scholarly journals Multicriteria airport gate assignment and Pareto simulated annealing

2008 ◽  
Vol 40 (4) ◽  
pp. 385-397 ◽  
Author(s):  
Andreas Drexl ◽  
Yury Nikulin
Keyword(s):  
Simulated Annealing ◽  
Gate Assignment ◽  
Pareto Simulated Annealing

scholarly journals Pareto simulated annealing for the design of experiments: illustrated by a gene expression study

2012 ◽  
Vol 37 (3) ◽  
pp. 199-221 ◽  
Author(s):  
Penny Sanchez ◽  
Gary Glonek ◽  
Andrew Metcalfe
Keyword(s):  
Gene Expression ◽  
Simulated Annealing ◽  
Minimum Cost ◽  
Optimal Designs ◽  
Expression Study ◽  
Expression Studies ◽  
Optimal Values ◽  
Gene Expression Studies ◽  
And Performance ◽  
Pareto Simulated Annealing

Abstract Experimental design is concerned with the problem of allocating resources within an experiment to ensure that objectives of the experiment are achieved at the minimum cost. This paper focuses on the generation of optimal or near-optimal designs for large and complex experiments where it is infeasible to carry out an ex- haustive search of the design space. Optimal designs for gene expression studies, aimed at investigating the behaviour of genes, are considered, where the optimality criterion employed is Pareto optimality. We develop an adaptation of the metaheuris- tic method of Pareto simulated annealing to generate an approximation to the set of Pareto optimal designs for large and complex experiments. We develop algorithms that utilise response surface methodology to search systematically for the optimal values of parameters associated with Pareto simulated annealing and performance is evaluated using quality measures.

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.

PARETO SIMULATED ANNEALING (SA)-BASED MULTI-OBJECTIVE OPTIMIZATION FOR MEMS DESIGN AND APPLICATION

2005 ◽  
Vol 15 (02) ◽  
pp. 455-460 ◽  
Author(s):  
ANDOJO ONGKODJOJO ONG ◽  
FRANCIS E. H. TAY
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Method ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Global Optimization Method ◽  
Micro Electro Mechanical Systems ◽  
Pareto Ranking ◽  
Pareto Simulated Annealing ◽  
Design And Application

In this paper we present a global optimization method for multiple objective functions using the Pareto Simulated Annealing (SA). This novel optimization method is very useful and promising for design and application in the field of Micro-Electro-Mechanical Systems (MEMS). Previously published global optimization method has been reported by us for only single objective function. The proposed method automatically assigns different objective weights to each objective functions so that it can generate multiple solutions simultaneously. It also offers the trade-off between the objective functions so that we will be able to select the most suitable solution for MEMS design and applications. Based on the global Pareto ranking of the solutions, the optimization method can provide the best solution (the first Pareto ranking) as well.

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