Abstract

There has been growing interest in air transportation community to develop a routing decision model based on probabilistic characterization of severe weather. In the probabilistic air traffic management (PATM), decisions are made based on the stochastic weather information in the expected total cost sense. Probabilistic approach aims to enhance routing flexibility and reduce the risks associated with uncertainty of the future weather. In this research, a geometric model is adopted to generate optimal route choice when the future weather is stochastic. The geometric recourse model (GRM) is a strategic PATM model that incorporates route hedging and en-route recourse options to respond to weather change. Hedged routes are routes other than the nominal or detour route, and aircraft is re-routed to fly direct to the destination, which is called recourse, when the weather restricted airspace become flyable. Aircraft takes either the first recourse or the second recourse: The first recourse occurs when weather clears before aircraft reaches it flying on the initial route. The second recourse occurs when the aircraft is at the weather region. There are two variations of GRM: Single Recourse Model (SRM) with first recourse only and Dual Recourse Model (DRM) with both the first and second recourse options. When the weather clearance time follows a uniform distribution, SRM becomes convex with optimal route being either the detour or a hedged route. The DRM has a special property when the maximum storm duration time is less than the flight time to the tip of the storm on the detour route: it is always optimal to take the nominal route. The performance study is conducted by measuring the cost saving from either SRM or DRM. The result shows that there are cases with substantial cost saving, reaching nearly 30% with DRM.The ground-airborne hybrid model is an extension of the GRM, where both ground holding as well as route hedging are considered. The optimal combination of ground delay and route choice is determined by weather characteristics as well as the ground-airborne cost ratio. The numerical analysis reveals that whenever ground delay is required, the optimal route choice is the nominal one, while a non-nominal route is optimal when the ground delay is zero. There exists a unique critical cost ratio associated with given weather condition, which determines whether ground holding is optimal or not.

Abstract

"jats:p"The article proposes a method for determining the rational motion intensity of specific train traffic flows on railway transport corridors with account for balance of expenses on traction resources and cargo owners. A mathematical model based on stochastic optimization is developed, which allows to optimize, in the conditions of risks, the interval between trailing trains on the railway lines taking into account the limited resources of the traction rolling stock, the capacity of the stations and freight fronts at the cargo destination point. Solving this mathematical model allows to find a balance between the expenses for movement of train traffic flows from different railway lines to their terminal reference station and the expenses of a consignee, subject to the limitations of the technological logistics chain in cargo transportation. For the solution of this mathematical model, a Real-coded Genetic Algorithm (RGA) was used. Document type: Article

Abstract

Part 4: Energy Efficiency International audience We study the Stochastic Traffic Engineering (STE) problem arising from the support of QoS-demanding live (e.g., real time) audio/video applications over unreliable IP-over-wireless access pipes. First, we recast the problem to be tackled in the form of a suitable nonlinear stochastic optimization problem, and then we develop a goodput analysis for the resulting IP-over-wireless pipe that points out the relative effects of fading-induced errors and congestion-induced packet’s losses. Second, we present an optimal resource-management policy that allows a joint scheduling of playin, transmit and playout rates. Salient features of the developed joint scheduling policy are that: i) it is self-adaptive; and, ii) it is able to implement reliable Constant Bit Rate (CBR) connections on the top of unreliable energy-limited wireless pipes. Document type: Part of book or chapter of book

Abstract

Part 1: Smart Supply Networks; International audience; After a large-scale disaster, the emergency commodity should be distributed to relief centers. However, the initial commodity distribution may be unbalanced due to the incomplete information and uncertain environment. It is necessary to rebalance the emergency commodity among relief centers. Traffic congestion is an important factor to delay delivery of the commodity. Neither the commodity rebalancing nor traffic congestion is considered in previous studies. In this study, a two-stage stochastic optimization model is proposed to manage the commodity rebalancing, where uncertainties of demand and supply are considered. The goals are to minimize the expected total weighted unmet demand in the first stage and minimize the total transportation time in the second stage. Finally, a numerical analysis is conducted for a randomly generated instance; the results illustrate the effectiveness of the proposed model in the commodity rebalancing over the transportation network with traffic congestion. Document type: Conference object