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- Basso, Rafael, 1979, et al.
(författare)
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Traffic aware electric vehicle routing
- 2016
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil,November 1-4. ; , s. Art no 7795588, Pages 416-421
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Konferensbidrag (refereegranskat)abstract
- Since the main constraint of electric vehicles is range due to limited battery capacity, the focus for routing these kind of vehicles should be energy consumption minimization. And since energy consumption depends on several aspects, this article introduces a new model for route optimization of Electric Commercial Vehicles, with a realistic energy consumption model based on factors such as road inclination, weight and speed. The main new feature is to consider average speed for the road network at different times during the day, with the vehicle adapting to traffic flow. Several experiments were performed to evaluate the impact of different elements in energy consumption. As a result a few topics are recommended for future work.
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| 2. |
- Basso, Rafael, 1979, et al.
(författare)
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Energy consumption estimation integrated into the Electric Vehicle Routing Problem
- 2019
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 69, s. 141-167
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Tidskriftsartikel (refereegranskat)abstract
- When planning routes for fleets of electric commercial vehicles, it is necessary to precisely predict the energy required to drive and plan for charging whenever needed, in order to manage their driving range limitations. Although there are several energy estimation models available in the literature, so far integration with Vehicle Routing Problems has been limited and without demonstrated accuracy. This paper introduces the Two-stage Electric Vehicle Routing Problem (2sEVRP) that incorporates improved energy consumption estimation by considering detailed topography and speed profiles. First, a method to calculate energy cost coefficients for the road network is outlined. Since the driving cycle is unknown, the model generates an approximation based on a linear function of mass, as the latter is only determined while routing. These coefficients embed information about topography, speed, powertrain efficiency and the effect of acceleration and braking at traffic lights and intersections. Secondly, an integrated two-stage approach is described, which finds the best paths between pairs of destinations and then finds the best routes including battery and time-window constraints. Energy consumption is used as objective function including payload and auxiliary systems. The road cost coefficients are aggregated to generate the path cost coefficients that are used in the routing problem. In this way it is possible to get a proper approximation of the complete driving cycle for the routes and accurate energy consumption estimation. Lastly, numerical experiments are shown based on the road network from Gothenburg-Sweden. Energy estimation is compared with real consumption data from an all-electric bus from a public transport route and with high-fidelity vehicle simulations. Routing experiments focus on trucks for urban distribution of goods. The results indicate that time and energy estimations are significantly more precise than existing methods. Consequently the planned routes are expected to be feasible in terms of energy demand and that charging stops are properly included when necessary.
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| 5. |
- Eriksson Barman, Sandra, 1985, et al.
(författare)
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Modeling and solving vehicle routing problems with many available vehicle types
- 2015
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Ingår i: Springer Proceedings in Mathematics & Statistics. - Cham : Springer International Publishing. - 2194-1009 .- 2194-1017. - 9783319185668
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Konferensbidrag (refereegranskat)abstract
- Vehicle routing problems (VRP) involving the selection of vehicles from a large set of vehicle types are hitherto not well-studied in the literature. Such problems arise at Volvo Group Trucks Technology, who faces an immense set of possible vehicle configurations, of which an optimal set needs to be chosen for each specific combination of transport missions. Another property of real-world VRP’s that is often neglected in the literature is that the fuel resources required to drive a vehicle along a route is highly dependent on the actual load of the vehicle. We define the fleet size and mix VRP with many available vehicle types, called many-FSMVRP, and suggest an extended set-partitioning model of this computationally demanding combinatorial optimization problem. To solve the extended model, we have developed a method based on Benders’ decomposition, the subproblems of which are solved using column generation, and the column generation subproblems being solved using dynamic programming; the method is implemented with a so-called projection-of-routes procedure. The resulting method is compared with a column generation approach for the standard set-partitioning model. Our method for the extended model performs on par with column generation applied to the standard model for instances such that the two models are equivalent. In addition, the utility of the extended model for instances with very many available vehicle types is demonstrated. Our method is also shown to efficiently handle cases in which the costs are dependent on the load of the vehicle. Computational tests on a set of extended standard test instances show that our method, based on Benders’ algorithm, is able to determine combinations of vehicles and routes that are optimal to a relaxation (w.r.t. the route decision variables) of the extended model. Our exact implementation of Benders’ algorithm appears, however, too slow when the number of customers grows. To improve its performance, we suggest that relaxed versions of the column generation subproblems are solved, and that the set-partitioning model is replaced by a set-covering model.
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| 7. |
- Lindroth, Peter, 1979, et al.
(författare)
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Approximating the Pareto Optimal Set using a Reduced Set of Objective Functions
- 2010
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Ingår i: European Journal of Operational Research. - : Elsevier BV. - 0377-2217. ; 207:3, s. 1519-1534
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Tidskriftsartikel (refereegranskat)abstract
- Real-world applications of multi-objective optimization often involve numerous objective functions. But while such problems are in general computationally intractable, it is seldom necessary to determine the Pareto optimal set exactly. A significantly smaller computational burden thus motivates the loss of precision if the size of the loss can be estimated. We describe a method for finding an optimal reduction of the set of objectives yielding a smaller problem whose Pareto optimal set w.r.t. a discrete subset of the decision space is as close as possible to that of the original set of objectives. Utilizing a new characterization of Pareto optimality and presuming a finite decision space, we derive a program whose solution represents an optimal reduction. We also propose an approximate, computationally less demanding formulation which utilizes correlations between the objectives and separates into two parts. Numerical results from an industrial instance concerning the configuration of heavy-duty trucks are also reported, demonstrating the usefulness of the method developed. The results show that multi-objective optimization problems can be significantly simplified with an induced error which can be measured.
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| 10. |
- Lindroth, Peter, 1979, et al.
(författare)
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Optimizing truck tyres
- 2014
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Ingår i: Orbit medlemsblad for Dansk Selskab for Operationsanalyse og Svenska OperationsAnalysFöreningen. - 1601-8893. ; :23, s. 12-14
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Tidskriftsartikel (refereegranskat)
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