Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

The airline schedule planning problem is defined as the sequence of decisions that need to be made to obtain a fully operational flight schedule. Historically, the airline scheduling problem has been sequentially solved. However, there have already been many attempts in order to obtain airline schedules in an integrated way. But due to tractability issues it is nowadays impossible to determine a fully operative and optimal schedule with an integrated model which accounts for all the key airline related aspects such as competitive effects, stochastic demand figures and uncertain operating conditions. Airlines usually develop base schedules, which are obtained much time in advance to the day of operations and not accounting for all the related uncertainty. This paper proposes a mathematical model in order to update base schedules in terms of timetable and fleet assignments while considering stochastic demand figures and uncertain operating conditions, and where robust itineraries are introduced in order to ameliorate miss-connected passengers. The proposed model leads to a large-scale problem which is difficult to be solved. Therefore, a novel improved and accelerated Benders decomposition approach is proposed. The analytical work is supported with case studies involving the Spanish legacy airline, IBERIA. The presented approach shows that the number of miss-connected passengers may be reduced when robust planning is applied.     

Privacy-preserving MaaS fleet management

On-demand traffic fleet optimization requires operating Mobility as a Service (MaaS) companies such as Uber, Lyft to locally match the offer of available vehicles with their expected number of requests referred to as demand (as well as to take into account other constraints such as driver’s schedules and preferences). In the present article, we show that this problem can be encoded into a Constrained Integer Quadratic Program (CIQP) with block independent constraints that can then be relaxed in the form of a convex optimization program. We leverage this particular structure to yield a scalable distributed optimization algorithm corresponding to computing a gradient ascent in a dual space. This new framework does not require the drivers to share their availabilities with the operating company (as opposed to standard practice in today’s mobility as a service companies). The resulting parallel algorithm can run on a distributed smartphone based platform.     

A cost-competitiveness analysis of charging infrastructure for electric bus operations

This study investigates the cost competitiveness of different types of charging infrastructure, including charging stations, charging lanes (via charging-while-driving technologies) and battery swapping stations, in support of an electric public transit system. To this end, we first establish mathematical models to investigate the optimal deployment of various charging facilities along the transit line and determine the optimal size of the electric bus fleet, as well as their batteries, to minimize total infrastructure and fleet costs while guaranteeing service frequency and satisfying the charging needs of the transit system. We then conduct an empirical analysis utilizing available real-world data. The results suggest that: (1) the service frequency, circulation length, and operating speed of a transit system may have a great impact on the cost competitiveness of different charging infrastructure; (2) charging lanes enabled by currently available inductive wireless charging technology are cost competitive for most of the existing bus rapid transit corridors; (3) swapping stations can yield a lower total cost than charging lanes and charging stations for transit systems with high operating speed and low service frequency; (4) charging stations are cost competitive only for transit systems with very low service frequency and short circulation; and (5) the key to making charging lanes more competitive for transit systems with low service frequency and high operating speed is to reduce their unit-length construction cost or enhance their charging power.     

A bi-objective dynamic programming approach for airline green fleet planning

Ensuring a fleet of green aircraft is a basic step in mitigating aviation pollution issues that are expected to be worsen in the coming years due to rapid air traffic growth. This study proposed a novel methodology in green fleet planning in which both profit and green performance of airline are considered simultaneously and explicitly. To do this, a Green Fleet Index (GFI) is derived as an indicator to quantify the green performance of airline’s fleet. It measures the degree of airline compliance with a standard requirement in terms of emission, noise, and fuel consumption. A bi-objective dynamic programming model is then formulated to find optimal aircraft acquisition (lease or purchase) decision by minimizing GFI and maximizing profit. Several interesting results are obtained: (1) considering environmental issue as secondary objective yields a greener fleet; (2) airline’s profit is affected, but could be recovered from environmental cost savings; (3) increasing load factor is an effective operational improvement strategy to enhance airline’s green performance and raise profit level. It is anticipated that the framework developed in this study could assist airlines to make a smart decision when considering the need to be green.     

