Using heuristics to solve the dedicated aircraft recovery problem (original) (raw)
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Aircraft schedule recovery problem – a dynamic modeling framework for daily operations
EWGT, 2015
In this paper we present an innovative dynamic modeling framework to the aircraft schedule recovery problem (ASRP). The ASRP can be defined as the problem of modifying the flight and aircraft schedules to compensate the presence of irregular operations that result in the temporary or permanent unavailability of aircraft. Previous works on this topic often make use of static disruption test scenarios, simulating a set of disrupted events in a single time evaluation. The modeling framework here presented, named Disruption Set Solver (DSS), is innovative because it tackles aircraft schedule disruptions in a dynamic way (i.e., the recovery problem is solved as disruptions happen, involving the solutions of new disruption but also considering decision the incumbent solution) and because it is the first time that parallel time-space networks are used to track individual aircraft in the fleet. The framework relies on the combined usage of an efficient aircraft selection algorithm and a linear-programming model based on parallel aircraft specific time-space networks. The goal of the optimization model used to solve the ASRP is to minimize costs, including operational, passengers delay and cancellation costs. The decision variables involve the cancellation of flights, the delay of flights and the swap of aircraft between flights. The validation of the framework is done applying it to a set of real disruptive days in the operation of a major African airline. The results suggest two conclusions: (1) that the traditional static approach can lead to unreliable solutions, neglecting practical challenge and underestimating the disruption costs; and (2) that the proposed dynamic DSS framework can solve real aircraft schedule disruption problems within a time-window suitable for real-time operations.
Operational Problems Recovery in Airlines – A Specialized Methodologies Approach
Disruption management is one of the most important scheduling problems in the airline industry because of the elevated costs associated, however this is relatively new research area comparing for example with fleet and tail assignment. The major goal to solve this kind of problem is to achieve a feasible solution for the airline company minimizing the several costs involved and within time constraints. An approach to solve operational problems causing disruptions is presented using different specialized methodologies for the problems with aircrafts and crewmembers including flight graph based with meta-heuristic optimization algorithms. These approaches were built to fit on a multi-agent system with specialist agents solving disruptions. A comparative analysis of the algorithms is also presented. Using a complete month real dataset we demonstrate an example how the system handled disruption events. The resulting application is able to solve disruption events optimizing costs and respecting operational constraints.
2013 8th EUROSIM Congress on Modelling and Simulation, 2013
The Aircraft Recovery Problem appears when external events cause disruptions in a flight schedule. Thus in order to minimize the losses caused by the externalities, aircrafts must be reallocated (rescheduled) in the best possible way. The aim of this paper is to develop a suitable methodology that combines optimization techniques with a simulation approach to tackle the so-called Stochastic Aircraft Recovery Problem. The approach solves the problem through the rescheduling of the flight plan using delays, swaps, and cancellations. The main objective of the optimization model is to restore as much as possible the original flight schedule, minimizing the total delay and the number of cancelled flights. By applying simulation techniques, the robustness of the given solution is assessed. The proposed methodology is applied on a medium-sized scenario based on real data provided by a commercial airline. The obtained results show that the methodology described in the paper is capable of producing a feasible and robust solution for this problem.
The Aircraft Recovery Problem (ARP) appears when external events cause disruptions in a flight schedule. Thus in order to minimize the losses caused by the externalities, aircraft must be reallocated (rescheduled) in the best possible way. If uncertain conditions are taken into account the Stochastic Aircraft Recovery Problem (SARP) arises. The aim of this paper is to develop a suitable approach based on Constraint Programming paradigm and using simulation to solve this socalled SARP . The approach solves the problem through the rescheduling of the flight plan using delays and swaps. The main objective is to restore as much as possible the original flight schedule, minimizing the total delay. Several tests have been carried out on medium-sized scenarios to assess the accuracy of the solutions provided by our approach.
Disruption management in the airline industry--Concepts, models and methods
2010
This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery. An overview of model formulations of the aircraft and crew scheduling problems is presented in order to emphasize similarities between solution approaches applied to the planning and recovery problems.
OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery
2009
The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems. These problems have several challenging characteristics, the two most challenging of which include: 1) they span long- and short-term horizons, from strategic planning of flight schedules operated several months into the future, to real-time operations in which strategies must be developed and implemented immediately to recover scheduled operations from disruptions; and 2) they include multiple resources that must be coordinated, such as aircraft, crews, and passengers. While optimization approaches have been essential to the airline industry and effective in developing aircraft and crew schedules, historical models and approaches often fail to capture the complexity of airline operations. For example, approaches, often by necessity, involve a sequential, rather than an integrated process to develop schedules for aircraft and crews, and moreover, the pro...
Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn
Transportation Science, 2016
In this paper we present a novel approach addressing airline delays and recovery. Airline schedule recovery involves making decisions during operations to minimize additional operating costs while getting back on schedule as quickly as possible. The mechanisms used include aircraft swaps, flight cancelations, crew swaps, reserve crews and passenger rebookings. In this context, we introduce another mechanism, namely flight planning, which enables flight speed changes. Flight planning is the process of determining flight plan(s) specifying the route of a flight, its speed and its associated fuel burn. Our key idea in integrating flight planning and disruption management is to adjust the speeds of flights during operations, trading off flying time and fuel burn, and combining with existing mechanisms such as flight holds; all with the goal of striking the right balance of fuel costs and passenger-related delay costs incurred by the airline. We present models for integrated aircraft and passenger recovery with flight planning, both exact and approximate. From computational experiments on data provided by a European airline, we estimate approximately that reductions in passenger disruptions on the order of 66-83%, accompanied by small increases in fuel burn of 0.152-0.155% and total cost savings of 5.7-5.9% for the airline, may be achieved using our approach. We discuss the relative benefits of two mechanisms studied-specifically, flight speed changes and intentionally holding flight departures, and show significant synergies in applying these mechanisms. The results, compared to recovery without integrated flight planning, are increased swap possibilities during recovery, decreased numbers of flight cancelations, and fewer disruptions to passengers.
An integrated decision support tool for airlines schedule recovery during irregular operations
European Journal of Operational Research, 2008
This paper presents a decision support tool for airlines schedule recovery during irregular operations. The tool provides airlines control centers with the capability to develop a proactive schedule recovery plan that integrates all flight resources. A rolling horizon modeling framework, which integrates a schedule simulation model and a resource assignment optimization model, is adopted for this tool. The schedule simulation model projects the list of disrupted flights in the system as function of the severity of anticipated disruptions. The optimization model examines possible resource swapping and flight re-quoting to generate an efficient schedule recovery plan that minimizes flight delays and cancellations. A detailed example that illustrates the application of the tool to recover the schedule of a major US air-carrier during a hypothetical ground delay program scenario is presented. The results of several experiments that illustrates overall model performance in terms of solution quality and computation experience are also given. Published by Elsevier B.V.
Heuristics for flight and maintenance planning of mission aircraft
Annals of Operations Research, 2013
Flight and Maintenance Planning (FMP) of mission aircraft addresses the question of which available aircraft to fly and for how long, and which grounded aircraft to perform maintenance operations on, in a group of aircraft that comprise a unit. The objective is to achieve maximum fleet availability of the unit over a given planning horizon, while also satisfying certain flight and maintenance requirements. The application of exact methodologies for the solution of the problem is quite limited, as a result of their excessive computational requirements. In this work, we prove several important properties of the FMP problem, and we use them to develop two heuristic procedures for solving large-scale FMP instances. The first heuristic is based on a graphical procedure which is currently used for generating flight and maintenance plans of mission aircraft by many Air Force organizations worldwide. The second heuristic is based on the idea of splitting the original problem into smaller sub-problems and solving each sub-problem separately. Both heuristics have been roughly sketched in earlier works that have appeared in the related literature. The present paper develops the theoretical background on which these heuristics are based, provides in detail the algorithmic steps required for their implementation, analyzes their worst-case computational complexity, presents computational results illustrating their computational performance on random problem instances, and evaluates the quality of the solutions that Electronic supplementary material The online version of this article (they produce. The size and parameter values of some of the randomly tested problem instances are quite realistic, making it possible to infer the performance of the heuristics on real world problem instances. Our computational results demonstrate that, under careful consideration, even large FMP instances can be handled quite effectively. The theoretical results and insights that we develop establish a fundamental background that can be very useful for future theoretical and practical developments related to the FMP problem.
Airline disruption management--Perspectives, experiences and outlook
2007
Since the deregulation of many markets, airlines have become more concerned with developing an optimal flight schedule, allowing little slack to accommodate variations from the optimal solution. During operation, the planned schedules often have to be revised because of disruptions caused by severe weather, technical problems and crew sickness. Thus, airline disruption management techniques have emerged.