Cruise Speed Control Research Articles

A matheuristic for aircraft maintenance routing problem incorporating cruise speed control

Aircraft maintenance routing problem incorporating cruise speed control (AMRP-CSC) is an extension of the well-known aircraft maintenance routing problem (AMRP) where the flexible cruise times of flights are considered in route construction. Changing cruise times in AMRP can transform the infeasible flight connections into feasible ones, resulting in a larger solution space and further opportunities for efficiently routing. This may open up the possibility for a substantial improvement in aircraft utilization, but also increases resolution complexity. In this study, a new solution methodology, namely a matheuristic approach, is proposed for this problem. It is composed of three main components: an improved ant colony optimization (IACO) algorithm, a set partitioning (SP) procedure, and a neighborhoods search (NS) procedure. The IACO algorithm serves as a route generator, populating a pool of routes with promising feasible aircraft maintenance routes. Then, a SP model, which features the high-quality columns corresponding to the routes in the pool, is solved to produce a possible better solution. Finally, this solution is further improved by a NS procedure that iteratively solves the reduced AMRP-CSC instances to optimality. This matheuristic approach is analyzed and tested using the data extracting from the Bureau of Transportation Statistics (BTS), and then its accuracy and the efficiency have been demonstrated by experiment analyses.

Reinforcement Learning‐Based Co‐Optimization of Adaptive Cruise Speed Control and Energy Management for Fuel Cell Vehicles

With the development of intelligent autodriving vehicles, the co‐optimization of speed control and energy management under the insurance of safe and comfortable driving has become a vital issue. Herein, the adaptive cruise control scenario is discussed. A co‐optimization method for speed control and energy management for fuel cell vehicles is suggested to delay the degradation of energy sources while preserving fuel cell efficiency. A reward function based on a reinforcement learning (RL) algorithm is developed to optimize the safety coefficient, comfortability, car‐following efficiency, and economy at the speed control level. The RL agent learns to control vehicle speed while avoiding collisions and maximizing the cumulative rewards. To handle the problem of energy management, an adaptive equivalent consumption minimization strategy, which takes into account the deterioration of energy sources, is implemented at the energy management level. The results indicate that the suggested method reduces the demand power by 1.7%, increases the lifetime of power sources, and reduces equivalent hydrogen consumption by 9.4% compared to the model predictive control.

Towards efficient airline disruption recovery with reinforcement learning

Disruptions to airline schedules precipitate flight delays/cancellations and significant losses for airline operations. The goal of the integrated airline recovery problem is to develop an operational tool that provides the airline with an instant and cost-effective solution concerning aircraft, crew members and passengers in face of the emerging disruptions. In this paper, we formulate a decision recommendation framework which incorporates various recovery decisions including aircraft and crew rerouting, passenger reaccommodation, departure holding, flight cancellation and cruise speed control. Given the computational hardness of solving the mixed-integer nonlinear programming (MINP) model by the commercial solver (e.g., CPLEX), we establish a novel solution framework by incorporating Deep Reinforcement Learning (DRL) to the Variable Neighborhood Search (VNS) algorithm with well-designed neighborhood structures and state evaluator. We utilize Proximal Policy Optimization (PPO) to train the stochastic policy exploited to select neighborhood operations given the current state throughout the Markov Decision Process (MDP). Experimental results show that the objective value generated by our approach is within a 1.5% gap with respect to the optimal/close-to-optimal objective of the CPLEX solver for the small-scale instances, with significant improvement regarding runtime. The pre-trained DRL agent can leverage features/weights obtained from the training process to accelerate the arrival of objective convergence and further improve solution quality, which exhibits the potential of achieving Transfer Learning (TL). Given the inherent intractability of the problem on practical size instances, we propose a method to control the size of the DRL agent’s action space to allow for efficient training process. We believe our study contributes to the efforts of airlines in seeking efficient and cost-effective recovery solutions.

Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control

The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm.

Operational aircraft maintenance routing problem incorporating cruise speed control

Allowing variability in flight flying time in aircraft rerouting can achieve improved flight connection opportunities, and thus higher aircraft utilization and enhanced schedule flexibility. However, similar impacts on aircraft routing are underexplored. In this study, a new operational aircraft maintenance routing problem (OAMRP) model that incorporates cruise speed control to realize the flying time variability is formulated. A graph reduction procedure is developed to reduce the excessive model size. Then, the model is solved using a novel ant colony optimization algorithm that considers node attractions in the state transition rule. Computational experiments show that the proposed model, compared with the traditional model, can generate aircraft maintenance routes with higher aircraft utilization, and thereby save aircraft. Moreover, both schedule stability and flexibility are maintained in the proposed model.

