Schedule Perturbations Research Articles

A reinforcement learning approach for multi-fleet aircraft recovery under airline disruption

An airline scheduler plans flight schedules with efficient resource utilization. However, unpredictable airline disruptions, such as temporary closures of an airports, cause schedule perturbations. Therefore, recovering disrupted flight schedules is essential for airlines. Many previous studies have relied on copies of flight arcs, which could affect the quality of solutions, and have not addressed the key measure of airlines’ on-time performance as their objective. To fill these research gaps, we propose Q-learning and Double Q-learning algorithms using the reinforcement learning approach for aircraft recovery to support airline operations. We present an artificial environment of daily flight schedules and the Markov decision process for aircraft recovery. The proposed approach is first compared with existing algorithms on the benchmark instance. In comparison with other algorithms, the developed Q-learning and Double Q-learning algorithms obtain high-quality solutions within the proper computation time. To verify that the proposed approach can be applicable to a real-world case and can adapt to realistic conditions, we employ a domestic flight schedule from one of the airlines in South Korea. We evaluate the reinforcement learning approach on a set of experiments carried out on real-world data. Computational experiments show that reinforcement learning algorithms recover disrupted flight schedules effectively, and that our approaches flexibly adapt to various objectives and realistic conditions.

EPEC 4.0: an Industry 4.0-supported lean production control concept for the semi-process industry

The combination of lean production and Industry 4.0 exhibits excellent potential in advancing operations management and, especially, production control. This improvement potential is applicable to the semi-process industry (S-PI), as higher product variety and higher demand variability require greater flexibility from production systems. This flexibility is difficult to achieve in S-PI environments, in which it is imperative to cope with the long setup times and stability needed for the high utilization requirements. Heijunka addresses this stability-flexibility paradox but was designed for short setup times and is thus considered not directly applicable. In this article, we use design science research to adapt Heijunka to the S-PI and combine it with cyber-physical systems technologies. The developed production control concept, every product every cycle (EPEC) 4.0, aims to schedule the production system as efficiently as possible, providing the necessary flexibility and minimum schedule perturbation. Furthermore, we present the findings from a validation case study within the automotive supply industry.

Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network

Here we study a short-sea feeder network design problem based on mother and daughter vessels. The main feature of the studied system is performing transshipment of cargo between mother and daughter vessels at appropriate locations at sea. This operation requires synchronization between both types of vessels as they have to meet at the same location at the same time. This paper studies the problem of designing a synchronized feeder network, explicitly accounting for the effect of uncertain travel times caused by harsh weather conditions. We propose an optimization-simulation framework to find robust solutions for the transportation system. The optimization model finds optimal routes that are then evaluated by a discrete-even simulation model to measure their robustness under uncertain weather conditions. This process of optimization simulation is repeated until a satisfactory condition is reached. To find even better solutions, we include different performance-improving strategies by adding robustness during route generation or exploiting flexibility in sailing speed to recover from delays. We apply the solution method to a case based on realistic data from a Norwegian shipping company. The results show that the method finds near-optimal solutions that offer robustness against schedule perturbations due to harsh weather. They also highlight the importance of considering uncertainty when designing a short-sea feeder network with transshipment at sea.

A multi-objective optimization with a delay-aware component for airport stand allocation

Airport management is regularly challenged by the task of assigning flights to existing parking positions in the most efficient way while complying with existing policies, restrictions and capacity limitations. However, such process is frequently disrupted by various events, affecting punctuality of airline operations. This paper describes an innovative approach for obtaining an efficient stand assignment considering the stochastic nature of airport environment. Furthermore, the presented methodology combines benefits of Bayesian modelling and metaheuristics for generating solutions that are more robust to airport flight schedule perturbations. In addition, this paper illustrates that the application of the presented methodology combined with simulation provides a valuable tool for assessing the robustness of the developed stand assignment to flight delays.

