Aircraft Routing Research Articles

An integrated model of aircraft routing and crew rostering problems to develop fair schedule for the crew under COVID-19 condition

This paper focuses on aircraft routing and crew rostering problems simultaneously considering the risk of COVID-19 infection. As airports are among high-risk places in COVID-19 pandemic, the crew prefer to spend less sit time in airports and come back to their home base at the end of each duty day. In this research, an integrated model is developed to assign crew and aircraft to flights in order to achieve a fair schedule for the crew. The objective function is minimisation of the difference between crew sit times. Moreover in this model, a framework including flight hours, number of days and number of take-offs is considered for maintenance requirements. Particle swarm optimisation (PSO) is used as the solution approach. To validate the solution approach, 20 test problems were solved using GAMS and PSO. The results show that PSO improved CPU time significantly (98.279% in average) in turn of 1.902% gap with GAMS in optimum solution.

An integrated model of aircraft routing and crew rostering problems to develop fair schedule for the crew under COVID-19 condition

An integrated model of aircraft routing and crew rostering problems to develop fair schedule for the crew under COVID-19 condition

A Game‐Theoretic Approach for the Robust Daily Aircraft Routing Problem

In the operation of airlines, the most important link is determining the route and scheduling of aircraft. The key to this problem is to input the flight segment and aircraft type and finally determine all flight segments for each aircraft. In this paper, we focus on finding feasible, robust scheduling for various uncertainties in the flight process. This paper presents a new robust integer mathematical model based on game theory that considers daily aircraft routing. Then, in order to find the suboptimal solution to a large‐scale integer programming problem in a limited amount of time, a heuristic algorithm integrating a column generation algorithm and variable domain search is introduced. In addition, we use the data of a Chinese airline to verify, and the experimental results show that the model proposed by us is more robust than the model in general.

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

Aircraft taxiing in large airports is often delayed by traffic conflicts. The increased fuel consumption and pollutant emission lower the efficiency of airport surface operation and bring potential safety hazards. Previous studies on route planning for surface taxiing seldom involve refined delay analysis under different traffic conflict types and discussions on route planning at airports with various environmental parameters and under diverse aircraft types. In this study, dynamic models of crossing, head-on, and trailing conflicts during aircraft taxiing were constructed, and delays of these three conflict types were determined. The shortest route set of restricted routing was determined by the Yen algorithm according to the topological network, conflict-induced delay, and spatial distributions of taxiing at airports. Considering the three optimization objects, the aircraft route planning was optimized and determined from the shortest route set according to the differences in the environmental parameters and aircraft types. Experiment results show that the proposed method achieves 9.6–12.1% higher route planning precision in all traffic periods compared with previous route planning with considerations to taxiing conflict. This method also provides support to the dynamic decision of airport operation and control departments according to the environment and performance of aircraft.

A matheuristic for the robust integrated airline fleet assignment, aircraft routing, and crew pairing problem

We address an integrated airline scheduling problem that combines three airline planning processes: fleet assignment, aircraft routing, and crew pairing. For a given daily flight schedule, the problem requires simultaneously assigning aircraft and crews to each scheduled flight, taking into account aircraft maintenance restrictions and crew work rules. We propose to solve this complex problem of integrated flight planning while taking into account robustness considerations. In this regard, robustness is achieved by restricting tight connections in the schedule and increasing the number of connections where crews follow the aircraft. We formulate the problem using a very large-scale, yet compact, mixed-integer programming model, and we propose a matheuristic consisting of a decomposition approach and a proximity search algorithm. Computational experiments carried out on real instances from a major airline and having up to 14,014 itineraries, 646 flights, and 202 aircraft provide evidence of the proposed approach’s efficacy. In particular, we find that the average deviation from a conservative bound is at most equal to 0.6%.

