An integrated optimal control model and its solution algorithm have been developed for commuting corridor management. Three trac control measures, including ramp metering, intersection signal timing, and freeway¯ow diversion, have been incorporated and optimized simultaneously. As an improvement to our previous work, the presented model and algorithm have the following three unique features: (1) modeling trac state evolution on surface streets with¯ow conservation within sections,¯ow transition between sections, and¯ow discharging at downstream intersections; (2) estimating time-dependent model parameters adaptively with real-time trac measurements, rather than assuming as pre-determined; and (3) having a limited-step convergence property in its solution logic. Preliminary numerical tests have demonstrated the potential of the developed model and algorithm for integrated real-time corridor control. #
The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline’s operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.
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