Maximization of the sensor network lifetime by activity schedule heuristic optimization

Wireless Sensor Networks (WSNs) consist of a set of devices with limited energy capacity, therefore the longevity of the network can be one of the decisive quality parameters of WSNs. We study the problem of WSN lifetime maximization for a model of the network where sensors are randomly deployed over an area to keep watch on a number of points of interest (POI). Although sensors have limited battery capacity, their number is large, and their monitoring ranges overlap. Therefore, not all the sensors have to be active all the time. Moreover, for effective monitoring, it is enough to observe all the time just a required percentage of POIs, not all of them. Our goal is to create a schedule of sensor activity giving the longest possible time of effective monitoring of a given set of POIs. Here, we present three heuristic algorithms for sensors activity scheduling: a random and fine-tuning approach, an approach inspired by cellular automata, and a hypergraph model approach. Since the outcome of these algorithms does not represent optimal solutions and can be further improved, we use the obtained schedules as an input for a local search strategy with problem-specific neighborhood functions. Three versions of schedule perturbation operators are presented. We also propose a suite of benchmark test cases for experimental evaluation of our algorithms and present the results of experiments with heuristic algorithms and local search methods.

Some effects of perturbed flight schedules to the the performance of aircraft routings

In this paper, we observe some effects of schedule perturbations to the performance of aircraft routings. We show that moving departure time of flights to adding their block-time do not guarantee that the departure delay of the flights will be reduced. Contrarily, moving arrival time of flights always improve the performance of aircraft routings, especially for reducing arrival delay. The computational results also show that by adding more slacks, we obtain better performance of aircraft routings.

The daily tail assignment problem under operational uncertainty using look-ahead maintenance constraints

The tail assignment problem is a critical part of the airline planning process that assigns specific aircraft to sequences of flights, called lines-of-flight, to satisfy operational constraints. The aim of this paper is to develop an operationally flexible method, based upon the one-day routes business model, to compute tail assignments that satisfy short-range—within the next three days—aircraft maintenance requirements. While maintenance plans commonly span multiple days, the methods used to compute tail assignments for the given plans can be overly complex and provide little recourse in the event of schedule perturbations. The presented approach addresses operational uncertainty by using solutions from the one-day routes aircraft maintenance routing approach as input. The daily tail assignment problem is solved with an objective to satisfy maintenance requirements explicitly for the current day and implicitly for the subsequent two days. A computational study will be performed to assess the performance of exact and heuristic solution algorithms that modify the input lines-of-flight to reduce maintenance misalignments. The daily tail assignment problem and the developed algorithms are demonstrated to compute solutions that effectively satisfy maintenance requirements when evaluated using input data collected from three different airlines.

Efficient calculation of the makespan for job-shop systems without recirculation using max-plus algebra

In this paper, max-plus algebraic techniques are used to develop an efficient method to calculate the makespan of a perturbed job-shop manufacturing system. The methodology assumes that the perturbations in schedule are confined to a particular sub-system which is termed the variant and the rest of the system is called the invariant . The paper further develops a very efficient algorithm that calculates the makespan, for a given schedule, of a system when consists of only a single machine. Recirculation of jobs is not allowed in the system. In other words, the arrival times of the parts for are not dependent on the departure times of these parts. The complexity of the algorithm is O(N), where N is the number of jobs. It is interesting to note that the model for the sub-system contains only two vectors and a scalar regardless of its size and complexity.

Recoverable robust single day aircraft maintenance routing problem

Aircraft maintenance planning is of critical importance to the safe and efficient operations of an airline. It is common to solve the aircraft routing and maintenance planning problems many months in advance, with the solution spanning multiple days. An unfortunate consequence of this approach is the possible infeasibility of the maintenance plan due to frequent perturbations occurring in operations. There is an emerging concept that focuses on the generation of aircraft routes for a single day to ensure maintenance coverage that night, alleviating the effects of schedule perturbations from preceding days. In this paper, we present a novel approach to ensure that a sufficient number of aircraft routes are provided each day so maintenance critical aircraft receive maintenance that night. By penalising the under supply of routes terminating at maintenance stations from each overnight airport, we construct a single day routing to provide the best possible maintenance plan. This single day aircraft maintenance routing problem (SDAMRP) is further protected from disruptions by applying the recoverable robustness framework. To efficiently solve the recoverable robust SDAMRP acceleration techniques, such as identifying Pareto-optimal cuts and a trust region approach, have been applied. The SDAMRP is evaluated against a set of flight schedules and the results demonstrate a significantly improved aircraft maintenance plan. Further, the results demonstrate the magnitude of recoverability improvement that is achieved by employing recoverable robustness to the SDAMRP.