A Holistic Approach for Optimal Pre-Planning of Multi-Path Standardized Taxiing Routes

Standardized Taxiing Routes (STRs) are defined as published taxiing-in and taxiing-out routes for aircraft between gates and runways, aiming at improving ground movement safety at busy or complex airports. Most of the STRs specify only one path between each O–D (Origin–Destination) pair, which compromises the flexibility of route choice in time-varying traffic scenarios. In this paper, we present a holistic approach of planning and validating Multi-Path Standardized Taxiing Routes (MPSTRs) based on System-Optimal Traffic Assignment (SOTA), by firstly defining the flow-based congestion cost of runway, taxiway, and sectorized apron operation at a macroscopic level. A human-in-the-loop experiment comprised of six operation scenarios follows to investigate the impact of the pre-planned MPSTRs on human controllers’ performance. Results confirm the positive effect of the MPSTRs on taxiing performance without increasing the controllers’ workload, which also implies that the MPSTRs would be a promising approach for balancing safety and efficiency for the STRs-based taxiing operation and dynamic routing optimization without substantial investment.

Randomized greedy algorithm for helicopter optimization in the energy industry: a practical approach to model development and solution deliverance

Randomized greedy algorithm for helicopter optimization in the energy industry: a practical approach to model development and solution deliverance

Determination of airports’ atmospheric mixing height boundaries using operational data

PurposeThis study aims to determine the distance and duration to reach airports mixing height of 3,000 feet limit. Airport operations significantly contribute to the aircraft landing and take-off (LTO) cycle. Eurocontrol’s SO6 data sets comprise several abutted segment data to analyse the duration and distance for specific flights.Design/methodology/approachTwo consequential methods have been used to calculate the distance and destination from the SO6 databases. First, SQL filtering and pivot tables were formed for the required data. Second, over 583,000 data lines for a year of Boeing 747–400 aircraft routes were calculated and filtered for the monthly assessments.FindingsLTO cycles’ durations have deviated −24% to 76% from the ICAO assumptions. Distance facts determined for specific airports as 2.57 to 3.66 nm for take-off and 5.02 to 23.25 nm for the landing. The average duration of the aircraft’s in mentioned airport take-off are 66 to 74 s and 40 to 50 s; averages have been calculated as 70 to 44 s. Landing durations have been calculated for four different airports as 173 to 476 s.Practical implicationsThis study provides a re-evaluation chance for the current assumptions and helps for better assessments. Each airport and aircraft combinations have their duration and distance figures.Originality/valueThis study has calculated the first LTO distances in the literature for the aerodrome. This method applies to all airports, airline fleets and aircraft if the segmented SO6 data are available.

Two-Phase Heuristic Algorithm for Integrated Airline Fleet Assignment and Routing Problem

High profitability and high costs have stiffened competition in the airline industry. The main purpose of the study is to propose a computationally efficient algorithm for integrated fleet assignments and aircraft routing problems for a real-case hub and spoke airline planning problem. The economic concerns of airline operations have led to the need for minimising costs and increasing the ability to meet rising demands. Since fleets are the most limited and valuable assets of airline carriers, the allocation of aircraft to scheduled flights directly affects profitability/market share. The airline fleet assignment problem (AFAP) addresses the assignment of aircraft, each with a different capacity, capability, availability, and requirement, to a given flight schedule. This study proposes a mathematical model and heuristic method for solving a real-life airline fleet assignment and aircraft routing problem. We generate a set of problem instances based on real data and conduct a computational experiment to assess the performance of the proposed algorithm. The numerical study and experimental results indicate that the heuristic algorithm provides optimal solutions for the integrated fleet assignment and aircraft routing problem. Furthermore, a computational study reveals that compared with the heuristic method, solving the mathematical model takes significantly longer to execute.

