Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method

Airline crew cost is the second-largest operating cost component and its marginal improvement may translate to millions of dollars annually. Further, it's highly constrained-combinatorial nature brings-in high impact research and commercial value. The airline crew pairing optimization problem (CPOP) is aimed at generating a set of crew pairings, covering all flights from its timetable, with minimum cost, while satisfying multiple legality constraints laid by federations, etc. Depending upon CPOP's scale, several Genetic Algorithm and Column Generation based approaches have been proposed in the literature. However, these approaches have been validated either on small-scale flight datasets (a handful of pairings) or for smaller airlines (operating-in low-demand regions) such as Turkish Airlines, etc. Their search-efficiency gets impaired drastically when scaled to the networks of bigger airlines. The contributions of this paper relate to the proposition of a customized genetic algorithm, with improved initialization and genetic operators, developed by exploiting the domain-knowledge; and its comparison with a column generation based large-scale optimizer (developed by authors). To demonstrate the utility of the above-cited contributions, a real-world test-case (839 flights), provided by GE Aviation, is used which has been extracted from the networks of larger airlines (operating up to 33000 monthly flights in the US).Comment: 7 pages, 3 figure

Quantum Computing for Airline Planning and Operations

Classical algorithms and mathematical optimization techniques have beenused extensively by airlines to optimize their profit and ensure that regulationsare followed. In this thesis, we explore which role quantum algorithmscan have for airlines. Specifically, we have considered the two quantum optimizationalgorithms; the Quantum Approximate Optimization Algorithm(QAOA) and Quantum Annealing (QA). We present a heuristic that integratesthese quantum algorithms into the existing classical algorithm, whichis currently employed to solve airline planning problems in a state-of-the-artcommercial solver. We perform numerical simulations of QAOA circuits andfind that linear and quadratic algorithm depth in the input size can be requiredto obtain a one-shot success probability of 0.5. Unfortunately, we areunable to find performance guarantees. Finally, we perform experiments withD-wave’s newly released QA machine and find that it outperforms 2000Q formost instances

Methods for Improving Robustness and Recovery in Aviation Planning.

In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd

Solving the long haul crew pairing problem dc by Rajesh Gopaladrishna Shenoi.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1994.Includes bibliographical references (leaves 107-118).M.S

Integrated aircraft routing and crew pairing problem by benders decomposition

Master'sMASTER OF ENGINEERIN

Optimization Approaches for Solving Large-Scale Personnel Scheduling Problems

Personnel scheduling is one of the most critical components in logistical planning for many practical areas, particularly in transportation, public services, and clinical operations. Because manpower is both an expensive and scarce resource, even a tiny improvement in utilization can provide huge expense savings for businesses. Additionally, a slightly better assignment schedule of the involved professionals can significantly increase their work satisfaction, which can in turn greatly improve the quality of the services customers or patients receive. However, practical personnel scheduling problems (PSPs) are hard to solve because modeling all of the complicated and nuanced requirements and rules is challenging. Moreover, since an iterative construction process may be necessary for handling the multiple-criteria or ill-defined objective nature of many PSPs, the model is expected to be solved in a short time while providing high-quality solutions, despite its large size and complexity. In this dissertation, we propose new models and solution approaches to address these challenges. We study in total three real-world PSPs. We first consider the crew pairing construction for a cargo airline. Each crew pairing is a sequence of flights assigned to a specific line/bid crew to operate in practice. Unlike traditional passenger aviation, due to the cargo airline's underlying network, each crew pairing will specify a complete flying schedule for the assigned crew over the entire planning horizon. Consequently, an extra and unique set of requirements must be incorporated into the construction process. We solve the problem using a delayed column generation framework. We develop a restricted shortest path model to incorporate the entire set of complicated requirements simultaneously and solve it using a labeling algorithm accelerated by a handful of proposed strategies. Computational experiments show that our approach can solve the crew pairing problem in a short time, while almost always delivering an optimal solution. Second, we consider an extension of the previous cargo crew scheduling problem, where a "break" is allowed to take place in the "middle" of each crew pairing. This break feature, working as a special type of conventional deadheading, is expected to significantly increase the flight coverage for practical deployment. However, incorporating this feature will result in an extremely dense underlying network, which introduces new computational challenges. To address this issue, we propose a bidirectional labeling based arc selection approach, which only needs to work on a tiny sub-network each time but can still guarantee the exactness of the delayed column generation process. We demonstrate through real-world instances that our proposed approach can solve this relaxed problem extension in a very short time and the resulting flight coverage will increase by over 30%. Finally, we study a medical resident annual block scheduling problem. We need to assign residents to perform services at different clinical units during each time period across the academic year so that the residents receive appropriate training while the hospital gets staffed sufficiently. We propose a two-stage partial fixing solution framework to address the long runtime issue caused by traditional approaches. A network-based model is also developed to provide a high-quality service selection to initiate this two-stage framework. Experiments using inputs from our clinical collaborator show that our approach can speed up the schedule construction at least 5 times for all instances and even over 100 times for some huge-size ones compared to a widely-used traditional approach.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169758/1/jhguo_1.pd

Airline workforce scheduling based on multi-agent systems

El trabajo consiste en realizar una programación de horarios para los empleados del servicio al cliente de una aerolínea, junto con el transporte y ruteo de los mismos. Estos problemas son altamente complejos (NP-Hard), por consiguiente, se desarrolló un sistema basado en agentes que permitiera realizar la programación de horarios y simular escenarios inesperados para encontrar una solución eficaz y efectiva. Además, se busca comparar las soluciones de dos métodos diferentes, centralizado y distribuido, junto con la solución actual de la aerolínea, analizando el impacto que cada una de estas genera.This project focuses on the workforce scheduling for an airline's customer service employees, along with their transportation and routing. These problems are highly complex (NP-Hard), therefore, an agent-based system was developed that allowed scheduling and simulating unexpected scenarios to find an efficient and effective solution. In addition, it seeks to compare the solutions of two different methods, centralized and distributed, with the current solution of the airline, analyzing the impact that each of these generates.Ingeniero (a) IndustrialPregrad

A Combined Adaptive Tabu Search and Set Partitioning Approach for the Crew Scheduling Problem with an Air Tanker Crew Application

This research develops the first metaheuristic approach to the complete air crew scheduling problem. It develops the first dynamic, integrated, set-partitioning based vocabulary scheme for metaheuristic search. Since no benchmark flight schedules exist for the tanker crew scheduling problem, this research defines and develops a Java™ based flight schedule generator. The robustness of the tabu search algorithms is judged by testing them using designed experiments. An integer program is developed to calculate lower bounds for the tanker crew scheduling problem objectives and to measure the overall quality of solutions produced by the developed algorithms