An IP Framework for the Crew Pair-ing Problem using Subsequence Generation (original) (raw)
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A Subsequence Generation Approach for the Airline Crew Pairing Problem
2011
In this paper we consider an important problem for the airline industry. The widely studied crew pairing problem is typically formulated as a set partitioning problem and solved using the branchand-price methodology. Here we develop a new integer programming framework, based on the concept of subsequence generation, for solving the set partitioning formulation. In subsequence generation one restricts the number of permitted subsequent flights, that a crew member can turn to after completing any particular flight. By restricting ...
Subsequence Generation for the Airline Crew Pairing Problem
2011
Abstract Good and fast solutions to the airline crew pairing problem are highly interesting for the airline industry, as crew costs are the biggest expenditure after fuel for an airline. The crew pairing problem is typically modelled as a set partitioning problem and solved by column generation. However, the extremely large number of possible columns naturally has an impact on the solution time.
An integer programming approach to generating airline crew pairings
Computers & Operations Research, 2009
The ability to generate crew pairings quickly is essential to solving the airline crew scheduling problem. Although techniques for doing so are well-established, they are also highly customized and require significant implementation efforts. This greatly impedes researchers studying important problems such as robust planning, integrated planning, and automated recovery, all of which also require the generating of crew pairings. As an alternative, we present an integer programming (IP) approach to generating crew pairings, which can be solved via traditional methods such as branch-and-bound using off-the-shelf commercial solvers. This greatly facilitates the prototyping and testing of new research ideas. In addition, we suggest that our modeling approach, which uses both connection variables and marker variables to capture the non-linear cost function and constraints of the crew scheduling problem, can be applicable in other scheduling contexts as well. Computational results using data from a major U.S. hub-and-spoke carrier demonstrate the performance of our approach.
A Mathematical Programming Approach to Airline Crew Pairing Optimization
2012
In the ever increasing competitive environment of the airline industry, efficient personnel's planning is among the most challenging tasks and solely responsible for the largest impact on an airline's cost structure. The problem is theoretically appealing because it is computationally difficult due to the huge number of possibilities, and the amount of rules that determine crew planning. This work proposes a methodology to determine the most efficient and least costly way to serve the flights in an airline schedule during a weekly planning horizon considering layovers and deadheads. The strategy is composed of three optimization models: a binary programming model, a model based on the set partitioning problem, and a third model based on the set covering problem, the two latter solved via column generation. Computational results for the three models are presented using test instances built from a mid-sized Colombian airline.
On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks
ArXiv, 2020
Crew pairing optimization (CPO) is critically important for any airline, since its crew operating costs are second-largest, next to the fuel-cost. CPO aims at generating a set of flight sequences (crew pairings) covering a flight-schedule, at minimum-cost, while satisfying several legality constraints. For large-scale complex flight networks, billion-plus legal pairings (variables) are possible, rendering their offline enumeration intractable and an exhaustive search for their minimum-cost full flight-coverage subset impractical. Even generating an initial feasible solution (IFS: a manageable set of legal pairings covering all flights), which could be subsequently optimized is a difficult (NP-complete) problem. Though, as part of a larger project the authors have developed a crew pairing optimizer (AirCROP), this paper dedicatedly focuses on IFS-generation through a novel heuristic based on divide-and-cover strategy and Integer Programming. For real-world large and complex flight ne...
