scholarly journals Selective Simulated Annealing for Large Scale Airspace Congestion Mitigation

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 288
Author(s):  
Julien Lavandier ◽  
Arianit Islami ◽  
Daniel Delahaye ◽  
Supatcha Chaimatanan ◽  
Amir Abecassis
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Assignment Problem ◽  
Large Scale ◽  
Optimization Problem ◽  
Traffic Assignment ◽  
Combinatorial Optimization Problem ◽  
Slot Allocation ◽  
Traffic Assignment Problem ◽  
Aircraft Trajectories

This paper presents a methodology to minimize the airspace congestion of aircraft trajectories based on slot allocation techniques. The traffic assignment problem is modeled as a combinatorial optimization problem for which a selective simulated annealing has been developed. Based on the congestion encountered by each aircraft in the airspace, this metaheuristic selects and changes the time of departure of the most critical flights in order to target the most relevant aircraft. The main objective of this approach is to minimize the aircraft speed vector disorder. The proposed algorithm was implemented and tested on simulated trajectories generated with real flight plans on a day of traffic over French airspace with 8800 flights.

Optimizing Gate Assignments at Airport

2014 ◽  
Vol 556-562 ◽  
pp. 4178-4184
Author(s):  
Pan Zheng ◽  
Jing Li ◽  
Ying Hui Liang
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Objective Function ◽  
Assignment Problem ◽  
Optimization Problem ◽  
High Efficiency ◽  
Heuristic Method ◽  
Combinatorial Optimization Problem ◽  
The Core ◽  
Gate Assignment

Airport gate assignment is to appoint a gate for the arrival or leave flight and to ensure that the flight is on schedule. Assigning the airport gate with high efficiency is a key task among the airport ground busywork. As the core of airport operation, aircraft gate assignment is known as a kind of complicated combinatorial optimization problem. In this paper, we consider the over-constrained Airport Gate Assignment Problem where the number of flights exceeds the number of gates available, and where the objective is to minimize the overall variance of slack time (OVST). According to the intrinsic characteristics of the objective function itself, we design a meta-heuristic method and simulated annealing to solve the problem. Finally, the illustrative examples show the validity of the proposed approach.

Matched Field Processing Based on the Simulated Annealing

2013 ◽  
Vol 651 ◽  
pp. 879-884
Author(s):  
Qi Wang ◽  
Ying Min Wang ◽  
Yan Ni Gou
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Mediterranean Sea ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Combinatorial Optimization Problem ◽  
Matched Field Processing ◽  
Combinatorial Optimization Problems ◽  
Passive Localization

The matched field processing (MFP) for localization usually needs to match all the replica fields in the observation sea with the received fields, and then find the maximum peaks in the matched results, so how to find the maximum in the results effectively and quickly is a problem. As known the classical simulated annealing (CSA) which has the global optimization capability is used widely for combinatorial optimization problems. For passive localization the position of the source can be recognized as a combinatorial optimization problem about range and depth, so a new matched field processing based on CSA is proposed. In order to evaluate the performance of this method, the normal mode was used to calculate the replica field. Finally the algorithm was evaluated by the dataset in the Mediterranean Sea in 1994. Comparing to the conventional matched field passive localization (CMFP), it can be conclude that the new one can localize optimum peak successfully where the output power of CMFP is maximum, meanwhile it is faster than CMFP.

scholarly journals Analysis of flow shop problems and their algorithms

2009 ◽  
Vol 50 ◽  
Author(s):  
Lina Rajeckaitė ◽  
Narimantas Listopadskis
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Flow Shop ◽  
Exact Algorithm ◽  
Combinatorial Optimization Problem ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

The combinatorial optimization problem considered in this paper is flow shop scheduling problem arising in logistics, management, business, manufacture and etc. A set of machines and a set of jobs are given. Each job consists of a set of operations. Machines are working with unavailability intervals. The task is to minimize makespan, i.e. the overall length of the schedule. There is overview of combinatorial optimization, scheduling problems and methods used to solve them. There is also presented and realized one exact algorithm – Branch and Bound, and two meta-heuristics: Simulated Annealing and Tabu Search. Analysis of these three algorithms is made.

Correction of measured multiplicity distributions by the microcanonical algorithm

2014 ◽  
Vol 25 (03) ◽  
pp. 1350094
Author(s):  
M. Hafidouni
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Cost Function ◽  
Optimization Problem ◽  
Maximum Entropy Principle ◽  
Combinatorial Optimization Problem ◽  
Stochastic Algorithm ◽  
Entropy Principle ◽  
Multiplicity Distributions ◽  
Annealing Method

Correction of observed multiplicity distributions by a method based on the maximum entropy principle reduces to a combinatorial optimization problem, where one has to determine a combination of parameters which would minimize a given (cost) function. We use a stochastic algorithm, i.e. an annealed version of the microcanonical algorithm to minimize this function. This algorithm yields results which are compatible with those from simulated annealing method.

scholarly journals A Hyperheuristic for the Dial-a-Ride Problem with Time Windows

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Enrique Urra ◽  
Claudio Cubillos ◽  
Daniel Cabrera-Paniagua
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Time Windows ◽  
Combinatorial Optimization Problem ◽  
Heuristic Methods ◽  
Problem Space ◽  
Abstraction Level ◽  
Transportation Schedule

The dial-a-ride problem with time windows (DARPTW) is a combinatorial optimization problem related to transportation, in which a set of customers must be picked up from an origin location and they have to be delivered to a destination location. A transportation schedule must be constructed for a set of available vehicles, and several constraints have to be considered, particularly time windows, which define an upper and lower time bound for each customer request in which a vehicle must arrive to perform the service. Because of the complexity of DARPTW, a number of algorithms have been proposed for solving the problem, mainly based on metaheuristics such as Genetic Algorithms and Simulated Annealing. In this work, a different approach for solving DARPTW is proposed, designed, and evaluated: hyperheuristics, which are alternative heuristic methods that operate at a higher abstraction level than metaheuristics, because rather than searching in the problem space directly, they search in a space of low-level heuristics to find the best strategy through which good solutions can be found. Although the proposed hyperheuristic uses simple and easy-to-implement operators, the experimental results demonstrate efficient and competitive performance on DARPTW when compared to other metaheuristics from the literature.

scholarly journals Finding shortest path in static networks

2012 ◽  
Vol 1 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Seher Abbasi ◽  
Fahimeh Moosavi
Keyword(s):  
Combinatorial Optimization ◽  
Shortest Path ◽  
Large Scale ◽  
Optimization Problem ◽  
Rare Event ◽  
Combinatorial Optimization Problem ◽  
Time Saving ◽  
Large Scale Network ◽  
Scale Network ◽  
Modified Algorithm

This paper considers the problem of finding the shortest path in a static network, where the costs are constant. The CE Algorithm based strategy that is presented by Rubinstein to solving rare event and combinatorial optimization problem is modified to finding shortest path in this research. To analyze the efficiency of the used algorithm three sets of small, medium and large sized problems that generated randomly are solved. The results on the set of problems show that the modified algorithm produces good solutions and time saving in computation of large-scale network.

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