Feasibility Analysis of Time-Based Metering Using Cruise-Speed Control and Flight-Level Change for Domestic Flights in Japan

Feasibility Analysis of Time-Based Metering Using Cruise-Speed Control and Flight-Level Change for Domestic Flights in Japan

A practical feedforward speed control system for autonomous underwater vehicles

The autonomy of any airborne, land borne or waterborne vehicle starts with controlling its speed. To achieve cruise speed control, the conventional approach is to use closed-loop feedback control systems that require high precision sensors. Underwater sensors are expensive due to harsh environment and corrosive properties of seawater. In this study, we propose a speed control system for autonomous underwater vehicles (AUVs) that closes the control loop via a feedforward mechanism, eliminating the need for these expensive sensors. The feedforward control is based on the recently developed self-propulsion estimation model which can accurately predict propeller rotation rate for conventional AUV forms. The model is first validated by comparing the predicted resistance and propeller rotation rate of DARPA Suboff with the experimental and numerical results published in the literature. After observing a good match, the feedforward control system is used to ensure that the AUV reaches a predetermined surge speed. Two different underwater vehicles are considered in presenting the results of this study: the DARPA Suboff and the Gavia AUV, which has real time measurements available in the literature. The feedforward control system developed in this study achieves the desired speed with negligible errors. Transient response analysis shows that the computer simulation results are in good agreement with experimental results for the Gavia AUV.

Analyzing the trade-off between CO2 emissions and passenger service level in the airline industry: Mathematical modeling and constructive heuristic

The negative appraisals of the impact of airline industry on climate change have forced the industry to become greener. Over the past several decades, CO2 emissions in the airline industry have increased considerably, thereby adversely affecting the environment. Therefore, airlines are seeking different approaches to reduce the environmental degradation. In this paper, we study cruise speed control to achieve this goal by reducing CO2 emissions while studying passenger service level. We use some indicators to measure the passenger service level in the airline industry. These indicators create a conflict between CO2 emissions and passenger service level. We develop a bi-objective mixed-integer non-linear programming model to integrate flight scheduling, aircraft-path assignment, and gate assignment with the aim of reducing CO2 emissions while increasing passenger service level. Based on a real problem, the model can lead to 11% and 31% improvement in CO2 emissions and passenger service level, respectively. The model is NP-hard. Therefore, we develop a new constructive heuristic method, and we also use the multi-objective simulated annealing algorithm and extend this algorithm to enhance its performance. We also use the design of experiment and Taguchi method to optimize the algorithms parameters. Finally, we evaluate the proposed methods based on information retrieved from major U.S. airlines.

Flight Network-Based Approach for Integrated Airline Recovery with Cruise Speed Control

Airline schedules are generally tight and fragile to disruptions. Disruptions can have severe effects on existing aircraft routings, crew pairings, and passenger itineraries that lead to high delay and recovery costs. A recovery approach should integrate the recovery decisions for all entities (aircraft, crew, passengers) in the system as recovery decisions about an entity directly affect the others’ schedules. Because of the size of airline flight networks and the requirement for quick recovery decisions, the integrated airline recovery problem is highly complex. In the past decade, an increasing effort has been made to integrate passenger and crew related recovery decisions with aircraft recovery decisions both in practice and in the literature. In this paper, we develop a new flight network based representation for the integrated airline recovery problem. Our approach is based on the flow of each aircraft, crew member, and passenger through the flight network of the airline. The proposed network structure allows common recovery decisions such as departure delays, aircraft/crew rerouting, passenger reaccommodation, ticket cancellations, and flight cancellations. Furthermore, we can implement aircraft cruise speed (flight time) decisions on the flight network. For the integrated airline recovery problem defined over this network, we propose a conic quadratic mixed integer programming formulation that can be solved in reasonable CPU times for practical size instances. Moreover, we place a special emphasis on passenger recovery. In addition to aggregation and approximation methods, our model allows explicit modeling of passengers and evaluating a more realistic measure of passenger delay costs. Finally, we propose methods based on the proposed network representation to control the problem size and to deal with large airline networks.

Integrated aircraft-path assignment and robust schedule design with cruise speed control

Assignment of aircraft types, each having different seat capacity, operational expenses and availabilities, critically affects airlines’ overall cost. In this paper, we assign fleet types to paths by considering not only flight timing and passenger demand, as commonly done in the literature, but also operational expenses, such as fuel burn and carbon emission costs associated with adjusting the cruise speed to ensure the passenger connections. In response to flight time uncertainty due to the airport congestions, we allow minor adjustments on the flight departure times in addition to cruise speed control, thereby satisfying the passenger connections at a desired service level. We model the uncertainty in flight duration via a random variable arising in chance constraints to ensure the passenger connections. Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult. To handle them, we use mixed-integer second order cone programming. We compare the performance of a schedule generated by the proposed model to the published schedule for a major U.S. airline. On the average, there exists a 20% overall operational cost saving compared to the published schedule. To solve the large scale problems in a reasonable time, we also develop a two-stage algorithm, which decomposes the problem into planning stages such as aircraft-path assignment and robust schedule generation, and then solves them sequentially.

