen de 5,66% par rapport aux bornes inférieures déterminées par le modèle. Cependant, le modèle mathématique ne parvient pas à trouver aucune solution pour les instances de données réelles. Pour pouvoir résoudre ce problème complexe, une grande attention a été portée à la performance de l'implantation des algorithmes proposés afin d'améliorer leur rapidité en termes de temps d'exécution. Le problème reste très compliqué, surtout lorsque nous considérons une grande partie du réseau routier sous-jacent avec des données de trafic très précises. Pour cela, nous avons utilisé différentes techniques pour optimiser l'effort de calcul afin de résoudre le problème en évaluant l'impact engendré sur la précision tout en évitant la perte de précieuses informations. Nous avons développé deux types d'agrégation de données couvrant deux niveaux d'information différents. Premièrement, nous avons manipulé la structure du réseau en réduisant sa taille, et deuxièmement en contrôlant le niveau d'agrégation temporel pour générer les données de trafic et pour déterminer la vitesse d'un véhicule à tout moment. Pour la structure du réseau, nous avons utilisé différentes techniques de réduction de graphe pour en réduire la taille. Nous avons étudié la valeur et le compromis de l'information spatiale. Les solutions générées en utilisant le graphe réduit sont analysées dans le Chapitre 3 pour évaluer la qualité et la perte d'information dû à la réduction. Cette analyse démontre également que la transformation classique du TD-SPVRP en un problème de tournées dépendant du temps (Time-dependant VRP - TD-VRP) équivalent résulte en un graphe plus grand qui nécessite un temps de traitement important ce qui a un impact sur la qualité de la solution. Notre développement montre que la résolution du TD-SPVRP nécessite en moyenne 1445 secondes tandis que la résolution du TD-VRP associé nécessite 41 181 secondes. Garder un haut niveau de précision et réussir à réduire la taille du graphe est possible. En particulier, deux procédures de réduction ont été développées, la réduction des nœuds et la réduction des arcs parallèles. Les deux techniques réduisent la taille du graphe. La réduction des nœuds conduit à une amélioration de 1,11%, la réduction des arcs parallèles donne un écart de 2,57% signifiant la présence d'une distorsion dans le graphe réduit. En ce qui concerne les informations sur le trafic, nous avons analysé les compromis entre une grande quantité de données très précises et un plus petit volume de données agrégées avec une perte potentielle d'information. Ceci est fait en analysant la précision des données agrégées sous différents modèles de détermination des temps de parcours. Ces approches sont présentées dans le Chapitre 4. Au niveau de la prévision des temps de parcours, il est important que chaque segment routier ait des observations de vitesse pour chaque intervalle de temps considéré, ce que nous appelons le niveau de couverture du réseau. Notre analyse indique qu'une couverture complète du réseau routier à tout moment de la journée est nécessaire pour atteindre un niveau de précision élevé. Le recours à une agrégation élevée (de grands intervalles de temps) permet de réduire la taille du problème et d'obtenir une meilleure couverture des données, mais au prix d'une perte d'information. Les modèles analysés, LTM (link travel mode) et FSM (flow speed model), partagent les mêmes performances lorsqu'on utilise un grand intervalle de temps (120, 300 et 600 secondes), donc un niveau d'agrégation plus élevé, avec un écart moyen absolu de 5,5% par rapport aux temps de parcours observés. Cependant, avec une courte période (1, 10, 30 et 60 secondes), FSM fonctionne mieux que LTM. Pour un intervalle d'une seconde, FSM donne un écart absolu moyen de 6,70%, tandis que LTM fournit un écart de 11,17%. Ce chapitre détermine ainsi sous quelles conditions les modèles d'estimation de temps de parcours fonctionnent bien et procurent des estimations fidèles des temps de parcours réalisés. Cette thèse est structurée de la manière suivante. À la suite d'une introduction générale dans laquelle nous présentons le cadre conceptuel de la thèse et son organisation, le Chapitre 1 présente une revue de la littérature pour les deux problèmes fondamentaux étudiés, le problème de plus court chemin (Shortest path problem - SPP) et le VRP et leurs variantes développées au cours des années. Le Chapitre 2 introduit une nouvelle variante du VRP, le TD-SPVRP. Le Chapitre 3 présente les différentes techniques développées pour réduire la taille du réseau en manipulant les informations spatiales du réseau routier. L'impact de ces réductions est évalué et analysé sur des instances réelles en utilisant plusieurs heuristiques. Le Chapitre 4 traite l'impact de l'agrégation des données temporelle et des modèles d'évaluation des temps de parcours. Le dernier chapitre constitue une conclusion et ouvre des perspectives de recherche relatives à nos travaux.The vehicle routing problem (VRP), introduced more than 60 years ago, is at the core of transportation systems. With decades of development, the VRP is one of the most studied problems in the literature, with a very rich set of variants. Yet, primarily due to the lack of data, two critical assumptions make the VRP fail to adapt effectively to traffic and congestion. The first assumption considers that the travel speed is constant over time ; the second, that each pair of customers is connected by an arc, ignoring the underlying street network. Traffic congestion is one of the biggest challenges in transportation systems. As traffic directly affects transportation