no code implementations • 29 Sep 2021 • Feng Guo, Qu Wei, Miao Wang, Zhaoxia Guo
We thus propose a Deep Dynamic Attention Models with Gate Mechanisms (DDAM-GM) to learn heuristics for time-dependent VRPs (TDVRPs) in real-world road networks.
no code implementations • 8 Dec 2020 • Enayat A. Moallemi, Sibel Eker, Lei Gao, Michalis Hadjikakou, Qi Liu, Jan Kwakkel, Patrick M. Reed, Michael Obersteiner, Zhaoxia Guo, Brett A. Bryan
Progress to-date towards the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets.
no code implementations • 1 Jun 2020 • Dongqing Zhang, Stein W. Wallace, Zhaoxia Guo, Yucheng Dong, Michal Kaut
We find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i. e., relative difference and variance) whichever origin-destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6-10 times lower for a stability level of 1\%; and (3) different origin-destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.