Aircraft Maintenance Routing Problem – A Literature Survey

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch

Queues, Planes and Games: Algorithms for Scheduling Passengers, and Decision Making in Stackelberg Games

In this dissertation, I present three theoretical results with real-world applications related to scheduling and distributionally-robust games, important fields in discrete optimization, and computer science. The first chapter provides simple, technology-free interventions to manage elevator queues in high-rise buildings when passenger demand far exceeds the capacity of the elevator system. The problem was motivated by the need to manage passengers safely in light of reduced elevator capacities during the COVID-19 pandemic. We use mathematical modeling, epidemiological expertise, and simulation to design and evaluate our algorithmic solutions. The key idea is to explicitly or implicitly group passengers that are going to the same floor into the same elevator as much as possible, substantiated theoretically using a technique from queuing theory known as stability analysis. This chapter is joint work with Charles Branas, Adam Elmachtoub, Clifford Stein, and Yeqing Zhou, directly in collaboration with the New York City Mayor’s Office of the Chief Technology Officer and the Department of Citywide Administrative Services. The second chapter proposes new algorithms for recomputing passenger itineraries for airlines during major disruptions when carefully planned schedules are thrown into disarray. An airline network is a massive temporal graph, often with tight regulatory and operational constraints. When disruptions propagate through an airline network, the objective is to \textit{recover} within a given time frame from a disruption, meaning we replan schedules affected by the disruption such that the new schedules have to match the originally planned schedules after the time frame. We aim to solve the large-scale airline recovery problem with quick, user-independent, consistent, and near-optimal algorithms. We provide new algorithms to solve the passenger recovery problem, given recovered flight and crew solutions. We build a preprocessing step and construct an Integer Program as well as a network-based approach based on solving multiple-label shortest path problems. Experiments show the tractability of our proposed algorithms on airline data sets with heavy flight disruptions. This chapter is joint work with Clifford Stein, stemming from an internship and collaboration with the Machine Learning team (Artificial Intelligence organization) of GE Global Research, Niskayuna, New York. The third chapter is about computing distributionally-robust strategies for a popular game theory model called Stackelberg games, where one player, called the leader, is able to commit to a strategy first, assuming the other player(s), called follower(s) would best respond to the strategy. In many of the real-world applications of Stackelberg games, parameters such as payoffs of the follower(s) are not known with certainty. Distributionally-robust optimization allows a distribution over possible model parameters, where this distribution comes from a set of possible distributions. The goal for the leader is to maximize their expected utility with respect to the worst-case distribution from the set. We initiate the study of distributionally-robust models for Stackelberg games, show that a distributionally-robust Stackelberg equilibrium always exists across a wide array of uncertainty models, and provide tractable algorithms for some general settings with experimental results. This chapter is joint work with Christian Kroer

Robust airline scheduling with controllable cruise times and chance constraints

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.This is a study on robust airline scheduling where flight block times are considered in two parts as cruise time and non-cruise time. Cruise times are controllable and non-cruise times are random variables. Cruise time controllability is used together with idle time insertion to handle uncertainty to guarantee passenger connection service levels while ensuring minimum costs. The nonlinearity of these cost functions are handled by representing them via second order conic inequalities. The uncertainty in non-cruise times are modeled through chance constraints on passenger connection service levels, which are expressed using second order conic inequalities using the closed form equations. Congestion levels of origin and destination airports are used to decide variability for each flight. Computational study shows exact solutions can be obtained by commercial solvers in seconds for a single hub schedule and in minutes for a 4-hub daily schedule of a major US carrieDuran, Aslıgül SerasuM.S