Operational planning of aircraft routes when servicing a random stream of requests arriving during the flight

The process of sen*icing ground facilities with a group of aircraft is being considered when there is a flow of flight requests. The problem of parametric optimization is posed in the selection of the number of aircraft based on the multiplicative form of a single efficiency criterion. It is shown that the solution of this problem is possible with the help of the queuing theory, according to which the system under consideration belongs to the class of multi-channel queuing systems with waiting. The inventive method for developing an operational planning algorithm is based on a combination of sequentially executed ranking operations in order to ensure the lowest operational costs based on a minimax criterion in the worst conditions in terms of service delay time. A simulation program has been developed that provides an analysis of various dispatching algorithms, as well as determining at each step the number of free requests and the number of available and occupied aircraft. Along with the choice of the most effective algorithm for operational planning of aircraft routes, the task of assigning the required number of aircraft is solved.

Capacitated Location-Allocation-Routing Problem with Time Windows for On-Demand Urban Air Taxi Operation

Urban air taxi (UAT) operation has gained traction with the advancements in distributed electric propulsion and the emergence of electric vertical take-off and landing aircraft. Start-up companies and aircraft manufacturers are pursuing the possibility of operating UAT at scale in urban and suburban areas and at an affordable price. However, considerable uncertainties remain about several strategic, tactical, and operational aspects that affect UAT adoption. We envision a mature state of UAT operation in which the UAT operator offers door-to-door, multimodal, on-demand, and per-seat service. We propose the concept of flexible meeting points for UAT operation where passengers are flexible about the location of the UAT pads for boarding and deboarding, and could therefore be pooled together to share an aircraft. Consequently, we model UAT fleet operation as a capacitated location-allocation-routing problem with time windows and present a mixed integer programming formulation. The formulation addresses decisions on request acceptance and rejection, allocation of requests to flights, and aircraft routing and scheduling. Additionally, it allows for consolidating the demand to increase the aircraft’s utilization and service rate. The numerical results indicate that the demand consolidation scheme could significantly decrease the number of rejected requests and the aerial mileage. Depending on the operator’s business model, the proposed formulation could be used offline in a static and deterministic setting when all requests are known in advance, or it could be employed online by sequentially solving the static and deterministic snapshot problems with no knowledge about future requests.

Airline integrated robust scheduling with a variable neighborhood search based heuristic

This study investigates an airline integrated robust scheduling problem, subsuming three steps: schedule design, fleet assignment, and aircraft routing, while considering the effects of propagated delays. We propose a mixed integer programming model incorporating propagated delays and flight re-timing decisions; the model is extended towards a second one in terms of passenger spill/recapture effects, optional flights, and passenger miss-connections. In order to solve our models for large-scale airline instances, we propose a column generation procedure as well as a sequential variable neighborhood search (VNS) heuristic; the integration of which leads to significantly improved convergence. Based on a real airline instance, from a Chinese legacy airline, we report an extensive set of numerical experiments to validate the effectiveness and efficiency of our models and solution techniques. Our results show that the VNS-based algorithms are significantly faster than the widely applied column generation algorithm for large instances. Sensitivity analysis further illustrates the benefits of the two integrated models in terms of revenue and propagated delays.

Aircraft routing problem model for fractional fleets using fault prognostics

PurposeThe objective of this work is to provide a novel aircraft allocation model for fractional business aviation. This model may provide decision-makers with alternative routing solutions that take into consideration preventive maintenance and failure prognostics information. The expected results are more efficient routing solutions when compared to conventional planning models, to help decision-makers improve operations and maintenance planning.Design/methodology/approachThe model is a mixed integer linear problem formulation addressing and considering preventive maintenance and failure prognostics for optimal operations. Numerical experiments were performed using both field and synthetic data to validate the proposed method. All instances are solved using branch, price and cut algorithms from open-source software.FindingsThe results obtained in this study show that the use of failure prognostics information in aircraft routing can provide improvements in overall planning. By choosing slightly longer flight legs, the flight cost will increase, but putting an aircraft with a higher risk of failure on a leg inbound to a maintenance base can reduce maintenance and overall operating cost.Originality/valueThe model and method provide decision-makers with routing solutions that consider new aspects of planning, not used in previous works, such as failure. Most of the literature focuses on solving routing problems for large commercial airlines. Considering that, few solutions are found in literature for fractional business operators, which have their own operational particularities, such as a company managing a fleet of aircraft belonging to multiple shareowners. In such operation, clients may not always fly in the aircraft that they are shareowners, but an aircraft from the fractional fleet of the same category. Here, the company managing the aircraft guarantees that an aircraft will be ready to attend client demands in minimum time. One of the major differences from other models of operation is the dynamic nature of its flight demands, thus requiring flexible and agile planning limiting the available time to find a routing solution.