A novel column generation strategy for large scale airline crew pairing problems
Expert Systems With Applications, 2016
A crew pairing is a sequence of flight legs beginning and ending at the same crew domicile. Crew pairing planning is the primary cost-determining phase in airline crew scheduling. Optimizing crew pairings of an airline timetable is an extremely important process which helps to minimize operational crew costs and to maximize crew utilization. There are various restrictions imposed by regulations or company policies that must be considered and satisfied in crew pairing generation process. Keeping these restrictions and regulations in mind, the main goal of the optimization is the generation of low cost sets of valid crew pairings which cover all flights in airline's timetable. For this research study, already existing works related to crew pairing optimization are examined and a new column generation strategy, a pricing network design and a pairing elimination heuristic are developed as a contribution to the previous studies. In the proposed strategy, the main problem is modeled and solved as a set-covering problem and the pricing sub problem is modeled as a shortest-path problem which is efficiently solved over a duty-flight overnight connection graph by the combined usage of heuristic and exact algorithms. The proposed strategy has been tested with real world data obtained from Turkish Airlines and it is seen that it is capable of generating very competitive solutions compared to current practices in Turkish Airlines. It is also observed that there are various advantages of proposed solution approach such as sensitivity to penalty coefficients, generating less deadheads, very close solution times with a single threaded software and light weight hardware.
On Large-Scale Airline Crew Pairing Generation
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
Crew operating cost is the second largest cost component of an airlines’ total operating cost (second only to the fuel cost) and even marginal cost savings here may amount to millions of dollars, annually. Towards it, a crew needs to be efficiently assigned a sequence of flights starting and ending at the same crew base (a crew pairing). The challenge for an airline is to generate crew pairings which completely cover a finite set of flights over a particular time window, with minimum cost, while satisfying multiple legality constraints linked to airlines’ own regulations, labor laws, and government safety rules etc. The success in solving the associated constrained optimization problem largely depends on solving NP-complete subproblems, linked to the generation of a feasible solution (legal crew pairings covering all flights) and generation of pairings with reasonable cost-quality. In an attempt to address these subproblems in a computationally- and time-efficient manner, the contri...
Solving large airline crew scheduling problems: Random pairing generation and strong branching
2001
The airline crew scheduling problem is the problem of assigning crew itineraries to flights. We develop a new approach for solving the problem that is based on enumerating hundreds of millions random pairings. The linear programming relaxation is solved first and then millions of columns with best reduced cost are selected for the integer program. The number of columns is further reduced by a linear programming based heuristic. Finally an integer solution is obtained with a commercial integer programming solver. The branching rule of the solver is enhanced with a combination of strong branching and a specialized branching rule. The algorithm produces solutions that are significantly better than ones found by current practice.
arXiv: Mathematical Software, 2020
Crew Pairing Optimization aims at generating a set of flight sequences (crew pairings), covering all flights in an airline's flight schedule, at minimum cost, while satisfying several legality constraints. CPO is critically important for airlines' business viability, considering that the crew operating cost is their second-largest expense. It poses an NP-hard combinatorial optimization problem, to tackle which, the state-of-the-art relies on relaxing the underlying Integer Programming Problem (IPP) into a Linear Programming Problem (LPP), solving the latter through Column Generation (CG) technique, and integerization of the resulting LPP solution. However, with the growing scale and complexity of the flight networks (those with a large number of flights, multiple crew bases and/or multiple hub-and-spoke subnetworks), the utility of the conventional CG-practices has become questionable. This paper proposed an Airline Crew Pairing Optimization Framework, AirCROP, whose constit...
The Integrated Aircraft Routing and Crew Pairing Problem: Ilp Based Formulations
Jurnal Teknologi, 2016
Minimization of cost is very important in airline as great profit is an important objective for any airline system. One way to minimize the costs in airline is by developing an integrated planning process. Airline planning consists of many difficult operational decision problems including aircraft routing and crew pairing problems. These two sub-problems, though interrelated in practice, are usually solved sequentially leading to suboptimal solutions. We propose an integrated aircraft routing and crew pairing problem model, one approach to generate the feasible aircraft routes and crew pairs, followed by three approaches to solve the integrated model. The integrated aircraft routing and crew scheduling problem is to determine a minimum cost aircraft routes and crew schedules while each flight leg is covered by one aircraft and one crew. The first approach is an integer programming solution method, the second formulation is developed in a way to lend itself to be used efficiently by ...