Minmax Regret Shortest Path

2008 ◽  
pp. 81-112
Author(s):  
Adam Kasperski
Keyword(s):  
Shortest Path ◽  
Minmax Regret

A fix‐and‐optimize heuristic for the minmax regret shortest path arborescence problem under interval uncertainty

2021 ◽  
Author(s):  
Iago A. Carvalho ◽  
Thiago F. Noronha ◽  
Christophe Duhamel ◽  
Luiz F. M. Vieira ◽  
Vinicius F. dos Santos
Keyword(s):  
Shortest Path ◽  
Interval Uncertainty ◽  
Minmax Regret

A minmax regret version of the time-dependent shortest path problem

2018 ◽  
Vol 270 (3) ◽  
pp. 968-981 ◽  
Author(s):  
Eduardo Conde ◽  
Marina Leal ◽  
Justo Puerto
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Time Dependent ◽  
Minmax Regret

scholarly journals Combinatorial two-stage minmax regret problems under interval uncertainty

2020 ◽  
Author(s):  
Marc Goerigk ◽  
Adam Kasperski ◽  
Paweł Zieliński
Keyword(s):  
Combinatorial Optimization ◽  
Shortest Path ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Interval Uncertainty ◽  
Combinatorial Optimization Problems ◽  
Second Stage ◽  
Minmax Regret ◽  
Uncertainty Set ◽  
Two Stages

AbstractIn this paper a class of combinatorial optimization problems is discussed. It is assumed that a feasible solution can be constructed in two stages. In the first stage the objective function costs are known while in the second stage they are uncertain and belong to an interval uncertainty set. In order to choose a solution, the minmax regret criterion is used. Some general properties of the problem are established and results for two particular problems, namely the shortest path and the selection problem, are shown.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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