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A NEW HEURISTIC ALGORITHM FOR MULTIPLE TRAVELING SALESMAN PROBLEM

Authors: NURİYEVA, F.; KİZİLATES, G.;

A NEW HEURISTIC ALGORITHM FOR MULTIPLE TRAVELING SALESMAN PROBLEM

Abstract

The Multiple Traveling Salesman Problem (mTSP) is a combinatorial optimization problem in NP-hard class. The mTSP aims to acquire the minimum cost for traveling a given set of cities by assigning each of them to a different salesman in order to create m number of tours. This paper presents a new heuristic algorithm based on the shortest path algorithm to find a solution for the mTSP. The proposed method has been programmed in C language and its performance analysis has been carried out on the library instances. The computational results show the efficiency of this method.

WOS: 000402669500011

Country
Turkey
Keywords

Graph theory, Multiple traveling salesman problem, graph theory, heuristic algorithms, Heuristic algorithms, multiple traveling salesman problem;heuristic algorithms;shortest path algorithm;insertion heuristic;graph theory., insertion heuristic, multiple traveling salesman problem, Shortest path algorithm, shortest path algorithm, Insertion heuristic

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green