An exact algorithm for the Traveling Salesman Problem with Deliveries and Collections

Networks ◽  
2003 ◽  
Vol 42 (1) ◽  
pp. 26-41 ◽  
Author(s):  
R. Baldacci ◽  
E. Hadjiconstantinou ◽  
A. Mingozzi
Keyword(s):  
Traveling Salesman Problem ◽  
Exact Algorithm ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem

scholarly journals A Multi Ant Colony Optimization Approach For The Traveling Salesman Problem

2021 ◽  
Vol Volume 34 - 2020 - Special... ◽  
Author(s):  
Mathurin SOH ◽  
Baudoin Nguimeya Tsofack ◽  
Clémentin Tayou Djamegni
Keyword(s):  
Traveling Salesman Problem ◽  
Execution Time ◽  
Exact Algorithm ◽  
Traveling Salesman ◽  
Optimization Approach ◽  
Solution Quality ◽  
New Approach ◽  
International Audience ◽  
The Traveling Salesman Problem

International audience In this paper, we propose a new approach to solving the Traveling Salesman Problem (TSP), for which no exact algorithm is known that allows to find a solution in polynomial time. The proposed approach is based on optimization by ants. It puts several colonies in competition for improved solutions (in execution time and solution quality) to large TSP instances, and allows to efficiently explore the range of possible solutions. The results of our experiments show that the approach leads to better results compared to other heuristics from the literature, especially in terms of the quality of solutions obtained and execution time.

Research of Features of the Combined Algorithm for Solving the Asymmetric Traveling Salesman Problem

2021 ◽  
Vol 27 (1) ◽  
pp. 3-8
Author(s):  
M. V. Ulyanov ◽  
◽  
M. I. Fomichev ◽  
◽  
◽  
...  
Keyword(s):  
Decision Tree ◽  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Exact Algorithm ◽  
Traveling Salesman ◽  
Branch And Bound Method ◽  
Asymmetric Traveling Salesman Problem ◽  
The Individual ◽  
The Traveling Salesman Problem ◽  
Combined Algorithm

The exact algorithm that implements the Branch and Boimd method with precomputed tour which is calculated by Lin-Kernighan-Helsgaun metaheuristic algorithm for solving the Traveling Salesman Problem is concerned here. Reducing the number of decision tree nodes, which are created by the Branches and Bound method, due to a "good" precomputed tour leads to the classical balancing dilemma of time costs. A tour that is close to optimal one takes time, even when the Lin-Kernighan-Helsgaun algorithm is used, however it reduces the working time of the Branch and Bound method. The problem of determining the scope of such a combined algorithm arises. In this article it is solved by using a special characteristic of the individual Traveling Salesman Problem — the number of changes tracing direction in the search decision tree generated by the Branch and Bound Method. The use of this characteristic allowed to divide individual tasks into three categories, for which, based on experimental data, recommendations of the combined algorithm usage are formulated. Based on the data obtained in a computational experiment (in range from 30 to 45), it is recommended to use a combined algorithm for category III problems starting with n = 36, and for category II problems starting with n = 42.

scholarly journals Parallelizing an exact algorithm for the traveling salesman problem

2017 ◽  
Vol 119 ◽  
pp. 97-102 ◽  
Author(s):  
Victor Vitalyevich Burkhovetskiy ◽  
Boris Yakovlevich Steinberg
Keyword(s):  
Traveling Salesman Problem ◽  
Exact Algorithm ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem

A New Heuristic Procedure To Solve The Traveling Salesman Problem

2007 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Paulo Henrique Siqueira ◽  
Sérgio Scheer ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Heuristic Procedure ◽  
The Traveling Salesman Problem

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

scholarly journals An Optimal Algorithm for the Traveling Salesman Problem with Time Windows

1995 ◽  
Vol 43 (2) ◽  
pp. 367-371 ◽  
Author(s):  
Yvan Dumas ◽  
Jacques Desrosiers ◽  
Eric Gelinas ◽  
Marius M. Solomon
Keyword(s):  
Traveling Salesman Problem ◽  
Optimal Algorithm ◽  
Time Windows ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem

scholarly journals Dynamic Shortest Paths Methods for the Time-Dependent TSP

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 21
Author(s):  
Christoph Hansknecht ◽  
Imke Joormann ◽  
Sebastian Stiller
Keyword(s):  
Column Generation ◽  
Traveling Salesman Problem ◽  
Shortest Path ◽  
Valid Inequalities ◽  
Shortest Paths ◽  
Traveling Salesman ◽  
Time Dependent ◽  
Full Generality ◽  
Branching Rule ◽  
The Traveling Salesman Problem

The time-dependent traveling salesman problem (TDTSP) asks for a shortest Hamiltonian tour in a directed graph where (asymmetric) arc-costs depend on the time the arc is entered. With traffic data abundantly available, methods to optimize routes with respect to time-dependent travel times are widely desired. This holds in particular for the traveling salesman problem, which is a corner stone of logistic planning. In this paper, we devise column-generation-based IP methods to solve the TDTSP in full generality, both for arc- and path-based formulations. The algorithmic key is a time-dependent shortest path problem, which arises from the pricing problem of the column generation and is of independent interest—namely, to find paths in a time-expanded graph that are acyclic in the underlying (non-expanded) graph. As this problem is computationally too costly, we price over the set of paths that contain no cycles of length k. In addition, we devise—tailored for the TDTSP—several families of valid inequalities, primal heuristics, a propagation method, and a branching rule. Combining these with the time-dependent shortest path pricing we provide—to our knowledge—the first elaborate method to solve the TDTSP in general and with fully general time-dependence. We also provide for results on complexity and approximability of the TDTSP. In computational experiments on randomly generated instances, we are able to solve the large majority of small instances (20 nodes) to optimality, while closing about two thirds of the remaining gap of the large instances (40 nodes) after one hour of computation.

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