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.</p

Optimization methods for scheduling in industrial applications

Scheduling optimization has always been a challenging topic in different industries especially over a long planning horizon. Decisions with a consideration of various operational factors subject to limited resources often need to be made to reduce overall costs, maximize utilization and balance resources. To this end, many researchers have developed various models and solution methodologies for deterministic scheduling problems with a consideration of a set of limited resources. As the scheduling system in different industries becomes more complex and sophisticated, additional resources should be incorporated and contradictory goals need to be more carefully evaluated to create a more practical and flexible plan. A robust plan that deals with uncertainties in scheduling has also received a lot of attention in recent years in case of unanticipated events. What’s more, extra information or requests can arise and should also be taken into account during the planning horizon, therefore a more dynamic method is preferred to update the scheduling plan, especially in multi-period problems. This work focuses on scheduling optimization problems in three different industries with a consideration of extra resources, more operational goals or uncertainties in a more dynamic environment. We also proposed multi-step methods or preprocessing procedures to solve the industrial-sized problems efficiently and obtain exact or near-optimal solutions. Chapter 1 presents an overall introduction to this dissertation and its chapters, along with the organization of it. Chapter 2 introduces a two-stage approach for minimizing the impact of daily disruptions on an airline’s published flight schedule. The problem is characterized by uncertainty in the duration of the disruption and the point in time when its length becomes known. Both a single-commodity network model and multi-commodity network model with side constraints are developed to first determine the flights that are most likely to be affected, and then to adjust their schedules to achieve system-wide optimality. The overall objective is to minimize the weighted sum of total passenger delay costs, cancellation costs, curfew violation costs, and variation from the original schedule. The two types of uncertainty are addressed by examining a range of scenarios that reflect the most likely outcomes. The results provide guidance and a measure of robustness for the flight director as the disruption unfolds. A rolling horizon approach that closely mimics current procedures used by several airlines is also presented to provide a benchmark for comparisons with the two-stage solutions. In Chapter 3, a discrete-time mixed-integer linear programming (MILP) model for a generalized flexible job-shop scheduling problem as represented by a state-task network in batch processing facilities in presented. The problem is characterized by reentrant flow, sequence-dependent changeover time, machine downtime, and skilled labor requirements. Two preprocessing procedures are proposed to reduce the size of the MILP model, and represent a major contribution of the research. The procedures reduce the number of assignment variables by exploiting job precedence and workforce qualifications. Machine availability for each task is determined as a function of possible start and end times, given duration, and maintenance schedule. The overall objective is to maximize the number of scheduled tasks while minimizing their total finish time. Computational experiments are conducted with real and randomly generated instances. The results show that optimal solutions can be obtained for medium-size problems within a reasonable amount of time, primarily due to the use of the preprocessing procedures. Chapter 4 presents a two-step approach for efficiently solving a weekly home health-care scheduling and routing problem. Two new mixed-integer programming (MIP) models are proposed, where the is first used for making patient-therapist assignments over the week, and the second for deriving daily routes. In both MIPs, the objective function contains a hierarchically weighted set of goals. The major components of the full problem are continuity of care, downgrading, workload balance, time windows, overtime, and mileage costs. A new preprocessing procedure is developed to limit the service area of each therapist to a single group of overlapping patients. Once the groups are formed, weekly schedules are constructed with the MIPs. The overall objective is to minimize the number of unscheduled visits and total travel and service costs subject to the operational and physical constraints mentioned above. Computational experiments are conducted with real data sets provided by a national home health agency. The results show that optimal solutions can be obtained quickly for large-size instances, and that they compare favorably with results obtained with a proposed integrated model as well as the actual schedules. Lastly, Chapter 5 concludes the dissertation by summarizing research contributions, key research findings, and indicating future research directions.Mechanical Engineerin

Robust Optimization for Airline Scheduling and Vehicle Routing

Robust optimization is an emerging modeling approach to make decisions under uncertainty. It provides an alternative framework to stochastic optimization where operational parameters are random and do not assume any probability distribution. In this thesis, we study three important problems in routing and scheduling under uncertainty, namely, the crew pairing problem, the shortest path problem with resource constraints, and the vehicle routing problem with time windows. We present robust optimization models and propose novel solution approaches, and perform extensive numerical testing to validate the models and solutions. The crew pairing problem finds a set of legal pairings with minimum cost to cover a set of flights. An optimal solution for the deterministic case, however, is often found to be far from optimal or even infeasible when implemented due to the several uncertainties inherent to the airline industry. We present a robust crew pairing formulation where time between flights may vary within an interval. The robust model determines a solution that minimizes crew cost and provides protection against disruptions with a specified level. A column generation approach is presented to solve the robust crew pairing problem. The robust model and the solution approach are tested on a set of instances based on an European airline. The solutions are more robust than the deterministic ones under simulated disruptions. The shortest path problem with resource constraints (SPPRC) is an important problem that appears as a subproblem in many routing and scheduling problems. The second study in the thesis focuses on the robust SPPRC where both cost and resource consumptions are random. The robust SPPRC determines a minimum cost path that is feasible when a number of variations occur for each resource. We present a mixed-integer programming (MIP) model that is equivalent to the robust SPPRC model, and develop graph reduction techniques and two solution methods. The first solution method is a sequential algorithm that solves a series of deterministic SPPRC. The second is a modified label-setting algorithm that uses a new dominance rule. Numerical testing shows that the modified label-setting algorithm outperforms the sequential algorithm and the MIP model. The third problem studied is the vehicle routing problem with time windows under uncertain customer demands. The robust model determines a set of routes with minimum cost such that each customer is served exactly once within the time window and each route is feasible when a number of customers change their demands. We propose a branch-and-price-and-cut algorithm and a novel separation strategy to determine valid inequalities that make use of data uncertainty. The model and solution methodology are tested on instances generated based on the Solomon instances. The robust solutions provide significant protection against random changes in customer demands compared to the deterministic solutions