An Optimization Approach to Airline Integrated Recovery

Although the airline industry has benefited from advancements made in computational and operational research methods, most implementations arise from the frictionless environment of the planning stage. Because 22% of all flights have been delayed and 3% have been cancelled in the United States since 2001, schedule perturbations are inevitable. The complexity of the operational environment is exacerbated by the need for obtaining a solution in as close to real-time as possible. Given some time horizon, the recovery process seeks to repair the flight schedule, aircraft rotations, crew schedule, and passenger itineraries in a tractable manner. Each component individually can be difficult to solve, so early research on irregular operations has studied these problems in isolation, leading to a sequential process by which the recovery process is conducted. Recent work has integrated a subset of these four components, usually abstracting from crew recovery. We present an optimization-based approach to solve the fully integrated airline recovery problem. After our solution methodology is presented, it is tested using data from an actual U.S. carrier with a dense hub-and-spoke network using a single-day horizon. It is shown that in several instances an integrated solution is delivered in a reasonable runtime. Moreover, we show the integrated approach can substantially improve the solution quality over the incumbent sequential approach. To the best of our knowledge, we are the first to present computational results on the fully integrated problem.

Global Stabilization of Feedforward Systems Under Perturbations in Sampling Schedule

For nonlinear systems that are known to be globally asymptotically stabilizable, control over networks introduces a major challenge because of the asynchrony in the transmission schedule. Maintaining global asymptotic stabilization in sampled-data implementations with zero-order hold and with perturbations in the sampling schedule is not achievable in general, but we show in this paper that it is achievable for the class of feedforward systems. We develop sampled-data feedback stabilizers which are not approximations of continuous-time designs but are discontinuous feedback laws that are specifically developed for maintaining global asymptotic stabilizability under any sequence of sampling periods that is uniformly bounded by a certain „maximum allowable sampling period.”

Real-Time Intermodal Substitution

A strategy for airline recovery from schedule perturbation caused by adverse weather or other temporary events is proposed. Capacity reduction at a hub airport caused by these temporary events is reflected as reduced slots (or later controlled time of arrivals) for airlines from ground delay programs. It is suggested that under severe circumstances legacy airlines with hub-and-spoke networks implement ground transportation into their network to substitute for flights. This strategy is called real-time intermodal substitution (RTIMS). RTIMS is different from air-rail cooperation practiced in Europe in that it is triggered only by a severe demand and supply imbalance at major hub airports and it performs operational integration of airside and ground transportation. Mathematical programming is presented to help airlines make decisions about if and how to delay, canceling flights, or substituting flights with buses. An approximation algorithm is proposed to obtain solutions, avoiding substantial computation time required to solve large-scale nonlinear integer programming. As an evaluation, experimental data are used to compare scenarios with and without tactical intermodal substitution.

Improving airline operational performance through schedule perturbation

Schedule development is typically the first phase of the airline planning process. We present a framework for perturbing scheduled departure and arrival times after a crew schedule has been found. We characterize perturbations that keep a schedule legal while not increasing the planned cost of the crew schedule. We show that when random delays occur in operations, the expected cost can be reduced and the on-time performance improved. Computational results are reported for two real fleets and a large number of crew schedules.

How Airlines and Airports Recover from Schedule Perturbations: A Survey

The explosive growth in air traffic as well as the widespread adoption of Operations Research techniques in airline scheduling has given rise to tight flight schedules at major airports. An undesirable consequence of this is that a minor incident such as a delay in the arrival of a small number of flights can result in a chain reaction of events involving several flights and airports, causing disruption throughout the system. This paper reviews recent literature in the area of recovery from schedule disruptions. First we review how disturbances at a given airport could be handled, including the effects of runways and fixes. Then we study the papers on recovery from airline schedule perturbations, which involve adjustments in flight schedules, aircraft, and crew. The mathematical programming techniques used in ground holding are covered in some detail. We conclude the review with suggestions on how singular perturbation theory could play a role in analyzing disruptions to such highly sensitive schedules as those in the civil aviation industry.

A SELF-ORGANIZING MODEL FOR SCHEDULING DISTRIBUTED AUTONOMOUS MANUFACTURING SYSTEMS

This paper proposes an approach to scheduling distributed autonomous manufacturing entities founded on the principles of self-organization. In this approach, called the behavior-based approach, the individual entities are endowed with adaptable scheduling behaviors and the ability to cooperate with each other. The individual adaptation helps a unit to deal with schedule perturbations from the environment. More important, it indirectly contributes to the community-level performance, such as to produce quality schedules jointly, reduce uncontrollable perturbations, and maintain stable allocations. This model has been tested for a variety of production situations and the results are promising. In this paper, we describe the underlying concepts of the model and outline the results of the experiments.

