scholarly journals A Hybrid Ant Colony Optimization for Dynamic Multidepot Vehicle Routing Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haitao Xu ◽  
Pan Pu ◽  
Feng Duan
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony ◽  
Optimization Approach ◽  
Dynamic Vehicle Routing ◽  
Routing Problem ◽  
Multiple Depots ◽  
Mutation Operation ◽  
Clustering Approach

In the real world, the vehicle routing problem (VRP) is dynamic and variable, so dynamic vehicle routing problem (DVRP) has obtained more and more attentions among researchers. Meanwhile, due to actual constraints of service hours and service distances, logistics companies usually build multiple depots to serve a great number of dispersed customers. Thus, the research of dynamic multidepot vehicle routing problem (DMDVRP) is significant and essential. However, it has not attracted much attention. In this paper, firstly, a clustering approach based on the nearest distance is proposed to allocate all customers to the depots. Then a hybrid ant colony optimization (HACO) with mutation operation and local interchange is introduced to optimize vehicle routes. In addition, in order to deal with dynamic problem of DMDVRP quickly, a real-time addition and optimization approach is designed to handle the new customer requests. Finally, the t-test is applied to evaluate the proposed algorithm; meanwhile the relations between degrees of dynamism (dod) and HACO are discussed minutely. Experimental results show that the HACO algorithm is feasible and efficient to solve DMDVRP.

scholarly journals Current State of Dynamic Vehicle Routing Problems Solved by Ant Colony Optimization Algorithm

2021 ◽  
Vol 15 (3) ◽  
pp. 429-434
Author(s):  
Luka Olivari ◽  
Goran Đukić
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Vehicle Routing Problem ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Dynamic Vehicle Routing ◽  
Routing Problems ◽  
Routing Problem ◽  
Solution Methods

Dynamic Vehicle Routing Problem is a more complex version of Vehicle Routing Problem, closer to the present, real-world problems. Heuristic methods are used to solve the problem as Vehicle Routing Problem is NP-hard. Among many different solution methods, the Ant Colony Optimization algorithm is proven to be the efficient solution when dealing with the dynamic version of the problem. Even though this problem is known to the scientific community for decades, the field is extremely active due to technological advancements and the current relevance of the problem. As various sub-types of routing problems and solution methods exist, there is a great number of possible problem-solution combinations and research directions. This paper aims to make a focused review of the current state in the field of Dynamic Vehicle Routing Problems solved by Ant Colony Optimization algorithm, to establish current trends in the field.

scholarly journals Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Haitao Xu ◽  
Pan Pu ◽  
Feng Duan
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Search Space ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Vehicle Routing ◽  
Routing Problem ◽  
High Level

As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP). Dynamic vehicle routing problem (DVRP) is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO), which is the traditional Ant Colony Optimization (ACO) fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.

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.

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