Time-Dependent Travelling Salesman Problem

OPSEARCH ◽  
2005 ◽  
Vol 42 (3) ◽  
pp. 199-227 ◽  
Author(s):  
V. Bhavani ◽  
M. Sundara Murthy
Keyword(s):  
Travelling Salesman Problem ◽  
Time Dependent ◽  
Travelling Salesman

Analysis and Branch-and-Cut Algorithm for the Time-Dependent Travelling Salesman Problem

2014 ◽  
Vol 48 (1) ◽  
pp. 46-58 ◽  
Author(s):  
Jean-François Cordeau ◽  
Gianpaolo Ghiani ◽  
Emanuela Guerriero
Keyword(s):  
Travelling Salesman Problem ◽  
Branch And Cut ◽  
Time Dependent ◽  
Travelling Salesman

Facets and valid inequalities for the time-dependent travelling salesman problem

2014 ◽  
Vol 236 (3) ◽  
pp. 891-902 ◽  
Author(s):  
Juan José Miranda-Bront ◽  
Isabel Méndez-Díaz ◽  
Paula Zabala
Keyword(s):  
Valid Inequalities ◽  
Travelling Salesman Problem ◽  
Time Dependent ◽  
Travelling Salesman

An enhanced lower bound for the Time-Dependent Travelling Salesman Problem

2020 ◽  
Vol 113 ◽  
pp. 104795 ◽  
Author(s):  
Tommaso Adamo ◽  
Gianpaolo Ghiani ◽  
Emanuela Guerriero
Keyword(s):  
Lower Bound ◽  
Travelling Salesman Problem ◽  
Time Dependent ◽  
Travelling Salesman

scholarly journals Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 340
Author(s):  
Gianpaolo Ghiani ◽  
Tommaso Adamo ◽  
Pierpaolo Greco ◽  
Emanuela Guerriero
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Computing Time ◽  
Time Dependent ◽  
Machine Learning Techniques ◽  
Travelling Salesman ◽  
Distribution Management ◽  
Learning Techniques ◽  
Average Improvement

In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5% for London, and about 4% for Paris.

A branch-and-bound algorithm for the time-dependent travelling salesman problem

Networks ◽  
2018 ◽  
Vol 72 (3) ◽  
pp. 382-392 ◽  
Author(s):  
Anna Arigliano ◽  
Tobia Calogiuri ◽  
Gianpaolo Ghiani ◽  
Emanuela Guerriero
Keyword(s):  
Branch And Bound ◽  
Travelling Salesman Problem ◽  
Branch And Bound Algorithm ◽  
Time Dependent ◽  
Travelling Salesman

scholarly journals Can the Agent with Limited Information Solve Travelling Salesman Problem?

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
The Other ◽  
Time Dependent ◽  
Critical Properties ◽  
Limited Information ◽  
Travelling Salesman ◽  
Benchmark Data ◽  
Benchmark Datasets

Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems.

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