Combination of Genetic and Random Restart Hill Climbing Algorithms for Vehicle Routing Problem

In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

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A Late Acceptance Hill-Climbing Heuristic Algorithm for the Double Vehicle Routing Problem with Multiple Stacks and Heterogeneous Demand

Advances in Intelligent Systems and Computing - Intelligent Systems Design and Applications ◽

10.1007/978-3-030-16657-1_71 ◽

2019 ◽

pp. 761-771

Author(s):

André L. S. Souza ◽

Jonatas B. C. Chagas ◽

Puca H. V. Penna ◽

Marcone J. F. Souza

Keyword(s):

Vehicle Routing ◽

Heuristic Algorithm ◽

Vehicle Routing Problem ◽

Hill Climbing ◽

Routing Problem ◽

Heterogeneous Demand ◽

Late Acceptance Hill Climbing

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Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

Computer Optics ◽

10.18287/2412-6179-co-732 ◽

2021 ◽

Vol 45 (1) ◽

pp. 154-160

Author(s):

H.H. Li ◽

H.R. Fu ◽

W.H. Li

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Time Window ◽

Hill Climbing ◽

Network Data ◽

Traffic Network ◽

Dynamic Vehicle Routing ◽

Routing Problem ◽

Total Cost ◽

Ga Algorithm

With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.

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Improved Genetic Algorithm for Capacitated Vehicle Routing Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1459 ◽

2012 ◽

Vol 253-255 ◽

pp. 1459-1462

Author(s):

Chun Yu Ren

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Hill Climbing ◽

Test Problems ◽

Improved Genetic Algorithm ◽

Routing Problem ◽

Capacitated Vehicle Routing Problem ◽

Searching Ability ◽

Capacitated Vehicle

This paper studies capacitated 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 combined hill-climbing algorithm can strengthen the partial searching ability of chromosome. Finally, comparing to a set of standard test problems, simulation results demonstrate the effectiveness and good quality.

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