124 research outputs found

    A viral system to optimise the daily drayage problem

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    The intermodal transport chain can become more efficient by means of a good organisation of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. This paper focuses on a new approach to tackle the daily drayage problem by the use of viral system (VS). VS is a novel bio-inspired approach that makes use of a virus-infection biological analogy that is producing very satisfactory results when dealing with complex problems with huge feasibility region.Unión Europea TEC2013-47286-C3-3-

    The one container drayage problem with soft time windows

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    Intermodal freight transport consists of using different modes of transport without changing the load unit. This results in a significant reduction in the time that goods spend at intermodal terminals, where transshipment takes place. Drayage refers to the transport of freight on trucks among intermodal terminals, depots, customers and suppliers. In spite of the fact that drayage only represents between 5 and 10 percent of total distance, it may amount up to more than 30 percent of the total costs. The aim of this work is to study drayage operations. First, an extensive literature review is undertaken. Since the intermodal transport chain can become more efficient by means of a proper organisation of the drayage movements, the optimization of the daily drayage problem has been identified as one of the main ways of reducing the drayage cost and improving intermodal operations. On this problem, the lack of a common benchmark has hindered reaching further conclusions from all the research carried out. Therefore, this paper proposes a common framework and presents a generalized formulation of the problem, which allows modeling most drayage policies, with the limitation of only considering one-container problems. Results show that flexible tasks in the repositioning of empty containers as well as soft time windows can reduce the operating costs and facilitate the management of drayage companies. This work may help consider adequate policies regarding drayage operations in intermodal terminals

    Dynamic approach to solve the daily drayage problem with travel time uncertainty

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    The intermodal transport chain can become more e cient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the di erent vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible using a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

    Using Simulated Annealing to Solve the Daily Drayage Problem with Hard Time Windows

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    Drayage is the stage of the intermodal transport that deals with transport of freight on trucks among the intermodal terminal, and customers and suppliers that are located in its hinterland. This work proposes an algorithm based on simulated annealing heuristics to solve the operations of drayage. This algorithm has been used to solve battery problems, demonstrating the validity and suitability of its results, which were compared with exact method

    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

    Concepts, Mechanisms, and Algorithms to Measure the Potential of Container Sharing in Seaport Hinterland Transportation

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    This thesis analyzes how trucking companies of a hinterland region can improve their routes if shipping companies allow the mutual exchange of their containers. In this case, trucking companies that are assigned by shipping companies cooperate by sharing information regarding which locations empty containers are currently stacked. These containers can then be integrated into a vehicle's route of any operating trucking company in the hinterland. The investigation aims at measuring the quantitative potential of the container sharing idea by means of problem settings illustrating realistic hinterland regions of a seaport. As a first step, the impact of street turns on the transportation costs of a trucking company should be measured. By forbidding or allowing the use of street turns for a single trucking company, the potential of the container sharing idea can be indicated, and the interrelation of empty container movements and transportation costs can be shown. As a further step, the benefit of exchanging empty containers between several trucking companies needs to be analyzed. In doing so, it is possible to investigate the potential and realistic limits of container sharing

    A satellite navigation system to improve the management of intermodal drayage

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    The intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The literature shows some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective, whereas the work we present here incorporates the knowledge of the real-time position of the vehicles, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

    Knowledge of Real Time Position of Vehicles and Its Impact on the Improvement of Intermodal Drayage Operations

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    Libro de abstracts en la web del congreso: http://www.civil.ist.utl.pt/wctr12_lisboa/int_04_program_conference.htmThe intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations. There are some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective. The main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The work we present here considers the knowledge of the vehicles’ real-time position, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass). This additional data are used to dynamically improve the solution
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