A hybrid genetic algorithm for the multi-depot open vehicle routing problem

OR Spectrum ◽  
2012 ◽  
Vol 36 (2) ◽  
pp. 401-421 ◽  
Author(s):  
Ran Liu ◽  
Zhibin Jiang ◽  
Na Geng
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

Study on Hybrid Genetic Algorithm for Multi-Type Vehicle Open Vehicle Routing Problem

2011 ◽  
Vol 204-210 ◽  
pp. 1287-1290
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Crossover Operator ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Open Vehicle Routing

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.

Multi-depot open vehicle routing problem with fuzzy time windows

2021 ◽  
Vol 40 (1) ◽  
pp. 427-438
Author(s):  
Xiaolong Diao ◽  
Houming Fan ◽  
Xiaoxue Ren ◽  
Chuanying Liu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Service Time ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Genetic Algorithm ◽  
Routing Problem ◽  
Maximum Time ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

This paper presents one method and one hybrid genetic algorithm for multi-depot open vehicle routing problem with fuzzy time windows (MDOVRPFTW) without maximum time windows. For the method, the degree of customers’ willingness to accept goods (DCWAG) is firstly proposed, it’s one fuzzy vague and determines maximum time windows. Referring to methods to determine fuzzy membership function, the function between DCWAG and the starting service time is constructed. By setting an threshold for DCWAG, the starting service time that the threshold corresponds can be treated as the maximum time window, which meets the actual situation. The goal of the model is to minimize the total cost. For the algorithm, MDOVRPFTW without maximum time windows is an extension of the NP-hard problem, the hybrid genetic algorithm was designed, which is combination of genetic algorithm and Hungarian algorithm. When the hybrid genetic algorithm applied to one pharmaceutical logistics company in Beijing City, China, one optimal scheme is determined. Then the rationality and the stability of solutions by the hybrid genetic algorithm are proved. Finally, sensitivity analyses are performed to investigate the impact of someone factor on DCWAG and some suggestions are proposed.

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

2021 ◽  
pp. 403-425
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Samarjit Kar ◽  
Tanmay De
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Central Point ◽  
Routing Problem ◽  
Genetic Search ◽  
Routing Optimization ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

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.

Research on Improved Genetic Algorithm for Heterogeneous Open Vehicle Routing Problem

2011 ◽  
Vol 55-57 ◽  
pp. 859-862
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
The Individual ◽  
Searching Ability ◽  
Standard Genetic Algorithm ◽  
Open Vehicle Routing

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.

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