255 research outputs found

    Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

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    abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

    Container Hinterland Drayage - On the Simultaneous Transportation of Containers Having Different Sizes

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    In an intermodal transportation chain drayage is the term used for the movement by truck of cargo that is filled in a loading unit. The most important intermodal transportation chain is the intermodal container transportation, in which containers represent the loading unit for cargo. Cost effectiveness constitutes a general problem of drayage operations. A major cost driver within container transportation chains is the movement and repositioning of empty containers. The present thesis investigates the potential to reduce drayage costs. Two solution methodologies are developed for operating a fleet of trucks that transports containers of different sizes, which addresses a recent gap in research in seaport hinterland regions

    Routing with Reloads

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    We examine routing problems with reloads, how they can be modeled, their properties and how they can be solved. We propose a simple model, the Pickup and Delivery Problem with Reloads (RPDP), that captures the process of reloading and can be extended for real world applications. We present results that show that the RPDP is solvable in polynomial time if the number of requests is bounded by a constant. Additionally, we examine a special case of the RPDP, the k-Star Hub Problem. This problem is solvable efficiently by network flow approaches if no more than two hubs are available. Otherwise, it is NP-complete. In the second part of this thesis, additional constraints are incorporated into the model and a tabu search heuristic for this problem is presented. The heuristic has been implemented and tested on several benchmarking instances, both artificial and a real-world application. In the appendix, we discuss the application of column generation for a reload problem

    Optimization of vehicle routing and scheduling with travel time variability - application in winter road maintenance

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    This study developed a mathematical model for optimizing vehicle routing and scheduling, which can be used to collect travel time information, and also to perform winter road maintenance operations (e.g., salting, plowing). The objective of this research was to minimize the total vehicle travel time to complete a given set of service tasks, subject to resource constraints (e.g., truck capacity, fleet size) and operational constraints (e.g., service time windows, service time limit). The nature of the problem is to design vehicle routes and schedules to perform the required service on predetermined road segments, which can be interpreted as an arc routing problem (ARP). By using a network transformation technique, an ARP can be transformed into a well-studied node routing problem (NRP). A set-partitioning (SP) approach was introduced to formulate the problem into an integer programming problem (I PP). To solve this problem, firstly, a number of feasible routes were generated, subject to resources and operational constraints. A genetic algorithm based heuristic was developed to improve the efficiency of generating feasible routes. Secondly, the corresponding travel time of each route was computed. Finally, the feasible routes were entered into the linear programming solver (CPL EX) to obtain final optimized results. The impact of travel time variability on vehicle routing and scheduling for transportation planning was also considered in this study. Usually in the concern of vehicle and pedestrian\u27s safety, federal, state governments and local agencies are more leaning towards using a conservative approach with constant travel time for the planning of winter roadway maintenance than an aggressive approach, which means that they would rather have a redundancy of plow trucks than a shortage. The proposed model and solution algorithm were validated with an empirical case study of 41 snow sections in the northwest area of New Jersey. Comprehensive analysis based on a deterministic travel time setting and a time-dependent travel time setting were both performed. The results show that a model that includes time dependent travel time produces better results than travel time being underestimated and being overestimated in transportation planning. In addition, a scenario-based analysis suggests that the current NJDOT operation based on given snow sector design, service routes and fleet size can be improved by the proposed model that considers time dependent travel time and the geometry of the road network to optimize vehicle routing and scheduling. In general, the benefit of better routing and scheduling design for snow plowing could be reflected in smaller minimum required fleet size and shorter total vehicle travel time. The depot location and number of service routes also have an impact on the final optimized results. This suggests that managers should consider the depot location, vehicle fleet sizing and the routing design problem simultaneously at the planning stage to minimize the total cost for snow plowing operations

    Essays on Shipment Consolidation Scheduling and Decision Making in the Context of Flexible Demand

