This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various scheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced scheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance.

This paper presents a model for optimising truck platoons formed at a platooning hub. Different planning and dispatching strategies, from static to dynamic, are investigated with respect to profitability and fuel savings across a range of input variables. The problem is solved using a dynamic programming based local search heuristic. As a case study, a virtual platooning hub close to the German Elb Tunnel is examined using data from a large European transport network model. It is concluded that profitability crucially depends on; i) dynamic outlook and ii) if chauffeurs are allowed to rest while driving in platoons.

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This paper introduces a three-phase heuristic approach for a large-scale energy management and maintenance scheduling problem. The problem is concerned with scheduling maintenance and refueling for nuclear power plants up to five years into the future, while handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production costs. The first phase of the heuristic solves a simplified constraint programming model of the problem, the second performs a local search, and the third handles overproduction in a greedy fashion.This work was initiated in the context of the ROADEF/EURO Challenge 2010, a competition organized jointly by the French Operational Research and Decision Support Society, the European Operational Research Society, and the European utility companyÉlectricité de France. In the concluding phase of the competition our team ranked second in the junior category and sixth overall.After correcting an implementation bug in the program that was submitted for evaluation, our heuristic solves all ten real-life instances, and the solutions obtained are all within 2.45% of the currently best known solutions. The results given here would have ranked first in the original competition.

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