Decision making under uncertainties for air traffic flow management

A goal of air traffic flow management is to alleviate projected demand-capacity imbalances at airports and in en route airspace through formulating and applying strategic Traffic Management Initiatives (TMIs). As a new tool in the Federal Aviation Administration\u27s NextGen portfolio, the Collaborative Trajectory Options Programs (CTOP) combines many components from its predecessors and brings two important new features: first, it can manage multiple constrained regions in an integrated way with a single program; second, it allows flight operators to submit a set of desired reroute options (called a Trajectory Options Set or TOS), which provides great flexibility and efficiency. One of the major research questions in TMI optimization is how to determine the planned acceptance rates for airports or congested airspace regions (Flow Constrained Areas or FCA) to minimize system-wide costs. There are two important input characteristics that need to be considered in developing optimization models to set acceptance rates in a CTOP: first, uncertain airspace capacities, which result from imperfect weather forecast; second, uncertain demand, which results from flights being geographically redistributed after their TOS options are processed. Although there are other demand disturbances to consider, such as popup flights, flight cancellations, and flight substitutions, their effect on demand estimates at FCAs will likely be far less than that of rerouting from TOSs. Hence, to cope with capacity and demand uncertainties, a decision-making under uncertainty problem needs to be solved. In this dissertation, three families of stochastic programming models are proposed. The first family of models, which are called aggregate stochastic models and are formulated as multi-commodity flow models, can optimally plan ground and air delay for groups of flights given filed route choice of each flight. The second family of models, which are called disaggregate stochastic models and directly control each individual flight, can give the theoretical lower bounds for the very general reroute, ground-, and air-holding problem with multiple congested airspace regions and multiple route options. The third family of models, called disaggregate-aggregate models, can be solved more efficiently compared with the second class of models, and can directly control the queue size at each congested region. Since we assume route choice is given or route can be optimized along with flight delay in a centralized manner, these three families of models, although can provide informative benchmarks, are not compatible with current CTOP software implementation and have not addressed the demand uncertainty problem. The simulation-based optimization model, which can use stochastic programming models as part of its heuristic, addresses the demand uncertainty issue by simulating CTOP TOS allocation in the optimization process, and can give good suboptimal solution to the practical CTOP rate planning problem. Airline side research problems in CTOP are also briefly discussed in this dissertation. In particular, this work quantifies the route misassignment cost due to the current imperfect Relative Trajectory Cost (RTC) design. The main contribution of this dissertation is that it gives the first algorithm that optimizes the CTOP rate under demand and capacity uncertainty and is compatible with the Collaborative Decision Making (CDM) CTOP framework. This work is not only important in providing much-needed decision support capabilities for effective application of CTOP, but also valuable for the general multiple constrained airspace resources multiple reroutes optimization problem and the design of future air traffic flow management program

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface

Airports are the main bottlenecks in the Air Traffic Management (ATM) system. The predicted 84% increase in global air traffic in the next two decades has rendered the improvement of airport operational efficiency a key issue in ATM. Although the operations on runways, taxiways, and aprons are highly interconnected and interdependent, the current practice is not integrated and piecemeal, and overly relies on the experience of air traffic controllers and stand allocators to manage operations, which has resulted in sub-optimal performance of the airport surface in terms of operational efficiency, capacity, and safety. This thesis proposes a mixed qualitative-quantitative methodology for integrated and joint optimisation of runways, taxiways, and aprons, aiming to improve the efficiency of airport surface operations by integrating the operations of all three resources and optimising their coordination. This is achieved through a two-stage optimisation procedure: (1) the Integrated Apron and Runway Assignment (IARA) model, which optimises the apron and runway allocations for individual aircraft on a pre-tactical level, and (2) the Integrated Dynamic Routing and Off-block (IDRO) model, which generates taxiing routes and off-block timing decisions for aircraft on an operational (real-time) level. This two-stage procedure considers the interdependencies of the operations of different airport resources, detailed network configurations, air traffic flow characteristics, and operational rules and constraints. The proposed framework is implemented and assessed in a case study at Beijing Capital International Airport. Compared to the current operations, the proposed apron-runway assignment reduces total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time and fuel consumption respectively by 15.5%, 15.28%, 45.1%, [58.7%, 35.3%, 16%] (RWY01, RWY36R, RWY36L) and 6.6%; gated assignment is increased by 11.8%. The operational feasibility of this proposed framework is further validated qualitatively by subject matter experts (SMEs). The potential impact of the integrated apron-runway-taxiway operation is explored with a discussion of its real-world implementation issues and recommendations for industrial and academic practice.Open Acces