A Study of Airline Fuel Planning Optimization Using R-Ga

2020 ◽  
Vol 46 ◽  
pp. 189-197
Author(s):  
Ekene Gabriel Okafor ◽  
Osaretin Kole Uhuegho ◽  
Christopher Manshop ◽  
Paul Olugbeji Jemitola ◽  
Osichinaka Chiedu Ubadike
Keyword(s):  
Regression Analysis ◽  
Fuel Consumption ◽  
Large Time ◽  
Inequality Constraints ◽  
Flight Time ◽  
Planning Problem ◽  
Study Objective ◽  
Planning Optimization ◽  
Equality And Inequality Constraints ◽  
Human Effort

In this study, airline planning optimization problem based on ferry strategy was considered. Cost was the study objective function subject to forty equality and inequality constraints. Regression analysis as well a genetic algorithm (GA) was used to solve the problem. The mathematical relationship between flight fuel consumption and flight time was established using regression analysis, while GA was used for the optimization. The established mathematical model was used to predict the fuel consumption for the twenty scheduled flight consider based on their respective flight time. The result was found to be satisfactory, as optimal fuel lift plan was achieved in approximately twenty seconds of program run time, as against the large time usually spend using human effort to solve the fuel planning problem. The optimized fuel lift plan was compared with the actual fuel lift plan executed by the airline for the twenty scheduled flight considered. The result revealed thirty percent savings using the optimized plan in comparison to the actual fuel lift plan executed by the airline.

scholarly journals Optimization-based approach to path planning for closed chain robot systems

2011 ◽  
Vol 21 (4) ◽  
pp. 659-670 ◽  
Author(s):  
Wojciech Szynkiewicz ◽  
Jacek Błaszczyk
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Closed Chain ◽  
Robot Systems ◽  
Equality And Inequality Constraints ◽  
Primal Dual ◽  
The Given

Optimization-based approach to path planning for closed chain robot systems An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a "quasi-dynamic" NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used, which implements a nonlinear primal-dual interior-point method, one of the leading techniques for large-scale nonlinear optimization.

Real-Time Optimal Coherent Phantom Track Generation via the Virtual Motion Camouflage Approach

2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Yunjun Xu ◽  
Gareth Basset
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Inequality Constraints ◽  
Early Termination ◽  
Planning Problem ◽  
Motion Camouflage ◽  
Equality And Inequality Constraints ◽  
Time Optimal ◽  
And Control ◽  
Air Vehicles

Coherent phantom track generation through controlling a group of electronic combat air vehicles is currently an area of great interest to the defense agency for the purpose of deceiving a radar network. However, generating an optimal or even feasible coherent phantom trajectory in real-time is challenging due to the high dimensionality of the problem and severe geometric, as well as state, control, and control rate constraints. In this paper, the bio-inspired virtual motion camouflage based methodology, augmented with the derived early termination condition, is investigated to solve this constrained collaborative trajectory planning problem in two approaches: centralized (one optimization loop) and decentralized (two optimization loops). Specifically, in the decentralized approach, the first loop finds feasible phantom tracks based on the early termination condition and the equality and inequality constraints of the phantom track. The second loop uses the virtual motion camouflage method to solve for the optimal electronic combat air vehicle trajectories based on the feasible phantom tracks obtained in the first loop. Necessary conditions are proposed for both approaches so that the initial and final velocities of the phantom and electronic combat air vehicles are coherent. It is shown that the decentralized approach can solve the problem much faster than the centralized one, and when the decentralized approach is applied, the computational cost remains roughly the same for the cases when the number of nodes and/or the number of electronic combat air vehicles increases. It is concluded that the virtual motion camouflage based decentralized approach has promising potential for usage in real-time implementation.

Optimum Path Planning for Mechanical Manipulators

1981 ◽  
Vol 103 (2) ◽  
pp. 142-151 ◽  
Author(s):  
J. Y. S. Luh ◽  
C. S. Lin
Keyword(s):  
Path Planning ◽  
Feasible Solution ◽  
Computing Time ◽  
Inequality Constraints ◽  
Programming Algorithm ◽  
Planning Problem ◽  
Linear Inequality Constraints ◽  
Nonlinear Inequality ◽  
Angular Velocities ◽  
Nonlinear Inequality Constraints

