@article{45587, author = {{Habla, Wolfgang and Huwe, Vera and Kesternich, Martin}}, issn = {{1361-9209}}, journal = {{Transportation Research Part D: Transport and Environment}}, keywords = {{General Environmental Science, Transportation, Civil and Structural Engineering}}, publisher = {{Elsevier BV}}, title = {{{Electric and conventional vehicle usage in private and car sharing fleets in Germany}}}, doi = {{10.1016/j.trd.2021.102729}}, volume = {{93}}, year = {{2021}}, } @inproceedings{48839, abstract = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}}, author = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-5618-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, orienteering, transportation}}, pages = {{585–592}}, publisher = {{Association for Computing Machinery}}, title = {{{Local Search Effects in Bi-Objective Orienteering}}}, doi = {{10.1145/3205455.3205548}}, year = {{2018}}, } @inproceedings{48885, abstract = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}}, author = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-5764-7}}, keywords = {{algorithm selection, optimization, performance measures, transportation, travelling salesperson problem}}, pages = {{1737–1744}}, publisher = {{Association for Computing Machinery}}, title = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}}, doi = {{10.1145/3205651.3208233}}, year = {{2018}}, } @article{2856, abstract = {{Taxi ridesharing1 (TRS) is an advanced form of urban transportation that matches separate ride requests with similar spatio-temporal characteristics to a jointly used taxi. As collaborative consumption, TRS saves customers money, enables taxi companies to economize use of their resources, and lowers greenhouse gas emissions. We develop a one-to-one TRS approach that matches rides with similar start and end points. We evaluate our approach by analyzing an open dataset of > 5 million taxi trajectories in New York City. Our empirical analysis reveals that the proposed approach matches up to 48.34% of all taxi rides, saving 2,892,036 km of travel distance, 231,362.89 l of gas, and 532,134.64 kg of CO2 emissions per week. Compared to many-to-many TRS approaches, our approach is competitive, simpler to implement and operate, and poses less rigid assumptions on data availability and customer acceptance.}}, author = {{Barann, Benjamin and Beverungen, Daniel and Müller, Oliver}}, journal = {{Decision Support Systems}}, keywords = {{Taxi ridesharing Collaborative consumption Transportation Open data Sustainability Shared mobility}}, number = {{July 2017}}, pages = {{86--95}}, publisher = {{Elsevier}}, title = {{{An open-data approach for quantifying the potential of taxi ridesharing}}}, doi = {{10.1016/j.dss.2017.05.008}}, volume = {{99}}, year = {{2017}}, } @article{40493, abstract = {{ The United Nations Conference on the Law of the Sea was an arena in which global resource equity was negotiated in a process that extended beyond the governmental actors who took centre stage. But our perceptions of the role of an increasingly civil society in framing national decision-making processes during the 1970s – for instance, through trade associations or nongovernmental organizations – is blurred. Both civil society and economic actors crafted similar policies, though for different purposes: some – with regard to the north–south divide – focused on the conservation of the ocean’s resources over the long term, whereas others were more concerned with short-term economic benefits. This article asks which arguments legitimized property and usage rights, and which resource narratives were used. By taking into account the charged relationship of local–national–global reaches, it also examines perceptions and management of resources in the context of resource equity on a global scale. }}, author = {{Sackel, Johanna}}, issn = {{0843-8714}}, journal = {{International Journal of Maritime History}}, keywords = {{Transportation, History}}, number = {{3}}, pages = {{645--659}}, publisher = {{SAGE Publications}}, title = {{{Food justice, common heritage and the oceans: Resource narratives in the context of the Third United Nations Conference on the Law of the Sea}}}, doi = {{10.1177/0843871417713682}}, volume = {{29}}, year = {{2017}}, } @inproceedings{48874, abstract = {{State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem TSP are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.}}, author = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037}}, isbn = {{978-3-319-49129-5}}, keywords = {{Combinatorial optimization, Instance hardness, Metaheuristics, Transportation, TSP}}, pages = {{3–12}}, publisher = {{Springer-Verlag}}, title = {{{Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}}}, doi = {{10.1007/978-3-319-49130-1_1}}, year = {{2016}}, } @inproceedings{48887, abstract = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}}, author = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference }}, isbn = {{978-1-4503-3472-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, online algorithms, transportation}}, pages = {{425–432}}, publisher = {{Association for Computing Machinery}}, title = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}}, doi = {{10.1145/2739480.2754705}}, year = {{2015}}, }