Abstract

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make that such solutions are not optimal with respect to minimizing the total duty time. Furthermore, VRPTW solutions do not account for complex driving hours regulations, which severely restrict the daily travel time available for a truck driver. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they account for temporal traffic congestions by modeling time-dependent travel times. For the latter, we assume a piecewise constant speed function. Computational experiments show that problem instances of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can be achieved. Finally, the results show that ignoring time-dependent travel times and driving hours regulations during the development of vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions.

Abstract

In this paper, we present a deep neural network based real-time integrated framework to detect objects, lane markings, and drivable space using a monocular camera for advanced driver assistance systems. The object detection framework detects and tracks objects on the road such as cars, trucks, pedestrians, bicycles, motorcycles, and traffic signs. The lane detection framework identifies the different lane markings on the road and also distinguishes between the ego lane and adjacent lane boundaries. The free space detection framework estimates the drivable space in front of the vehicle. In our integrated framework, we propose a pipeline combining the three deep neural networks into a single framework, for object detection, lane detection, and free space detection simultaneously. The integrated framework is implemented in C++ and runs real-time on the Nvidia's Drive PX 2 platform.

Abstract

This article discusses the results of supporting transition towards fully automated driving with remote operator support via the novel V2X channels. Automated passenger cars are equipped with multiple sensors (radars, cameras, LiDARs, inertia, GNSS, etc.), the operation of which is limited by weather, detection range, processing power and resolution. The study explores the use of a dedicated network for supporting automated driving needs. The MEC server latencies and bandwidths are compared between the Tampere, Finland test network and studies conducted in China to support remote passenger car operation. In China the main aim is to evaluate the network latencies in different communication planes, whereas the European focus is more on associated driving applications, thus making the two studies mutually complementary.5G revolutionizes connected driving, providing new avenues due to having lower and less latency variation and higher bandwidths. However, due to higher operating frequencies, network coverage is a challenge and one base station is limited to a few hundred meters and thus they deployed mainly to cities with a high population density. Therefore, the transport solutions are lacking so-called C-V2X (one form of 5G RAT) to enable data exchanges between vehicles (V2V) and also between vehicles and the digital infrastructure (V2I). The results of this study indicate that new edge-computing services do not cause a significant increase in latencies $(\\lt 100$ ms), but that latency variation (11 - 192 ms) remains a problem in the first new network configurations.

Abstract

International audience; We introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles. Electric vehicles are constrained in their service range by their battery capacity, and may require time-consuming recharging operations at some specific locations. Plug-in hybrid vehicles have two engines, an internal combustion engine and an electric engine using a built-in rechargeable battery. These vehicles can avoid visits to recharging stations by switching to fossil fuel. However, this flexibility comes at the price of a generally higher consumption rate and utility cost. To solve this complex problem variant, we design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search. All route evaluations, within the approach, are based on a layered optimization algorithm which combines labeling techniques and greedy evaluation policies to optimally insert recharging stations visits in a fixed trip and to select the fuel types. The metaheuristic is finally hybridized with an integer programming solver, over a set partitioning formulation, so as to recombine high-quality routes from the past search into better solutions. Extensive experimental analyses are conducted, highlighting the good performance of the algorithm and the contribution of each of its main components. Finally, we investigate the impact of fuel and energy cost on fleet composition decisions. Our experiments show that a careful use of a mixed fleet can significantly reduce operational costs in a large variety of price scenarios, in comparison with the use of a fleet composed of a single vehicle class.

