Solving the traveling salesman problem using simulated annealing

Markov Chain Anticipation for the Online Traveling Salesman Problem by Simulated Annealing Algorithm

The arc costs are assumed to be online parameters of the network and decisions should be made while the costs of arcs are not known. The policies determine the permitted nodes and arcs to traverse and they are generally defined according to the departure nodes of the current policy nodes. In on-line created tours arc costs are not available for decision makers. The on-line traversed nodes are f...

Solving Traveling Salesman Problem Using Parallel Genetic Algorithm and Simulated Annealing

The traveling salesman problem (TSP) is to find a tour of a given number of cities (visiting each city exactly once and returning to the starting city) where the length of this tour is minimized. In this project, we implemented solutions to the traveling salesman problem using parallel genetic algorithm and simulated annealing, and compared the parallel performance and results of these two impl...

Simulated Annealing applied to the Traveling Salesman Problem

An Approach for Solving Traveling Salesman Problem

In this paper, we introduce a new approach for solving the traveling salesman problems (TSP) and provide a solution algorithm for a variant of this problem. The concept of the proposed method is based on the Hungarian algorithm, which has been used to solve an assignment problem for reaching an optimal solution. We introduced a new fittest criterion for crossing over such problems, and illu...

Solving Bus Terminal Location Problem Using Simulated Annealing Method

Bus network design is an important problem in public transportation. A main step to this design is determining the number of required terminals and their locations. This is a special type of facility location problem, which is a time-consuming, large scale, combinatorial problem. In a previous attempt by the authors, this problem had been solved by GAMS, based on a branch and bound algorithm.&...