scholarly journals Exploring Link Covering and Node Covering Formulations of Detection Layout Problem

2012 ◽  
Vol 2308 (1) ◽  
pp. 17-26 ◽  
Author(s):  
J. Barceló ◽  
F. Gilliéron ◽  
M. P. Linares ◽  
O. Serch ◽  
L. Montero
Keyword(s):  
Layout Problem ◽  
Node Covering

scholarly journals A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

2021 ◽  
Vol 11 (15) ◽  
pp. 6676
Author(s):  
Lorenzo Salas-Morera ◽  
Laura García-Hernández ◽  
Carlos Carmona-Muñoz
Keyword(s):  
Coral Reef ◽  
Collaborative Design ◽  
Expert Knowledge ◽  
Fitness Function ◽  
Facility Layout ◽  
Layout Problem ◽  
New Strategy ◽  
Problem Instances ◽  
Expert Designer ◽  
First Time

The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function.

Applications of Multiple Objective Genetic Algorithms in the Optimization Design of Tracked Self-Moving Power’s Layout

2013 ◽  
Vol 307 ◽  
pp. 161-165
Author(s):  
Hai Jin ◽  
Jin Fa Xie
Keyword(s):  
Optimization Design ◽  
Pareto Front ◽  
Layout Optimization ◽  
Multiple Objective ◽  
Multi Objective Optimization ◽  
Layout Problem ◽  
Basic Principles ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Set Up

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.

A node covering algorithm

1977 ◽  
Vol 24 (2) ◽  
pp. 213-233 ◽  
Author(s):  
Egon Balas ◽  
Haakon Samuelsson
Keyword(s):  
Node Covering
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