An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control

To place an emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers’ connection service levels are ensured via chance constraints. We propose a nonlinear mixed integer programming model due to the nonlinear fuel consumption and CO2 emission cost terms in the objective function, which is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of fuel efficient aircraft and even to decrease total number of aircraft needed while satisfying the same service level and maintenance requirements for aircraft fleeting and routing. There is a critical tradeoff between the number of aircraft needed to fulfill the required flights and overall operational expenses. We also propose two heuristic methods to solve larger size problems. Finally, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.

Exploring Energy Efficiency and Service Quality of Airlines with Cruise Speed Control

Exploring Energy Efficiency and Service Quality of Airlines with Cruise Speed Control

The pilot study of object following for automated guided vehicle

This paper aims at the pilot study of the object following for automated guided vehicle (AGV).The proposed approach utilizes the laser range finder (LRF) to detect the distances of all obstacles surrounded in a specified angle range. The AGV can autonomously follow the assigned object according to the rational reflection intensity and range of the object sensed from LRF. The cruise speed control for a moving object depending on the distance between AGV and this object is considered since it is one of the crucial parameters for object following. This paper also discusses the influences of the sensor delay which is an indispensable issue in both control law and control logic. This pilot study employs a down scaled AGV to verify the feasibilities of the steering, accelerating, braking, and object following algorithms developed for AGVs.

Aircraft Rescheduling with Cruise Speed Control

Airline operations are subject to frequent disruptions typically due to unexpected aircraft maintenance requirements and undesirable weather conditions. Recovery from a disruption often involves propagating delays in downstream flights and increasing cruise stage speed when possible in an effort to contain the delays. However, there is a critical trade-off between fuel consumption (and its adverse impact on air quality and greenhouse gas emissions) and cruise speed. Here we consider delays caused by such disruptions and propose a flight rescheduling model that includes adjusting cruise stage speed on a set of affected and unaffected flights as well as swapping aircraft optimally. To the best of our knowledge, this is the first study in which the cruise speed is explicitly included as a decision variable into an airline recovery optimization model along with the environmental constraints and costs. The proposed model allows one to investigate the trade-off between flight delays and the cost of recovery. We show that the optimization approach leads to significant cost savings compared to the popular recovery method delay propagation. Flight time controllability, nonlinear delay, fuel burn and CO2 emission cost functions, and binary aircraft swapping decisions complicate the aircraft recovery problem significantly. In order to mitigate the computational difficulty we utilize the recent advances in conic mixed integer programming and propose a strengthened formulation so that the nonlinear mixed integer recovery optimization model can be solved efficiently. Our computational tests on realistic cases indicate that the proposed model may be used by operations controllers to manage disruptions in real time in an optimal manner instead of relying on ad-hoc heuristic approaches.

Analytical Solution to the Minimum Energy Consumption Based Velocity Profile Optimization Problem With Variable Road Grade

The importance of eco-driving in reducing cumulative fuel consumption of on road vehicles is a well known issue. However, so far a generic algorithm that can globally solve the non-linear optimization problem and still implementable in to the current state of computing on board units is not present. In this study, we examine one aspect of the problem by incorporating the effects of road grade to the optimization problem and generate an optimal velocity trajectory for a given road grade profile. We developed simple yet accurate vehicle and fuel consumption models and employed the models as the objective function and state trajectory constraint of the optimization problem. The necessary conditions derived by employing the calculus of variation theory require to separately solve the differential equations with a set of interior point constraints for each road grade interval. As the control became linear to the Hamiltonian function we defined a set of singular arcs and derived the state and optimal control input trajectories along the arcs. We have tested the analytical solution in two example problems and compared the results with a dynamic programming (DP) solution and constant speed cruise operation. The results have shown that the analytical and DP solutions generate very close velocity trajectories which are around 8 – 10% more efficient than the constant cruise speed control case for the given examples. Moreover the calculation time of the analytical solution is significantly shorter than the DP solution rendering it possible to real-time on board implementations.

Integrated aircraft and passenger recovery with cruise time controllability

Disruptions in airline operations can result in infeasibilities in aircraft and passenger schedules. Airlines typically recover aircraft schedules and disruptions in passenger itineraries sequentially. However, passengers are severely affected by disruptions and recovery decisions. In this paper, we present a mathematical formulation for the integrated aircraft and passenger recovery problem that considers aircraft and passenger related costs simultaneously. Using the superimposition of aircraft and passenger itinerary networks, passengers are explicitly modeled in order to use realistic passenger related costs. In addition to the common routing recovery actions, we integrate several passenger recovery actions and cruise speed control in our solution approach. Cruise speed control is a very beneficial action for mitigating delays. On the other hand, it adds complexity to the problem due to the nonlinearity in fuel cost function. The problem is formulated as a mixed integer nonlinear programming (MINLP) model. We show that the problem can be reformulated as conic quadratic mixed integer programming (CQMIP) problem which can be solved with commercial optimization software such as IBM ILOG CPLEX. Our computational experiments have shown that we could handle several simultaneous disruptions optimally on a four-hub network of a major U.S. airline within less than a minute on the average. We conclude that proposed approach is able to find optimal tradeoff between operating and passenger-related costs in real time.