scholarly journals A Variation of the ATC Work Shift Scheduling Problem to Deal with Incidents at Airport Control Centers

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 321 ◽  
Author(s):  
Antonio Jiménez-Martín ◽  
Faustino Tello ◽  
Alfonso Mateos
Keyword(s):  
Traffic Management ◽  
Air Traffic ◽  
Neighborhood Search ◽  
Second Phase ◽  
Scheduling Problem ◽  
Shift Scheduling ◽  
Control Center ◽  
Traffic Demand ◽  
Work Shift ◽  
Real Time Traffic

This paper deals with a variation of the air traffic controller (ATC) work shift scheduling problem focusing on the tactical phase, in which the plan for the day of operations can be modified according to real-time traffic demand or other possible incidents (one or more ATCs become sick and/or there is an increase in unplanned air traffic), which may lead to a new sectorization and a lower number of available ATCs. To deal with these issues, we must reassign the available ATCs to the new sectorization established at the time the incident happens, but also taking into account the work done by the ATCs up to that point. We propose a new methodology consisting of two phases. The goal of the first phase is to build an initial possibly infeasible solution, taking into account the sectors that have been closed or opened in the new sectorization, together with the ATCs available after the incident. In the second phase, we use simulated annealing (SA) and variable neighborhood search (VNS) metaheuristics to derive a feasible solution in which the available ATCs are used and all the ATC labor conditions are met. A weighted additive objective function is used in this phase to account for the feasibility of the solution but also for the number of changes in the control center at the time the incident happens and the similarity of the derived solution with templates usually used by the network manager operations center, a center managing the air traffic flows of an entire network of control centers. The methodology is illustrated by means of seven real instances provided by the Air Traffic Management Research, Development and Innovation Reference Center (CRIDA) experts representing possible incidents that may arise. The solutions derived by SA outperform those reached by VNS in terms of both the number of violated constraints in all seven instances, and solution compactability in six out the seven instances, and both are very similar with regard to the number of control center changes at the time of the incident. Although computation times for VNS are clearly better than for SA, CRIDA experts were satisfied with SA computation times. The solutions reached by SA were preferred.

scholarly journals The Air Traffic Controller Work-Shift Scheduling Problem in Spain from a Multiobjective Perspective: A Metaheuristic and Regular Expression-Based Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Faustino Tello ◽  
Alfonso Mateos ◽  
Antonio Jiménez-Martín ◽  
Adán Suárez
Keyword(s):  
Simulated Annealing ◽  
Rank Order ◽  
Optimal Solution ◽  
Air Traffic ◽  
Second Phase ◽  
Scheduling Problem ◽  
Shift Scheduling ◽  
Work Shift ◽  
Labor Conditions ◽  
Feasible Solutions

We address an air traffic control operator (ATCo) work-shift scheduling problem. We consider a multiple objective perspective where the number of ATCos is fixed in advance and a set of ATCo labor conditions have to be satisfied. The objectives deal with the ATCo work and rest periods and positions, the structure of the solution, the number of control center changes, or the distribution of the ATCo workloads. We propose a three-phase problem-solving methodology. In the first phase, a heuristic is used to derive infeasible initial solutions on the basis of templates. Then, a multiple independent run of the simulated annealing metaheuristic is conducted aimed at reaching feasible solutions in the second phase. Finally, a multiple independent simulated annealing run is again conducted from the initial feasible solutions to optimize the objective functions. To do this, we transform the multiple to single optimization problem by using the rank-order centroid function. In the search processes in phases 2 and 3, we use regular expressions to check the ATCo labor conditions in the visited solutions. This provides high testing speed. The proposed approach is illustrated using a real example, and the optimal solution which is reached outperforms an existing template-based reference solution.

scholarly journals A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 636 ◽  
Author(s):  
Faustino Tello ◽  
Antonio Jiménez-Martín ◽  
Alfonso Mateos ◽  
Pablo Lozano
Keyword(s):  
Simulated Annealing ◽  
Solution Structure ◽  
Variable Neighborhood Search ◽  
Fitness Function ◽  
Phase 1 ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Shift Scheduling ◽  
Work Shift ◽  
Starting Point

