scholarly journals Sectorization and Configuration Transition in Airspace Design

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Xiang Zou ◽  
Peng Cheng ◽  
Bang An ◽  
Jingyan Song
Keyword(s):  
Minimum Cost ◽  
Nsga Ii ◽  
Beijing Area ◽  
Control Center ◽  
Practical Applications ◽  
Current Configuration ◽  
Dynamic Airspace Configuration ◽  
Constrained Evolution ◽  
Consecutive Time ◽  
Configuration Transition

Current airspace is sectorized according to some predefined rules that are not flexible. To facilitate utilizing the airspace more efficiently, methods to design sectors need to be promoted. In this paper, we propose an undirected graph cut-based approach that employs a memetic local search-embedded constrained evolution algorithm, NSGA-II, to generate nondominated airspace configurations. We also propose a new concave hull-based method to automatically depict sector boundaries. In addition, we also study the configuration transition problem. We define the similarity of the two different configurations and calculate their similarity with a bisection diagram and a minimum cost flow algorithm. We build a forward network to represent configuration transitions across several consecutive time periods and use multiobjective dynamic programming to determine a series of nondominated configuration links from the first period to the end. We test our approaches by simulation in high-altitude airspace controlled by Beijing Area Control Center. The results show that our sectorization method outperforms the current configuration in practice, providing a lower sector number, lower intersector flow, more balanced workload distribution among the different sectors, and no constraint violations, so that the proposed approach shows its significant potential as practical applications for dynamic airspace configuration.

scholarly journals A biobjective capacitated vehicle routing problem using metaheuristic ILS and decomposition

2021 ◽  
Vol 12 (3) ◽  
pp. 293-304 ◽  
Author(s):  
Luis Fernando Galindres-Guancha ◽  
Eliana Toro-Ocampo ◽  
Ramón Gallego-Rendón
Keyword(s):  
Approximate Method ◽  
Vehicle Routing ◽  
Real Life ◽  
Minimum Cost ◽  
Iterated Local Search ◽  
Test Cases ◽  
Nsga Ii ◽  
Routing Problem ◽  
Decomposition Approach ◽  
Multiobjective Approach

Vehicle routing problems (VRPs) have usually been studied with a single objective function defined by the distances associated with the routing of vehicles. The central problem is to design a set of routes to meet the demands of customers at minimum cost. However, in real life, it is necessary to take into account other objective functions, such as social functions, which consider, for example, the drivers' workload balance. This has led to growth in both the formulation of multiobjective models and exact and approximate solution techniques. In this article, to verify the quality of the results, first, a mathematical model is proposed that takes into account both economic and work balance objectives simultaneously and is solved using an exact method based on the decomposition approach. This method is used to compare the accuracy of the proposed approximate method in test cases of medium mathematical complexity. Second, an approximate method based on the Iterated Local Search (ILS) metaheuristic and Decomposition (ILS/D) is proposed to solve the biobjective Capacitated VRP (bi-CVRP) using test cases of medium and high mathematical complexity. Finally, the nondominated sorting genetic algorithm (NSGA-II) approximate method is implemented to compare both medium- and high-complexity test cases with a benchmark. The obtained results show that ILS/D is a promising technique for solving VRPs with a multiobjective approach.

scholarly journals Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 811 ◽  
Author(s):  
Yongmao Xiao ◽  
Qingshan Gong ◽  
Xiaowu Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Model ◽  
Low Cost ◽  
Minimum Cost ◽  
Processing Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Set Up

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.

OPTIMIZATION OF MAINTENANCE SCHEDULING OF SHIP BORNE MACHINERY FOR IMPROVED RELIABILITY AND REDUCED COST

2012 ◽  
Vol 19 (03) ◽  
pp. 1250014 ◽  
Author(s):  
AJIT KUMAR VERMA ◽  
A. SRIVIDYA ◽  
ANIL RANA ◽  
SANJAY K. KHATTRI
Keyword(s):  
Minimum Cost ◽  
Time Interval ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Multi Objective ◽  
Search Spaces ◽  
Maintenance Schedule ◽  
Select Group ◽  
Daunting Task ◽  
Multiple Variables

Ships have a wide variety of machinery available onboard that is crucial for her sustenance at sea for prolonged durations. The machinery can be grouped into various plants, such as propulsion plant, air conditioning plants, power generation plants, etc., each having its own specific function. The plants in turn are composed of various systems which further comprise various types of machinery. There are redundancies built in at the plant level, as well as at the system and at machinery level, so as to improve the reliability of the ship as a whole. Planning of maintenance schedule, specifically for tasks which can only be undertaken in an ashore repair yard is a daunting task for the maintenance managers. The paper presents a NSGA-II (nondominated sorting genetic algorithm) based multi-objective optimization approach to arrive at an optimum maintenance plan for the vast variety of machinery in order to improve the average reliability of ship's operations at sea at minimum cost. The paper presents the advantages that can accrue from introducing short maintenance periods for a select group of machinery, within the constraints of mandatory operational time, over the method of following a common maintenance interval for all the machinery. The problem function in hand is nonlinear, multi-modal and multi-objective in nature. The search spaces for the problem is noncontinuous and the (multiple) variables, such as time interval for maintenance, serial number of equipment, number of minor maintenance actions, etc., are uncoded real parameters.

scholarly journals Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 225 ◽  
Author(s):  
Xianghua Xu ◽  
Chengwei Zhao ◽  
Tingcong Ye ◽  
Tao Gu
Keyword(s):  
Detection Threshold ◽  
Region Of Interest ◽  
Minimum Cost ◽  
Circular Ring ◽  
Barrier Coverage ◽  
Bistatic Radar ◽  
Radar Sensor ◽  
Minimum Threshold ◽  
Practical Applications ◽  
Closed Area

Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

A Manufacturing Oriented Single Point Search Hyper-Heuristic Scheme for Multi-Objective Optimization

2017 ◽  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert Gao ◽  
Jiong Tang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Single Point ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Practical Applications ◽  
Multi Objective ◽  
Point Search

Multi-objective optimization problems are frequently encountered in engineering analyses. Optimization techniques in practical applications are devised and evaluated mostly for specific problems, and thus may not be generally applicable when applications vary. In this study we formulate a probability matching based hyper-heuristic scheme, then propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-pick) towards multi-objective optimization problems of various properties, namely DTLZ and UF test instances. Making use of the domination amount, crowding distance and hypervolume calculations, the hyper-heuristic scheme could meet different optimization requirements. The approach developed (MOSA/R-HH) exhibits better and more robust performance compared to AMOSA, NSGA-II and MOEA/D as illustrated in the numerical tests. The outcome of this research may potentially benefit various design and manufacturing practices.

Dynamic Airspace Configuration Using Approximate Dynamic Programming

2012 ◽  
Vol 2266 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Sameer Kulkarni ◽  
Rajesh Ganesan ◽  
Lance Sherry
Keyword(s):  
Dynamic Programming ◽  
Traffic Control ◽  
Programming Approach ◽  
Programming Technique ◽  
Control Center ◽  
Traffic Demand ◽  
Dynamic Programming Approach ◽  
Dynamic Airspace Configuration ◽  
Maximum Benefit ◽  
Ground Delay

On the basis of weather and high traffic, the Next Generation Air Transportation System envisions an airspace that is adaptable, flexible, controller friendly, and dynamic. Sector geometries, developed with average traffic patterns, have remained structurally static with occasional changes in geometry due to limited forming of sectors. Dynamic airspace configuration aims at migrating from a rigid to a more flexible airspace structure. Efficient management of airspace capacity is important to ensure safe and systematic operation of the U.S. National Airspace System and maximum benefit to stakeholders. The primary initiative is to strike a balance between airspace capacity and air traffic demand. Imbalances in capacity and demand are resolved by initiatives such as the ground delay program and rerouting, often resulting in systemwide delays. This paper, a proof of concept for the dynamic programming approach to dynamic airspace configuration by static forming of sectors, addresses static forming of sectors by partitioning airspace according to controller workload. The paper applies the dynamic programming technique to generate sectors in the Fort Worth, Texas, Air Route Traffic Control Center; compares it with current sectors; and lays a foundation for future work. Initial results of the dynamic programming methodology are promising in terms of sector shapes and the number of sectors that are comparable to current operations.

scholarly journals Optimizing the Multi-Objective Deployment Problem of Mlat System

2018 ◽  
Vol 200 ◽  
pp. 00014 ◽  
Author(s):  
Rabie Fadil ◽  
Badr Abou El Majd ◽  
Hassan El Ghazi ◽  
Hicham Rahil
Keyword(s):  
Minimum Cost ◽  
Air Traffic ◽  
Time Data ◽  
Nsga Ii ◽  
Coverage Area ◽  
Central Processing ◽  
Regulatory Standards ◽  
Minimum Number ◽  
And Performance ◽  
Processing Subsystem

Multilateration (MLAT) systems are powerful means for air traffic surveillance. These systems aim to extract, and display to air traffic controllers identification of aircrafts or vehicles equipped with a transponder. They provide an accurate and real-time data without human intervention using a number of ground receiving stations, placed in some strategic locations around the coverage area, and they are connected with a Central Processing Subsystem (CPS) to compute the target (i.e., aircraft or vehicle) position. The MLAT performance strongly depends on system layout design which consists on deploying the minimum number of stations, in order to obtain the requested system coverage and performance, meeting all the regulatory standards with a minimum cost. In general, choosing the number of stations and their locations to cope with all the requirements is not an obvious task and the system designer has to make several attempts, by trial and error, before obtaining a satisfactory spatial distribution of the stations. In this work we propose a new approach to solve the deployment of Mlat stations problem by focusing on the number of deployed stations and the coverage as the main objectives to optimize. The Non-dominated Sorting Genetic Algorithm II(NSGA-II) was used in order to minimize the total number of stations required to identify all targets in a given area, with the aim to minimize the deployment cost, accelerating processes, and achieve high availability and reliability. The proposed approach is more efficient and converge rapidly which makes it ideal for our research involving optimal deployment of Mlat station.

Comparison of Artificial Intelligence-Based Solutions Applied to Economic Load Dispatch Problem

2020 ◽  
pp. 210-229
Author(s):  
Sarika Shrivastava ◽  
Piush Kumar
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Power Systems ◽  
Minimum Cost ◽  
Electric Power System ◽  
Optimization Techniques ◽  
Economic Load Dispatch ◽  
Practical Applications ◽  
Soft Computing Techniques ◽  
Loss Coefficients

The electric power system network is rapidly becoming more and more complex to meet energy requirements. With the development of integrated power systems, it becomes all the more necessary to operate the plant units most economically. More recently, soft computing techniques have received more attention and have been used in a number of successful and practical applications. In the chapter, artificial intelligence-based modern optimization techniques, the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), are used to solve the economic load dispatch related problems. In the chapter, the minimum cost is computed by adopting the genetic algorithm, PSO, and DE using the data from 15 generating units. Data has been taken from the published works containing loss coefficients are also given with the maximum-minimum power limits and cost function. All the techniques are implemented in MATLAB environment. Comparing the results obtained from GA, DE, and PSO-based method, better convergence was found in the PSO-based approach.

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