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1. Optimal Speed-based Cost of Resilience in Electrified High-speed Railway Systems

   Pan, W.; Shittu, E. (January 2022, Proceedings of the IISE Annual Conference & Expo 2022 K. Ellis, W. Ferrell, J. Knapp, eds.)

   Ellis, K.; Ferrell, W.; Knapp, J. (Ed.)

   There is no doubt that there is an increase in the penetration of electrical energy into the operation of high-speed railway systems (HSR). This is even more pronounced with the increasing trends in smart electric multiple units (EMU). The operational speed serves as a metric for punctuality and safety, as well as a critical element to maintain the balance between energy supply and consumption. The speed-based regenerative energy from EMU’s braking mode could be utilized in the restoration of system operation in the aftermath of a failure. This paper optimizes the system resiliency with respect to the operational speed for the purpose of restoration by minimizing the total cost of implementing recovery measures. By simultaneously valuating the dual-impact of any given fault on the speed deterioration level from the railway operation systems (ROS) side and the power supply and demand unbalance level from the railway power systems (RPS) side, this process develops an adaptive two-dimension risk assessment scheme for prioritizing the handling of different operational zones that are cascaded in the system. With the aid of an integrated speed-based resilience cost model, we determine the optimal resilience time, speed modification plan, and energy allocation strategy. The outcome from implementing this routine in a real-world HSR offers a pioneering decision-making strategy and perspective on optimizing the resilience of an integrated system. 

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2. The ASEP speed process

   https://doi.org/10.1016/j.aim.2023.109004  

   Aggarwal, Amol; Corwin, Ivan; Ghosal, Promit (June 2023, Advances in Mathematics)
3. Scheduling with Speed Predictions

   Balkanski, Eric; Ou, Tingting; Stein, Clifford; We, Hao-Ting (September 2023, Workshop on Approximation and Online Algorithms. Springer Lecture Notes in Computer Science)
4. Scheduling with Speed Predictions

   Balkanski, Eric; Ou, Tingting; Stein, Clifford; Wei, Hao-Ting (September 2023, The Workshop on Approximation and Online Algorithms)

   Algorithms with predictions is a recent framework that has been used to overcome pessimistic worst-case bounds in incomplete information settings. In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initially unknown. In this paper, we study the speed-robust scheduling problem where the speeds of the machines, instead of the processing times of the jobs, are unknown and augment this problem with predictions. Our main result is an algorithm that achieves a $$\min\{\eta^2(1+\alpha), (2 + 2/\alpha)\}$$ approximation, for any $$\alpha \in (0,1)$$, where $$\eta \geq 1$$ is the prediction error. When the predictions are accurate, this approximation outperforms the best known approximation for speed-robust scheduling without predictions of $2-1/m$, where $$m$$ is the number of machines, while simultaneously maintaining a worst-case approximation of $$2 + 2/\alpha$$ even when the predictions are arbitrarily wrong. In addition, we obtain improved approximations for three special cases: equal job sizes, infinitesimal job sizes, and binary machine speeds. We also complement our algorithmic results with lower bounds. Finally, we empirically evaluate our algorithm against existing algorithms for speed-robust scheduling. 

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5. Maximum speed of dissipation

   https://doi.org/10.1103/PhysRevE.109.L052104  

   Das, Swetamber; Green, Jason_R (May 2024, Physical Review E)