国际海上卫星船站系统在船—岸通信中的应用及其应用前景

国际海事卫星组织于2002年4月推出了新的国际海上卫星船站系统。该系统为船—岸通信提供了新的综合业务数字网和MPDS服务,该服务的按传输量计费使船舶能够始终在线连接到互联网,具有非常广阔的应用潜能。虽然船站系统在推出初期似乎并未如预期的那样迅速赢得市场,经过两年多在一些船上的实际应用,人们对船站系统的模糊认识正逐渐变得清晰。本文介绍了船站系统及其提供的MPDS服务,探讨了船站系统将带来的许多新应用。     

Potential of low viscosity oils to reduce CO2 emissions and fuel consumption of urban buses fleets

This paper shows the results of a comparative fleet test the main objective of which was to measure the influence of Low Viscosity Oils (LVO) over the fuel consumption and CO₂ emissions of urban buses. To perform this test, 39 urban buses, classified into candidate and reference groups depending on the engine oil viscosity, covered a 60,000 km mileage corresponding to two rounds of standard Oil Drain Interval (ODI). In the same way, for 9 buses of the 39 buses, the effect of differential LVO over fuel consumption and their interaction with engine LVO was assessed during the second ODI.Test results confirm that the use of LVO could reduce fuel consumption, hence CO₂ emissions. However, special attention should be taken prior to its implementation in a fleet, particularly if the vehicles are powered by engines with high mechanical and thermal stresses during vehicle operation because this could lead to friction loss increase, loss of the potential fuel consumption reduction of LVO and, in the worst scenario, higher rates of engine wear.     

Weekly Fleet Assignment Model and Algorithm

A 0-1 integer programming model for weekly fleet assignment was put forward based on linear network and weekly flight scheduling in China. In this model, the objective function is to maximize the total profit of fleet assignment, subject to the constraints of coverage, aircraft flow balance, fleet size, aircraft availability, aircraft usage, flight restriction, aircraft seat capacity, and stopover. Then the branch-and-bound algorithm based on special ordered set was applied to solve the model. At last, a real-world case study on an airline with 5 fleets, 48 aircrafts and 1786 flight legs indicated that the profit increase was $1591276 one week and the running time was no more than 4 min, which shows that the model and algorithm are fairly good for domestic airline.     

Social and spatial effects of transforming the private vehicle fleet in Brisbane,Australia

Transformation of the motor vehicle fleet has been an important feature of the world’s peak car phenomenon. Very few urban transport studies have explored such important changes in large urban cities. Using an innovative green vehicle datasets constructed for 2009 and 2014, this paper investigates the ongoing change in urban private vehicle fleet efficiency (VFE) in Brisbane. The spatial patterns of VFE change were examined with social-spatial characteristics of the urban area. The results showed that the social and spatial effect of VFE changes remain uneven over urban space. The inner urban areas have experienced higher level of VFE change, whilst people in the outer and oil vulnerable areas showed a low tendency in shifting to more efficient vehicles. The implication of VFE change for future household vehicle adoption was also evaluated based on a cost-benefit analysis of new vehicle technology costs and expected fuel savings for households that choose a fuel efficient vehicle. The results show that imposing a stronger national fuel economy target in the long term would accelerate evolution of vehicle fleets and oil vulnerability reduction in Brisbane.     

Optimal timetable development for community shuttle network with metro stations

This paper investigates an issue for optimizing synchronized timetable for community shuttles linked with metro service. Considering a passenger arrival distribution, the problem is formulated to optimize timetables for multiple community shuttle routes, with the objective of minimizing passenger’s schedule delay cost and transfer cost. Two constraints, i.e., vehicle capacity and fleet size, are modeled in this paper. The first constraint is treated as soft, and the latter one is handled by a proposed timetable generating method. Two algorithms are employed to solve the problem, i.e., a genetic algorithm (GA) and a Frank–Wolfe algorithm combined with a heuristic algorithm of shifting departure times (FW-SDT). FW-SDT is an algorithm specially designed for this problem. The simulated and real-life examples confirm the feasibility of the two algorithms, and demonstrate that FW-SDT outperforms GA in both accuracy and effectiveness.