OR software

The tail assignment problem is a critical part of the airline planning process that assigns specific aircraft to sequences of flights, called lines-of-flight, to satisfy operational constraints. The aim of this paper is to develop an operationally flexible method, based upon the one-day routes business model, to compute tail assignments that satisfy short-range—within the next three days—aircraft maintenance requirements. While maintenance plans commonly span multiple days, the methods used to compute tail assignments for the given plans can be overly complex and provide little recourse in the event of schedule perturbations. The presented approach addresses operational uncertainty by using solutions from the one-day routes aircraft maintenance routing approach as input. The daily tail assignment problem is solved with an objective to satisfy maintenance requirements explicitly for the current day and implicitly for the subsequent two days. A computational study will be performed to assess the performance of exact and heuristic solution algorithms that modify the input lines-of-flight to reduce maintenance misalignments. The daily tail assignment problem and the developed algorithms are demonstrated to compute solutions that effectively satisfy maintenance requirements when evaluated using input data collected from three different airlines.

Multifleet routing and multistop flight scheduling for schedule perturbation

Efficient and effective incidental scheduling techniques for schedule perturbation are essential to an airline carrier's operations. This research aims at developing a framework to assist carriers in fleet routing and flight scheduling for schedule perturbations in the operations of multifleet and multistop flights. The framework is based on a basic multifleet schedule perturbation model constructed as a timespace network from which strategic models are developed to research incidental scheduling. These network models are formulated as multiple commodity network flow problems. Lagrangian relaxation with subgradient methods accompanied by the network simplex method, a Lagrangian heuristic and a modified subgradient method are developed to solve the problems. A case study regarding the international operations of a major Taiwan airline carrier is presented.

On the Airline Schedule Perturbation Problem Caused by the Ground Delay Program

The ground delay program is one of several programs that the Federal Aviation Administration is currently administering for efficient and equitable use of scarce airspace and airport capacity. In this paper, we study airline schedule perturbation problem caused by the ground delay program with the goal of improving airline dependability statistics defined by Department of Transportation as percentage of flights delayed more than 15 minutes. The problem is modeled as an integer program. To solve the model, we have derived valid inequalities for the integer programming formulation for strengthening the LP relaxation bound. The number of integer variables was reduced dramatically based on analysis of the model and its impact on the problem solubility was shown to be of significant importance. A heuristic procedure based on solving a restricted version of the model has been designed for finding good feasible solutions. Computational results indicate the effectiveness of the model reduction and the valid inequalities. Realistic problems have been solved to optimality within seconds on microcomputers. Improvement on airline dependability statistics has been shown to be substantial.

Airline Scheduling for the Temporary Closure of Airports

The poor scheduling of flights or a fleet resulting from temporary closure of airports may cause a substantial loss of profit and decreased levels of service for airline carriers. This research develops a framework in order to help carriers handle schedule perturbations resulting from the temporary closure of airports. The framework is based on a basic model constructed as a time-space network from which several strategic network models are developed for scheduling. These network models are formulated as pure network flow problems or network flow problems with side constraints. The former are solved using the network simplex method whereas the latter are solved using a Lagrangian relaxation-based algorithm. To test how well the model may be applied in the real world, a case study regarding the international operations of a major Taiwan airline was performed. The results show that the framework could be useful in actual operations.

A decision support framework for handling schedule perturbation

Perturbations of flight schedules may occur everyday. Poor scheduling of flights may result in a substantial loss of profit and decreased levels of service for air carriers. This research aims at developing a framework to help carriers in handling schedule perturbations caused by the breakdown of aircraft. The framework is based on a basic schedule perturbation model constructed as a dynamic network from which several perturbed network models are developed for scheduling following incidents. These network models are formulated as pure network flow problems or network flow problems with side constraints. The former are solved using the network simplex method while the latter are solved using Lagrangian relaxation with subgradient methods. To show how to apply the framework in the real world, a case study focusing on the operations of a major Taiwan air carrier in East Asia is presented.