scholarly journals An Adaptive Variable Neighborhood Search Ant Colony Algorithm for Vehicle Routing Problem With Soft Time Windows

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 21258-21266
Author(s):  
Meiling He ◽  
Zhixiu Wei ◽  
Xiaohui Wu ◽  
Yongtao Peng
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Ant Colony Algorithm ◽  
Time Windows ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Soft Time Windows

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

Optimization of Multi-Depot Heterogeneous Incident Vehicle Routing Problem with Soft Time Windows Based on an Improved Ant Colony Optimization

2014 ◽  
Vol 1061-1062 ◽  
pp. 1108-1117
Author(s):  
Ya Lian Tang ◽  
Yan Guang Cai ◽  
Qi Jiang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Ant Colony ◽  
Genetic Operators ◽  
Solution Quality ◽  
Routing Problem ◽  
Soft Time Windows ◽  
Heterogeneous Vehicle Routing Problem

Aiming at vehicle routing problem (VRP) with many extended features is widely used in actual life, multi-depot heterogeneous vehicle routing problem with soft time windows (MDHIVRPSTW) mathematical model is established. An improved ant colony optimization (IACO) is proposed for solving this model. Firstly, MDHIVRPSTW was transferred into different groups according to nearest depot method, then constructing the initial route by scanning algorithm (SA). Secondly, genetic operators were introduced, and then adjusting crossover probability and mutation probability adaptively in order to improve the global search ability of the algorithm. Moreover, smooth mechanism was used to improve the performance of ant colony optimization (ACO). Finally, 3-opt strategy was used to improve the local search ability. The proposed IACO has been tested on a 32-customer instance which was generated randomly. The experimental results show that IACO is superior to other three algorithms in terms of convergence speed and solution quality, thus the proposed method is effective and feasible, and the proposed model is better than conventional model.

scholarly journals Pengembangan Algoritma Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS) untuk penyelesaian kasus Vehicle Routing Problem with Time Windows (VRPTW)

2017 ◽  
Vol 6 (1) ◽  
pp. 49
Author(s):  
Titi Iswari
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Heuristic Method ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Time Required

<p><em>Determining the vehicle routing is one of the important components in existing logistics systems. It is because the vehicle route problem has some effect on transportation costs and time required in the logistics system. In determining the vehicle routes, there are some restrictions faced, such as the maximum capacity of the vehicle and a time limit in which depot or customer has a limited or spesific opening hours (time windows). This problem referred to Vehicle Routing Problem with Time Windows (VRPTW). To solve the VRPTW, this study developed a meta-heuristic method called Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS). HRSA-VNS algorithm is a modification of Simulated Annealing algorithm by adding a restart strategy and using the VNS algorithm scheme in the stage of finding neighborhood solutions (neighborhood search phase). Testing the performance of HRSA-VNS algorithm is done by comparing the results of the algorithm to the Best Known Solution (BKS) and the usual SA algorithm without modification. From the results obtained, it is known that the algorithm perform well enough in resolving the VRPTW case with the average differences are -2.0% with BKS from Solomon website, 1.83% with BKS from Alvarenga, and -2.2% with usual SA algorithm without any modifications.</em></p><p><em>Keywords : vehicle routing problem, time windows, simulated annealing, VNS, restart</em></p>

A Improved Pareto of Ant Colony Algorithm to Solve the Vehicle Routing Problem with Time Windows

2014 ◽  
Vol 1030-1032 ◽  
pp. 1941-1944
Author(s):  
Hong Dou Zhang ◽  
Ning Guo ◽  
Jian Lin Mao ◽  
Hai Feng Wang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Time Windows ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimum ◽  
Routing Problem ◽  
Important Extension

Vehicle routing problem with time Windows (VRPTW) that is a kind of important extension type for VPR. In view of problem which the ant colony algorithm in solving VRPTW easily plunged into local optimum , this paper defines a new ant transition probability of saving ideas, and uses the Pareto optimal solution set of global pheromone updating rule, and puts forward a kind of improved Pareto ant colony algorithm (IPACA) . Through the simulation experiments show that IPACA improves the global search ability of ACA, effectively avoids the algorithm falls into local optimum, and reduces the total distribution cost (distance), so as to verify the effectiveness of the proposed algorithm.

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