To assure a successful completion of an assigned task without interruption, such as the collision with fixtures, the hand of a mechanical manipulator often travels along a preplanned path. An advantage of requiring the path to be composed of straight-line segments in Cartesian coordinates is to provide a capability for controlled interaction with objects on a moving conveyor. This paper presents a method of obtaining a time schedule of velocities and accelerations along the path that the manipulator may adopt to obtain a minimum traveling time, under the constraints of composite Cartesian limit on linear and angular velocities and accelerations. Because of the involvement of a linear performance index and a large number of nonlinear inequality constraints, which are generated from physical limitations, the “method of approximate programming (MAP)” is applied. Depending on the initial choice of a feasible solution, the iterated feasible solution, however, does not converge to the optimum feasible point, but is often entrapped at some other point of the boundary of the constraint set. To overcome the obstacle, MAP is modified so that the feasible solution of each of the iterated linear programming problems is shifted to the boundaries corresponding to the original, linear inequality constraints. To reduce the computing time, a “direct approximate programming algorithm (DAPA)” is developed, implemented and shown to converge to optimum feasible solution for the path planning problem. Programs in FORTRAN language have been written for both the modified MAP and DAPA, and are illustrated by a numerical example for the purpose of comparison.

scholarly journals Relations between Abs-Normal NLPs and MPCCs. Part 2: Weak Constraint Qualifications

2021 ◽  
Vol Volume 2 (Original research articles>) ◽  
Author(s):  
Lisa C. Hegerhorst-Schultchen ◽  
Christian Kirches ◽  
Marc C. Steinbach
Keyword(s):  
Normal Form ◽  
Optimization Problems ◽  
Constraint Qualifications ◽  
Inequality Constraints ◽  
First Order ◽  
Nonlinear Programs ◽  
Mathematical Programs ◽  
Ongoing Effort ◽  
Equality And Inequality Constraints ◽  
Smooth Optimization

This work continues an ongoing effort to compare non-smooth optimization problems in abs-normal form to Mathematical Programs with Complementarity Constraints (MPCCs). We study general Nonlinear Programs with equality and inequality constraints in abs-normal form, so-called Abs-Normal NLPs, and their relation to equivalent MPCC reformulations. We introduce the concepts of Abadie's and Guignard's kink qualification and prove relations to MPCC-ACQ and MPCC-GCQ for the counterpart MPCC formulations. Due to non-uniqueness of a specific slack reformulation suggested in [10], the relations are non-trivial. It turns out that constraint qualifications of Abadie type are preserved. We also prove the weaker result that equivalence of Guginard's (and Abadie's) constraint qualifications for all branch problems hold, while the question of GCQ preservation remains open. Finally, we introduce M-stationarity and B-stationarity concepts for abs-normal NLPs and prove first order optimality conditions corresponding to MPCC counterpart formulations.

A Comparative Study of Improved Teaching Learning Based Optimization Technique on Economic Load Dispatch Problem with Generator Constraints

2016 ◽  
Vol 5 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Sumit Banerjee ◽  
Chandan Chanda ◽  
Deblina Maity
Keyword(s):  
Optimization Technique ◽  
Inequality Constraints ◽  
Test System ◽  
Economic Load Dispatch ◽  
Thermal Plant ◽  
Equality And Inequality Constraints ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Operational Constraints ◽  
Rate Limit

This article presents a novel improved teaching learning based optimization (I-TLBO) technique to solve economic load dispatch (ELD) problem of the thermal plant without considering transmission losses. The proposed methodology can take care of ELD problems considering practical nonlinearities such as ramp rate limit, prohibited operating zone and valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. I-TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. The effectiveness of the proposed algorithm has been verified on test system with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

scholarly journals Study on the Optimization Method of Point Merge Procedure Based on Benefit in the Terminal Area

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yong Tian ◽  
Dawei Xing ◽  
Lili Wan ◽  
Bojia Ye
Keyword(s):  
Fuel Consumption ◽  
Rapid Development ◽  
Flow Distribution ◽  
Optimization Method ◽  
Flight Time ◽  
Pollutant Emission ◽  
Flight Operations ◽  
Obstacle Clearance ◽  
Noise Impact ◽  
Terminal Area

With the rapid development of the air transport industry, the problem of airspace congestion and flight delay in the terminal area (TMA) becomes more and more serious. In order to improve the efficiency of flight operations in TMA, point merge procedure had been devised. This paper takes the approach routes in TMA as the research object, taking into account such conditions as obstacle clearance, flight interval, and procedure area. Based on the flight time, fuel consumption, pollutant emission, and noise impact, an optimization model of point merge procedure is constructed. Genetic algorithm is used to optimize the structure of procedure. The Shanghai Hongqiao International Airport is selected for simulation verification, and the actual flow distribution of the airport is analyzed as an example. The results show that the average flight time was reduced by 0.26 min, the average fuel consumption was reduced by 1,240.64 kg, the average NOx emissions were reduced by 1.09 kg, and the noise impact range was contracted by 55 km2 after optimization. The point merge procedure optimization method can be expected to reduce the flight time, fuel consumption, and environmental impact of flights in TMA, so as to optimize the aircraft approach trajectory.

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