activities, the whole supply chain needs to adjust to this factor. The continuous growth of freight in recent years worsens the situation, and a renewed focus on mobility, environment, and city logistics has shed light on these issues. Recently, advances in communications and real-time data acquisition technologies have made it possible to collect vehicle data such as their location, acceleration, driving speed, deceleration, etc. With the availability of this data, one can question the way we define, model, and solve transportation problems. This allows us to overcome the two issues indicated before and integrate congestion information and the whole underlying street network. We start by considering the whole underlying street network, which means we have customer nodes and intermediate nodes that constitute the street network. Then, we model the travel time of each street during the day. By dividing the day into small intervals, up to a precision of a second, we consider precise traffic information. This results in a new problem called the time-dependent shortest path vehicle routing problem (TD-SPVRP), in which we combine the time-dependent shortest path problem (TD-SPP) and the time-dependent VRP (TD-VRP), creating a more general and very challenging problem. The TD-SPVRP is closer to what can be found in real-world conditions, and it constitutes the topic of Chapter 2, where we formulate it as a mixed-integer linear programming model and design a fast and efficient heuristic algorithm to solve this problem. We test it on instances generated from actual traffic data from the road network in Québec City, Canada. Results show that the heuristic provides high-quality solutions with an average gap of only 5.66%, while the mathematical model fails to find a solution for any real instance. To solve the challenging problem, we emphasize the importance of a high-performance implementation to improve the speed and the execution time of the algorithms. Still, the problem is huge especially when we work on a large area of the underlying street network alongside very precise traffic data. To this end, we use different techniques to optimize the computational effort to solve the problem while assessing the impact on the precision to avoid the loss of valuable information. Two types of data aggregation are developed, covering two different levels of information. First, we manipulated the structure of the network by reducing its size, and second by controlling the time aggregation level to generate the traffic data, thus the data used to determine the speed of a vehicle at any time. For the network structure, we used different reduction techniques of the road graph to reduce its size. We studied the value and the trade-off of spatial information. Solutions generated using the reduced graph are analyzed in Chapter 3 to evaluate the quality and the loss of information from the reduction. We show that the transformation of the TD-SPVRP into an equivalent TD-VRP results in a large graph that requires significant preprocessing time, which impacts the solution quality. Our development shows that solving the TD-SPVRP is about 40 times faster than solving the related TD-VRP. Keeping a high level of precision and successfully reducing the size of the graph is possible. In particular, we develop two reduction procedures, node reduction and parallel arc reduction. Both techniques reduce the size of the graph, with different results. While the node reduction leads to improved reduction in the gap of 1.11%, the parallel arc reduction gives a gap of 2.57% indicating a distortion in the reduced graph. We analyzed the compromises regarding the traffic information, between a massive amount of very precise data or a smaller volume of aggregated data with some potential information loss. This is done while analyzing the precision of the aggregated data under different travel time models, and these developments appear in Chapter 4. Our analysis indicates that a full coverage of the street network at any time of the day is required to achieve a high level of coverage. Using high aggregation will result in a smaller problem with better data coverage but at the cost of a loss of information. We analyzed two travel time estimation models, the link travel model (LTM) and the flow speed model (FSM). They both shared the same performance when working with large intervals of time (120, 300, and 600 seconds), thus a higher level of aggregation, with an absolute average gap of 5.5% to the observed route travel time. With short periods (1, 10, 30, and 60 seconds), FSM performs better than LTM. For 1 second interval, FSM gives an average absolute gap of 6.70%, while LTM provides a gap of 11.17%. This thesis is structured as follows. After a general introduction in which we present the conceptual framework of the thesis and its organization, Chapter 1 presents the literature review for the two main problems of our development, the shortest path problem (SPP) and the VRP, and their time-dependent variants developed over the years. Chapter 2 introduces a new VRP variant, the TD-SPVRP. Chapter 3 presents the different techniques developed to reduce the size of the network by manipulating spatial information of the road network. The impact of these reductions is evaluated and analyzed on real data instances using multiple heuristics. Chapter 4 covers the impact of time aggregation data and travel time models when computing travel times on the precision of their estimations against observed travel times. The conclusion follows in the last chapter and presents some research perspectives for our works

