scholarly journals Vehicle Routing Problem with Soft Time Windows Based on Improved Genetic Algorithm for Fruits and Vegetables Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Peiqing Li ◽  
Jie He ◽  
Dunyong Zheng ◽  
Yongsheng Huang ◽  
Chenhao Fan
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Fruits And Vegetables ◽  
Time Windows ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Total Cost ◽  
Soft Time Windows ◽  
Distribution Process

Fresh fruits and vegetables, perishable by nature, are subject to additional deterioration and bruising in the distribution process due to vibration and shock caused by road irregularities. A nonlinear mathematical model was developed that considered not only the vehicle routing problem with time windows but also the effect of road irregularities on the bruising of fresh fruits and vegetables. The main objective of this work was to obtain the optimal distribution routes for fresh fruits and vegetables considering different road classes with the least amount of logistics costs. An improved genetic algorithm was used to solve the problem. A fruit delivery route among the 13 cities in Jiangsu Province was used as a real analysis case. The simulation results showed that the vehicle routing problem with time windows, considering road irregularities and different classes of toll roads, can significantly influence total delivery costs compared with traditional VRP models. The comparison between four models to predict the total cost and actual total cost in distribution showed that the improved genetic algorithm is superior to the Group-based pattern, CW pattern, and O-X type cross pattern.

scholarly journals An Improved Genetic Algorithm for Vehicle Routing Problem Pick-up and Delivery with Time Windows

2021 ◽  
Vol 22 (1) ◽  
pp. 1-17
Author(s):  
Muhammad Faisal Ibrahim ◽  
M.M Putri ◽  
D Farista ◽  
Dana Marsetiya Utama
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Closing Time ◽  
Routing Problem ◽  
Logistics Company ◽  
Fitness Value

Vehicle Routing Problem (VRP) has many applications in real systems, especially in distribution and transportation. The optimal determination of vehicle routes impacts increasing economic interests. This research aims to find the optimal solution in Vehicle Routing Problem Pick-up and Delivery with Time Windows (VRPPDTW).  Targets of this problem included reducing distance travel and penalties. Three penalties that were considered are a capacity penalty, opening time capacity, and closing time capacity. An improved genetic algorithm was developed and used to determine the vehicle route.  There were one main depot and 42 customers. This research raised the problem of a shipping and logistics company. Analysis of the results showed that the proposed route obtained from improved genetic algorithms (GA) was better than the existing route and previous algorithm. Besides, this research was carried out an analysis on the effect of the number of iterations on distance traveled, the number of penalties, and the fitness value. This algorithm could be applied in VRPPDTW and produces an optimal solution.

An Improved Genetic Algorithm for Multi-Depot Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery Time Windows

2015 ◽  
Vol 738-739 ◽  
pp. 361-365 ◽  
Author(s):  
Yan Guang Cai ◽  
Ya Lian Tang ◽  
Qi Jiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Pickup And Delivery ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Simultaneous Pickup And Delivery ◽  
Elite Selection

Multi-depot heterogeneous vehicle routing problem with simultaneous pickup and delivery and time windows (MDHVRPSPDTW) is an extension of vehicle routing problem (VRP), MDHVRPSPDTW mathematical model was established. The improved genetic algorithm (IGA) is proposed for solving the model. Firstly, MDHVRPSPDTW is transferred into different groups by the seed customer selecting method and scanning algorithm (SA).Secondly, IGA based on elite selection and inversion operator is used to solve the model, and then cutting merge strategy based on greedy thought and three kinds of neighborhood search methods is applied to optimize the feasible solutions further. Finally, 3-opt local search is applied to adjust the solution. The proposed IGA has been test on a random new numerical example.The computational results show that IGA is superior to branch and bound algorithm (BBD) by Lingo 9.0 in terms of optimum speed and solution quality, and the model and the proposed approach is effective and feasible.

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