This paper deals with the air traffic controller (ATCo) work shift scheduling problem. This is a multi-objective optimization problem, as it involves identifying the best possible distribution of ATCo work and rest periods and positions, ATCo workload and control center changes in order to cover an airspace sector configuration, while, at the same time, complying with ATCo working conditions. We propose a three-phase problem-solving methodology based on the variable neighborhood search (VNS) to tackle this problem. The solution structure should resemble the previous template-based solution. Initial infeasible solutions are built using a template-based heuristic in Phase 1. Then, VNS is conducted in Phase 2 in order to arrive at a feasible solution. This constitutes the starting point of a new search process carried out in Phase 3 to derive an optimal solution based on a weighted sum fitness function. We analyzed the performance in the proposed methodology of VNS against simulated annealing, as well as the use of regular expressions compared with the implementation in the code to verify the feasibility of the analyzed solutions, taking into account four representative and complex instances of the problem corresponding to different airspace sectorings.

scholarly journals Relationship between air traffic demand, safety and complexity in high density airspace in Europe

2020 ◽  
Vol 314 ◽  
pp. 01004
Author(s):  
Tamara Pejovic ◽  
Fedja Natjasov ◽  
Dusan Crnogorac
Keyword(s):  
Traffic Management ◽  
Flow Characteristics ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Traffic Demand ◽  
Traffic System ◽  
Traffic Distribution ◽  
Traffic Performance ◽  
Aircraft Type ◽  
Conflict Risk

Air traffic performance of the European air traffic system depends not only on traffic demand but also on airspace structure and its traffic distribution. These structural (airspace structure) and flow characteristics (factors such as traffic volume, climbing/descending traffic, mix of aircraft type, military area activity) influence airspace complexity, which can affect controller workload and influence the probability of safety occurrence. In other words, all these dynamic and static complexity components can potentially have an impact upon the safety of the air traffic management (ATM) system. Having in mind fluctuation in traffic on daily, seasonal or annual level in certain airspace, a few questions arise: How changes in traffic demand influence complexity and conflict risk? Is there any correlation between traffic demand, conflict risk and complexity? Are there any differences between seasons? For that purpose, an investigation is performed on FAB Europe Central (FABEC) airspace, based on two weeks of operated traffic during the summer and winter of 2017. Air traffic complexity is estimated using the EUROCONTROL complexity metrics, while conflict risk is assessed using the conflict risk assessment simulation tool. Results show that certain positive relationship exists between traffic demand, conflict risk and complexity.

Effects of Air Traffic Congestion Delays under Several Flow-Management Policies

1996 ◽  
Vol 1517 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Gregory D. Glockner
Keyword(s):  
Optimization Model ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Air Traffic ◽  
Practical Implementation ◽  
Model Framework ◽  
Flow Management ◽  
Traffic Demand ◽  
Airport Capacity ◽  
Facility Construction

Air traffic delays occur when demand for airports or airspace exceeds available capacity. Consequently, these delay effects can be lessened by increasing capacity or by modifying the air traffic demand. Increasing capacity is an important solution, but it is a long-range option involving major changes such as facility construction, fundamental procedural changes, and improvements in navigational equipment. For short-term decision making a tactical-optimization model can suggest alternative flight plans to reduce delays. However, a tactical-optimization model is extremely complex because of the uncertainty in airport-capacity forecasts, which primarily depend on weather. A practical implementation of a tactical-optimization model must therefore make approximations so that a solution may be computed quickly and be of good quality. A practical model framework for the congestion-delay problem is given; this model framework is a generalization of several other flow-management models. Congested situations are simulated, to compare the practical performance of this model to other air traffic management tactics.

A regression model for terminal airspace delays

2017 ◽  
Vol 121 (1239) ◽  
pp. 680-692 ◽  
Author(s):  
F. Aybek Çetek ◽  
Y.M. Kantar ◽  
A. Cavcar
Keyword(s):  
Regression Model ◽  
Traffic Flow ◽  
Traffic Management ◽  
Air Traffic ◽  
Fast Time ◽  
Total Delay ◽  
Traffic Demand ◽  
Flight Operations ◽  
Entry Points ◽  
Air Traffic Flow

