scholarly journals Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing

2015 ◽  
Vol 6 (3) ◽  
pp. 405-418 ◽  
Author(s):  
Masoud Rabbani ◽  
Seyed Mahmood Kazemi
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Dijkstra’S Algorithm ◽  
Center Problem ◽  
Dijkstra's Algorithm ◽  
Multiple Allocation

SOLVING THE UNCAPACITATED MULTIPLE ALLOCATION p-HUB CENTER PROBLEM BY GENETIC ALGORITHM

2006 ◽  
Vol 23 (04) ◽  
pp. 425-437 ◽  
Author(s):  
JOZEF KRATICA ◽  
ZORICA STANIMIROVIĆ
Keyword(s):  
Genetic Algorithm ◽  
Higher Dimensions ◽  
Computational Time ◽  
Computational Results ◽  
Center Problem ◽  
Genetic Operators ◽  
Binary Coding ◽  
All Solutions ◽  
Multiple Allocation ◽  
Problem Instances

In this paper we describe a genetic algorithm (GA) for the uncapacitated multiple allocation p-hub center problem (UMApHCP). Binary coding is used and genetic operators adapted to the problem are constructed and implemented in our GA. Computational results are presented for the standard hub instances from the literature. It can be seen that proposed GA approach reaches all solutions that are proved to be optimal so far. The solutions are obtained in a reasonable amount of computational time, even for problem instances of higher dimensions.

scholarly journals A Novel Approach for Determination of Reliability of Covering a Node from K Nodes

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1461
Author(s):  
Biljana Panić ◽  
Nataša Kontrec ◽  
Mirko Vujošević ◽  
Stefan Panić
Keyword(s):  
Dijkstra’S Algorithm ◽  
Center Problem ◽  
Stochastic Problem ◽  
Dijkstra's Algorithm ◽  
Novel Approach ◽  
Demand Node

In this paper, a stochastic problem of multicenter location on a graph was formulated through the modification of the existing p-center problem to determine the location of a given number of facilities, to maximize the reliability of supplying the system. The system is represented by a graph whose nodes are the locations of demand and the potential facilities, while the weights of the arcs represent the reliability, i.e., the probability that an appropriate branch is available. First, k locations of facilities are randomly determined. Using a modified Dijkstra’s algorithm, the elementary path of maximal reliability for every demand node is determined. Then, a graph of all of elementary paths for demand node is formed. Finally, a new algorithm for calculating the reliability of covering a node from k nodes (k—covering reliability) was formulated.

scholarly journals Railway timetabling: a maximum bottleneck path algorithm for finding an additional train path

2020 ◽  
Author(s):  
Fredrik Ljunggren ◽  
Kristian Persson ◽  
Anders Peterson ◽  
Christiane Schmidt
Keyword(s):  
Experimental Evaluation ◽  
Temporal Distance ◽  
Dijkstra’S Algorithm ◽  
Passenger Traffic ◽  
Dijkstra's Algorithm ◽  
Railway Timetabling

Abstract We present an algorithm to insert a train path in an existing railway timetable close to operation, when we want to affect the existing (passenger) traffic as little as possible. Thus, we consider all other trains as fixed, and aim for a resulting train path that maximizes the bottleneck robustness, that is, a train path that maximizes the temporal distance to neighboring trains in the timetable. Our algorithm is based on a graph formulation of the problem and uses a variant of Dijkstra’s algorithm. We present an extensive experimental evaluation of our algorithm for the Swedish railway stretch from Malmö to Hallsberg. Moreover, we analyze the size of our constructed graph.

Parallel recombinative simulated annealing: A genetic algorithm

1995 ◽  
Vol 21 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Samir W. Mahfoud ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing
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