A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem

Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Samarjit Kar ◽  
Tanmay De

This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.

2019 ◽  
Vol 6 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Samarjit Kar ◽  
Tanmay De

This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.


2011 ◽  
Vol 55-57 ◽  
pp. 859-862
Author(s):  
Chun Yu Ren

This paper studies heterogeneous open vehicle routing problem. Since the standard genetic algorithm is short of convergent speed and partial searching ability as well as easily premature, improved genetic algorithm is then adopted as an optimized solution. Firstly, sequence of real numbers coding is used to simplify the problem; it may construct the initial solution pertinently in order to improve the feasibility. The individual amount control choice strategy can guard the diversity of group. The adopting some arithmetic crossover operator can enhance local search ability of the chromosome. Finally, comparing to standard genetic algorithm, simulation results demonstrate the effectiveness and good quality.


2011 ◽  
Vol 204-210 ◽  
pp. 1287-1290
Author(s):  
Chun Yu Ren

Multi-type vehicle open vehicle routing problem is logistics optimization indispensable part. Hybrid genetic algorithm is used to optimize the solution. Firstly, use sequence of real numbers coding so as to simplify the problem; Construct the targeted initial solution to improve the feasibility; adopt some arithmetic crossover operator to enhance whole search ability of the chromosome. Secondly, Boltzmann simulated annealing mechanism for control genetic algorithm crossover and mutation operations improve the convergence speed and search efficiency. Finally, comparing to standard genetic algorithm, simulation results demonstrate the effectiveness and good quality.


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