Numerical Characterization of Pretensioning of a Hybrid Joint Under Longitudinal Load

AbstractMost mechanical structures are composed of several components of different shape, size and material. In order to guarantee the connections among such parts is often necessary to adopt different connection mechanisms. The joints represent the structure possible weak points, can be easily site of damage and rupture so their careful design plays a decisive role. In recent years, the use of polymeric composite materials has become widespread, both in the automotive, aerospace and nautical fields. They allow advantages not only in terms of durability in aggressive environments, such as sea water or chemical industry, but also in terms of effective strength‐weight ratio. In particular, the latter translates into fuel savings and a consequent reduction in costs and pollution. It is stated a weight reduction of just 1 kg, on a mid‐range routing aircraft, can save up to 3000 L of fuel per year. The scientific community is making great efforts to develop high‐performance and reliable connections. Among the types of composite joint, the one of interest for this study is called hybrid, as it combines two connection mechanisms: bolting and bonding. Numerous studies and experimental researches, conducted on both the two separate variants and their hybrid version, the comparison of the analyses shows the hybrid one offers better results in terms of strength and fatigue life. In this paper, virtual models are built to simulate traction tests; the results are compared to experimental ones in order to validate the numerical model. In particular, the study focuses on the pretensioning influence on the joint structural behavior.

Cloud icing by mineral dust and impacts to aviation safety

Ice particles in high-altitude cold clouds can obstruct aircraft functioning. Over the last 20 years, there have been more than 150 recorded cases with engine power-loss and damage caused by tiny cloud ice crystals, which are difficult to detect with aircraft radars. Herein, we examine two aircraft accidents for which icing linked to convective weather conditions has been officially reported as the most likely reason for catastrophic consequences. We analyze whether desert mineral dust, known to be very efficient ice nuclei and present along both aircraft routes, could further augment the icing process. Using numerical simulations performed by a coupled atmosphere-dust model with an included parameterization for ice nucleation triggered by dust aerosols, we show that the predicted ice particle number sharply increases at approximate locations and times of accidents where desert dust was brought by convective circulation to the upper troposphere. We propose a new icing parameter which, unlike existing icing indices, for the first time includes in its calculation the predicted dust concentration. This study opens up the opportunity to use integrated atmospheric-dust forecasts as warnings for ice formation enhanced by mineral dust presence.

Analysis of Aircraft Routing Strategies for North Atlantic Flights by Using AirTraf 2.0

Climate-optimized routing is an operational measure to effectively reduce the climate impact of aviation with a slight increase in aircraft operating costs. This study examined variations in the flight characteristics among five aircraft routing strategies and discusses several characteristics of those routing strategies concerning typical weather conditions over the North Atlantic. The daily variability in the North Atlantic weather patterns was analyzed by using the European Center Hamburg general circulation model (ECHAM) and the Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model in the specified dynamics mode from December 2008 to August 2018. All days of the ten complete winters and summers in the simulations were classified into five weather types for winter and into three types for summer. The obtained frequency for each of the weather types was in good agreement with the literature data; and then representative days for each weather type were selected. Moreover, a total of 103 North Atlantic flights of an Airbus A330 aircraft were simulated with five aircraft routing strategies for each representative day by using the EMAC model with the air traffic simulation submodel AirTraf. For every weather type, climate-optimized routing shows the lowest climate impact, at which a trade-off exists between the operating costs and the climate impact. Cost-optimized routing lies between the time- and fuel-optimized routings and achieves the lowest operating costs by taking the best compromise between flight time and fuel use. The aircraft routing for contrail avoidance shows the second lowest climate impact; however, this routing causes extra operating costs. Our methodology could be extended to statistical analysis based on long-term simulations to clarify the relationship between the aircraft routing characteristics and weather conditions.