Abstract

Trabajo presentado a la 4th International Conference on Control, Decision and Information Technologies (CoDIT), celebrada en Barcelona (España) del 5 al 7 de abril de 2017. Inland navigation networks are composed of several artificial canals that are characterized by no slope. These canals are particularly subject to resonance phenomena, which can create waves such that the navigation condition might not be guaranteed. It is therefore required to ensure dealing with freefault measured data and actuators. In this work, a fault detection and isolation method based on the Integrator Delay Zero model (IDZ) is designed for flat navigation canals. The proposed method is dedicated to the detection and isolation of sensor and actuator faults. Finally, it is tested by considering the Cuinchy-Fontinettes canal located in the north of France. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECOP) and FEDER through the project HARCRICS (ref. DPI2014-58104-R) and through the grant IJCI-2014-2081. Peer Reviewed Document type: Conference object

Abstract

We propose a metaheuristic for the Time-Dependent Pollution-Routing Problem, which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment with the objective of minimizing the cost of driver’s wage and greenhouse gases emissions. The vehicles face traffic congestion which, at peak periods, significantly restricts vehicle speeds and leads to increased emissions. Our algorithm is based on an adaptive large neighborhood search heuristic and uses new removal and insertion operators which significantly improve the quality of the solution. A previously developed departure time and speed optimization procedure is used as a subroutine to optimize departure times and vehicle speeds. Results from extensive computational experiments demonstrate the effectiveness of our algorithm.

Abstract

Increasing urbanization has turned transporting freight from, to, and within urban areas into a major challenge. Freight transportation represents a lifeline for urban retail and industry, but causes significant negative impacts on the quality of living in urban areas in terms of congestion, emissions and space consumption. City logistics initiatives have long suggested the need for collaborative and environmentally friendly urban transportation that could alleviate the negative impacts of urban transportation, but these face organizational and technological challenges of collaboration. Given technological advancements and innovative business models, concepts of collaborative urban transportation could contribute to a future paradigm of more sustainable and customer-friendly urban transportation. In this work, we collect and discuss contributions to collaborative freight transportation in urban areas with focus on recent publications (i.e. those published over the past ten years). We particularly analyze vertical and horizontal approaches of collaboration from an operations research perspective and point out strategic, tactical, and operational planning problems and solution approaches. To highlight research gaps and future research opportunities, we present innovative examples of collaborative urban transportation and analyze factors of failure and success. Collecting recent advances in theory and practice of collaborative urban transportation allows us to distill alternative visions of future collaborative freight transportation in urban areas. (C) 2018 Elsevier B.V. All rights reserved.

Abstract

International audience; Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.

Abstract

This is the post-print version of the final paper published in European Journal of Operational Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V. This study discusses a decision support framework that guides policy makers in their strategic transportation related decisions by using multi-methodology. For this purpose, a methodology for analyzing the effects of transportation policies on environment, society, economy, and energy is proposed. In the proposed methodology, a three-stage problem structuring model is developed. Initially, experts’ opinions are structured by using a cognitive map to determine the relationships between transportation and environmental concepts. Then a structural equation model (SEM) is constructed, based on the cognitive map, to quantify the relations among external transportation and environmental factors. Finally the results of the SEM model are used to evaluate the consequences of possible policies via scenario analysis. In this paper a pilot study that covers only one module of the whole framework, namely transportation–environment interaction module, is conducted to present the applicability and usefulness of the methodology. This pilot study also reveals the impacts of transportation policies on the environment. To achieve a sustainable transportation system, the extent of the relationships between transportation and the environment must be considered. The World Development Indicators developed by the World Bank are used for this purpose.

Abstract

val runways are a critical resource in the air traffic system. Arrival delays have a great impact on airline operations and cost. Therefore, tactical arrival planning is becoming increasingly important. In this paper, we consider the tactical single runway arrival problem. The current focus on collaborative decision making is reflected by giving airlines the possibility to provide cost functions related to arrival delays for their flights. A scaling method for these cost is introduced to ensure equity. Our formulation will assign landing times to the flights, while taking the costs into account. A problem-specific local search heuristic has been implemented to obtain reasonable solutions within acceptable computation times. A large number of instances, created using schedule data from a major European hub, have been tested. These experiments show large cost savings for the airlines compared to current practice. All airlines achieve improvements, which is important for the acceptance of the method. The heuristic is able to solve instances with over 100 flights in a few minutes. A simulation experiment shows the method can also be used in a dynamic setting and produces robust schedules. © 2007 Elsevier B.V. All rights reserved.