DYNAMIC DECISION MAKING FOR LESS-THAN-TRUCKLOAD TRUCKING OPERATIONS

On a typical day, more than 53 million tons of goods valued at about $36 million are moved on the US multimodal transportation network. An efficient freight transportation industry is the key in facilitating the required movement of raw materials and finished products. Among different modes of transportation, trucking remains the shipping choice for many businesses and is increasing its market share. Less-than-truckload (LTL) trucking companies provide a transportation service in which several customers are served simultaneously by using the same truck and shipments need to be consolidated at some terminals to build economical loads. Intelligent transportation system (ITS) technologies increase the flow of available data, and offer opportunities to control the transportation operations in real-time. Some research efforts have considered real-time acceptance/rejection of shipping requests, but they are mostly focused on truckload trucking operations. This study tries to use real-time information in decision making for LTL carriers in a dynamically changing environment. The dissertation begins with an introduction of LTL trucking operations and different levels of planning for this type of motor carriers, followed by the review of literature that are related to tactical and operational planning. Following a brief discussion on multi commodity network flow problems and their solution algorithm, a mathematical model is proposed to deal with the combined shipment and routing problem. Furthermore, a decision making procedure as well as a decision support application are developed and are presented in this dissertation. The main step in the decision making procedure is to solve the proposed mathematical problem. Three heuristic solution algorithms are proposed and the quality of the solutions is evaluated using a set of benchmark solutions. Three levels of numerical experiments are conducted considering an auto carrier that operates on a hub-and-spoke network. The accuracy of the mathematical model and the behavior of the system under different demand/supply situations are examined. Also, the performance of the solutions provided by the proposed heuristic algorithms is compared and the best solution method is selected. The study suggests that significant reductions in operational costs are expected as the result of using the proposed decision making procedure

Randomized rounding algorithms for large scale unsplittable flow problems

Unsplittable flow problems cover a wide range of telecommunication and transportation problems and their efficient resolution is key to a number of applications. In this work, we study algorithms that can scale up to large graphs and important numbers of commodities. We present and analyze in detail a heuristic based on the linear relaxation of the problem and randomized rounding. We provide empirical evidence that this approach is competitive with state-of-the-art resolution methods either by its scaling performance or by the quality of its solutions. We provide a variation of the heuristic which has the same approximation factor as the state-of-the-art approximation algorithm. We also derive a tighter analysis for the approximation factor of both the variation and the state-of-the-art algorithm. We introduce a new objective function for the unsplittable flow problem and discuss its differences with the classical congestion objective function. Finally, we discuss the gap in practical performance and theoretical guarantees between all the aforementioned algorithms

The IPS fidelity scale as a guideline to implement Supported Employment

info:eu-repo/semantics/publishe

Road-network location heuristics for the tactical harvest-scheduling model

In tactical planning in hierarchical forest management, cut-blocks are selected for maximizing revenue and road networks are allocated at minimal cost in order to maximize profit. The selected cut-block set and requisite road-network, connecting the cut-blocks, therefore have an interdependent relationship. The location of these two elements in tactical planning must therefore be considered simultaneously in a tactical harvest-scheduling model. This integration presents a major computational challenge, especially with regard to the execution time required to find an optimal solution to the tactical harvest-scheduling model. The objective of this thesis isto explore the influence of different road location heuristics, used within the tactical harvest scheduling model, upon the model’s execution time and solution quality. We nested the three different types of roadlocation heuristics within the harvest-scheduling model in order to evaluate their effectiveness by three criteria: execution time, road construction cost and objective function value. In addition, after the tactical model was run, we executed and evaluated the usefulness of a road network repair algorithm, designed to improve further the solution of the road-network location generated by the tactical harvest-scheduling model. The thre heuristics were evaluated on a real-world dataset, representing a section of the Kenogami forest in Ontario, Canada. Our result show: i) that the Shortest Path Origin Heuristic (SPOH) achieved the fastest execution time and lowest construction cost when integrated within the tactical harvest-scheduling model; and ii) that the road network repair algorithm successfully lowered the road network costs and thereby increased the objective function value of all solutions generated using the tactical planning model. These results are significant for two reasons: first, they show that the choice of the road network heuristic used within a tactical planning model can have a major influence on the model’s solution quality; and second, that the use of a road repair algorithm, on the solution generated using a tactical model, is of major economic value in forest management planning