The Tail Assignment Problem: A Case Study at Vueling Airlines

To schedule aircraft, assignments of fleet types to flights and of aircraft to routes must be determined. The former is known as the fleet assignment problem while the latter is known as the aircraft routing problem in the literature. Aircraft routing is usually addressed as a feasibility problem whose solution is needed for constructing crew schedules. These problems are usually solved from 4 to 6 months before the day of operations. Therefore, there is limited information regarding each aircraft’s operational condition. The tail assignment problem, which has received limited attention in air transportation literature, is solved when additional information regarding operational conditions is revealed aiming at determining each aircraft’s route for the day of operations accounting for the originally planned aircraft routes and crew schedules. Therefore, it is a problem to be solved the day before operations. We propose a mathematical programming approach based on sequencing that captures all operational constraints and maintenance requirements while assignment costs are minimized. Computational experiments are based on realistic cases drawn from a Spanish airline, which features a network with more than 1000 flights and more than 100 aircraft.

A Novel Approach to the Tail Assignment Problem in Airline Planning

Combinatorial optimization problems abound in the field of airline planning. Aircraft and passengers fly on networks made up of flights and airports. To schedule aircraft, assignments of fleet types to flights and of aircraft to routes must be determined. The former is known as the fleet assignment problem while the latter is known as the aircraft routing problem in the literature. Aircraft routing is typically addressed as a feasibility problem, the solution to which is required for the construction of crew schedules. All these issues are typically resolved 4 to 6 months before the day of operations. As a result, there is little information available about each aircraft’s operational status when making such decisions. The tail assignment problem is solved when additional information about operational conditions is revealed, with the goal of determining each aircraft’s route for the day of operations while accounting for the planned aircraft routes and crew schedules. It is a problem that must be resolved close to the day of operations. We propose a mathematical programming approach that captures all operational constraints and maintenance requirements while minimizing operational costs and schedule changes relative to original plans. The computational experiments are based on realistic cases drawn from a Spanish airline with over 1000 flights and over 100 aircraft.

A new approach to fleet assignment and aircraft routing problems

In this study, a new mathematical formulation is developed for fleet scheduling problems (i.e., the combination of fleet assignment and aircraft routing problems) in single hub & spoke systems. The proposed model aims to minimize the total cost of allocating aircraft to flights while observing the sequence of flights to be subject to seat capacity, passenger demand, aircraft availability, and overlapping flights. Since the classical fleet scheduling problem is NP-hard, a couple of complexity reduction techniques are developed. Thus, the size of the problem is decreased significantly. To evaluate the model’s performance, it is implemented to an airline carrier with 170 aircraft and 1290 (645 round trip) flights. As a result, the proposed method ended up with an improvement of 36.7% for a 2-day planning horizon.

Research on the Availability of VINS-Mono and ORB-SLAM3 Algorithms for Aviation

Navigation, which most people have an idea about, is often known as information about how to get from one point to another on the land. Navigation is an interest area for both civil and military aviation. Therefore, we get help from various sensors in order to trace the aircraft route. The most used sensor among them is GPS. It is a sensor that can be out of use although it has high accuracy rates. This research focused on being able to maintain the navigation of an aircraft in the environments where GPS is out of use. There are two visual-inertial navigation systems VINS-Mono and ORB-SLAM3, which are the best known algorithms in the literature, were examined and compared in terms of performance. It has been observed that ORB-SLAM3 outperformed almost twice the VINS-Mono system in various situations.