ABSTRACTAir Traffic Management (ATM) research generally focuses on achieving a safer, more effective and economical air traffic system. The current airspace system has become increasingly strained as the demand for air travel has steadily grown. Innovative, proactive and multi-disciplinary approaches to research are needed to solve flight congestion and delays as a consequence of this rapid growth. As a result of this growth, air traffic flow becomes more complex, especially in Terminal Airspaces (TMA) where climb and descent manoeuvres of departing and arriving flights take place around airports. As air traffic demand exceeds the capacity in a TMA, the resultant congestion leads to delays that spread all over the system. Therefore, the reduction of delays is critical for airspace designers to increase customer satisfaction and the perception of service quality. Numerous studies have been conducted to reduce delays within TMAs. This research focuses on defining the causes of delays quantitatively through statistical analysis. The first step was to create a fast-time simulation model of sample airspace for collecting delay data. After building up this model using the SIMMOD fast-time ATM simulation tool, simulation experiments were run to produce various traffic scenarios and to generate traffic delay data. The number of airports, entry points, fixes and flight operations in airspace and the probability of wide-body aircraft were considered as independent variables. The correlations between the considered variables were analysed, and the total delay data was modelled using a linear regression model. The findings of regression model present a statistical approach for airspace designers and air traffic flow planners.

Efficiency-Thoroughness Trade-Off Characteristics of Air Traffic Controllers During Tower Operations

2020 ◽  
Vol 64 (1) ◽  
pp. 1745-1749
Author(s):  
Tsubasa Takagi ◽  
Miwa Nakanishi
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Autonomous Systems ◽  
Air Traffic ◽  
Trade Off ◽  
Traffic Demand ◽  
Level Of Automation ◽  
Proactive Management ◽  
Air Traffic Controllers ◽  
Balancing Efficiency

Air traffic demand has been growing for years and many countries are trying to solve this situation by modernizing their national airspace through advanced automation. Those projects aim to provide a greater level of efficiency while ensuring a safe flow of air traffic. However, human air traffic controllers have been coping with difficult situations and unexpected events by flexibly balancing efficiency and thoroughness throughout their operations. In this study, we conducted an experiment using a simulator depicting air traffic control tower operations and analyzed the trade-off decisions made by humans under varying situations. By doing so, we proposed a model with the aim of applying the results to future air traffic management systems. This could allow those autonomous systems to make decisions that are similar to those of human preferences, which could lead to a proactive management of safety and a higher level of automation acceptance.

Scheduling Interval Job Shop with Flexible Preventive Maintenance Using Two-Phase Neighborhood Search

2014 ◽  
Vol 926-930 ◽  
pp. 1601-1609
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Preventive Maintenance ◽  
Job Shop ◽  
Neighborhood Search ◽  
Second Phase ◽  
Scheduling Problem ◽  
Computational Results ◽  
Dynamical Transition ◽  
Two Phase ◽  
Transition Mechanism ◽  
Neighborhood Structures

This paper addresses the scheduling problem in interval job shop with flexible preventive maintenance and proposes a two-phase neighborhood search (TPNS) for minimizing interval makespan. A small group of solutions are evolved independently in the first phase and only one solution is used in the second phase. New solutions are produced by using three neighborhood structures and their dynamical transition mechanism. TPNS is tested and compared with some methods from literature. Computational results show that TPNS is an effective approach to solve the considered problem.

Workload-Based Capacity for Air Traffic Management

2015 ◽  
Vol 2501 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Kenneth S. Lindsay
Keyword(s):  
Traffic Management ◽  
System Development ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Time On Task ◽  
National Airspace System ◽  
Traffic Demand ◽  
Workload Capacity ◽  
Convective Weather ◽  
Parameter Values

The charter of FAA is to promote the safe, orderly, and expeditious use of the National Airspace System (NAS). To ensure that traffic flow is safe and efficient, FAA needs to know the expected traffic demand on the sector and the sector's capacity to accommodate that demand. When sector capacity is inadequate to meet the demand, congestion occurs. To ensure that safety is not compromised, FAA often takes action to reduce demand or increase capacity to avoid congestion. The MITRE Corporation's Center for Advanced Aviation System Development developed a time-on-task workload model to assess capacity and congestion in en route NAS sectors. A metric was developed and used along with the workload generated by the model and a workload threshold to estimate sector capacity. The metric, as constructed, enabled equitable comparison of capacity of different sectors, regardless of size. A field and lab evaluation of the workload model was used to quantify the model's task coverage and to calibrate its parameter values. The workload model was used to generate workload, capacity, and congestion profiles for selected en route sectors during good weather and during convective weather. The data used to generate the profiles can be used for various air traffic management applications.

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