Research on Open Vehicle Routing Problem with Time Windows Based on Improved Genetic Algorithm

2009 ◽  
Author(s):  
Guiyun Li
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

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.

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.

scholarly journals Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading

2018 ◽  
Vol 15 (9) ◽  
pp. 2025 ◽  
Author(s):  
Ling Shen ◽  
Fengming Tao ◽  
Songyi Wang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Carbon Emission ◽  
Time Windows ◽  
Carbon Trading ◽  
Low Carbon ◽  
Trading Costs ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

In order to cut the costs of third-party logistics companies and respond to the Chinese government’s low-carbon economy plans, this paper studies the more practical and complex open vehicle routing problem, which considers low-carbon trading policies. A low-carbon multi-depot open vehicle routing problem with time windows (MDOVRPTW) model is constructed with minimum total costs, which include the driver’s salary, penalty costs, fuel costs and carbon emissions trading costs. Then, a two-phase algorithm is proposed to handle the model. In the first phase, the initial local solution is obtained with particle swarm optimization (PSO); in the second phase, we can obtain a global optimal solution through a further tabu search (TS). Experiments proved that the proposed algorithm is more suitable for small-scale cases. Furthermore, a series of experiments with different values of carbon prices and carbon quotas are conducted. The results of the study indicate that, as carbon trading prices and carbon quotas change, total costs, carbon emission trading costs and carbon emissions are affected accordingly. Based on these academic results, this paper presents some effective proposals for the government’s carbon trading policy-making and also for logistics companies to have better route planning under carbon emission constraints.

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