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    This dissertation contains three essays related to shipment consolidation scheduling and decision making in the presence of flexible demand. The first essay is presented in Section 1. This essay introduces a new mathematical model for shipment consolidation scheduling for a two-echelon supply chain. The problem addresses shipment coordination and consolidation decisions that are made by a manufacturer who provides inventory replenishments to multiple downstream distribution centers. Unlike previous studies, the consolidation activities in this problem are not restricted to specific policies such as aggregation of shipments at regular times or consolidating when a predetermined quantity has accumulated. Rather, we consider the construction of a detailed shipment consolidation schedule over a planning horizon. We develop a mixed-integer quadratic optimization model to identify the shipment consolidation schedule that minimizes total cost. A genetic algorithm is developed to handle large problem instances. The other two essays explore the concept of flexible demand. In Section 2, we introduce a new variant of the vehicle routing problem (VRP): the vehicle routing problem with flexible repeat visits (VRP-FRV). This problem considers a set of customers at certain locations with certain maximum inter-visit time requirements. However, they are flexible in their visit times. The VRP-FRV has several real-world applications. One scenario is that of caretakers who provide service to elderly people at home. Each caretaker is assigned a number of elderly people to visit one or more times per day. Elderly people differ in their requirements and the minimum frequency at which they need to be visited every day. The VRP-FRV can also be imagined as a police patrol routing problem where the customers are various locations in the city that require frequent observations. Such locations could include known high-crime areas, high-profile residences, and/or safe houses. We develop a math model to minimize the total number of vehicles needed to cover the customer demands and determine the optimal customer visit schedules and vehicle routes. A heuristic method is developed to handle large problem instances. In the third study, presented in Section 3, we consider a single-item cyclic coordinated order fulfillment problem with batch supplies and flexible demands. The system in this study consists of multiple suppliers who each deliver a single item to a central node from which multiple demanders are then replenished. Importantly, demand is flexible and is a control action that the decision maker applies to optimize the system. The objective is to minimize total system cost subject to several operational constraints. The decisions include the timing and sizes of batches delivered by the suppliers to the central node and the timing and amounts by which demanders are replenished. We develop an integer programing model, provide several theoretical insights related to the model, and solve the math model for different problem sizes

    Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

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    Travelling Salesman Problem (TSP) is a list of cities that must visit all cities that start and end in the same city to find the minimum cost of time or distance. The Artificial Bee Colony (ABC) algorithm was used in this study to resolve the TSP. ABC algorithms is an optimisation technique that simulates the foraging behaviour of honey bees and has been successfully applied to various practical issues. ABC algorithm has three types of bees that are used by bees, onlooker bees, and scout bees. In Bavaria from the Library of Traveling Salesman Problem, the distance from one city to another has been used to find the best solution for the shortest distance. The result shows that the best solution for the shortest distance that travellers have to travel in all the 29 cities in Bavaria is 3974km

    On the impact of spatio-temporal granularity of traffic conditions on the quality of pickup and delivery optimal tours

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    International audienceOptimizing the duration of delivery tours is a crucial issue in urban logistics. In most cases, travel times between locations are considered as constant for the whole optimization horizon. Making these travel times time-dependent is particularly relevant in real urban traffic environments as traffic conditions and thus travel speeds vary according to the time of the day.In this paper, we review the literature on time-dependent routing problems, with a specific focus on benchmarks and performance criteria used to experimentally evaluate the interest of exploiting time-dependent data, showing the lack of studies on the impact of spatio-temporal features of the benchmark on solutions. Hence, we introduce a new benchmark produced from a realistic traffic flow micro-simulation of Lyon city, allowing us to consider different levels of spatial granularity (i.e., number of sensors used to measure traffic conditions) and temporal granularity (i.e., frequency of measures). Finally, we experimentally evaluate the impact of the spatio-temporal granularity on the quality of solutions for different classical problems, including the traveling salesman problem, the pickup and delivery problem, and the dial-a-ride problem
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