scholarly journals Ant Colony Algorithms For The Vehicle Routing Problem With Time Window, Period And Multiple Depots

2021 ◽  
Author(s):  
Anita Agárdi ◽  
László Kovács ◽  
Tamás Bányai
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Ant Colony ◽  
Routing Problem ◽  
Window Period ◽  
Multiple Depots ◽  
Ant Colony Algorithms

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.

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 Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
Phan Nguyen Ky Phuc ◽  
Nguyen Le Phuong Thao
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Service Providers ◽  
Time Window ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Delivery Vehicle ◽  
Routing Problem

This study focuses on solving the vehicle routing problem (VRP) of E-logistics service providers. In our problem, each vehicle must visit some pick up nodes first, for instance, warehouses to pick up the orders then makes deliveries for customers in the list. Each pickup node has its own list of more than one customers requiring delivery. The objective is to minimize the total travelling cost while real-world application constraints, such as heterogeneous vehicles, capacity limits, time window, driver working duration, etc. are still considered. This research firstly proposes a mathematical model for this multiple pickup and multiple delivery vehicle routing problem with time window and heterogeneous fleets (MPMDVRPTWHF). In the next step, the ant colony optimization algorithm is studied to solve the problem in the large-scale.

An ant colony optimization algorithm with time window constraints relaxation for solving the vehicle routing problem

2018 ◽  
pp. 49-62
Author(s):  
Ольга Эдуардовна Долгова ◽  
Владимир Викторович Пересветов
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Hybrid Method ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Decision Time ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Routing Problem ◽  
Time Window Constraints

Рассмотрена задача маршрутизации транспорта с ограничениями по временным окнам. Требовалось составить план доставки товара клиентам, построив маршруты движения идентичных транспортных средств так, чтобы общая длина пройденного пути была минимальной. Для решения задачи разработан гибридный алгоритм. Он состоит из методов построения исходных решений, муравьиного алгоритма и локального поиска. В муравьином алгоритме в процессе формирования маршрутов разрешается нарушение временных ограничений при условии добавления штрафа в целевую функцию. Предложенный метод показал высокую эффективность при решении задач кластерного типа и задач с долгосрочным горизонтом планирования. The purpose of this paper is to improve the performance of a hybrid method based on ant colony optimization (ACO) that finds approximate solutions of the vehicle routing problem with time windows (VRPTW). In order to solve this problem it is required to design a plan for goods delivery to the customers generating the routes of identical vehicles so that the total travelled distance is minimal. For the VRPTW solving, the hybrid method is developed in which a usage of trial solutions makes it possible to explore the most promising parts of the search space. The initial methods for solution construction, an ant colony optimization (ACO) algorithm and local search are proposed in the framework of the hybrid method. In the ACO algorithm, when generating the routes, it is allowed to violate the time window constraints. A method to restore the feasibility of solutions is implemented within the relaxation scheme under “returns in time” principle. Numerical results for solving all problems with 25, 50 and 100 customers from the Solomon test set are obtained. We provide the results on the time and deviation of the solution of these problems in comparison with the results of other authors. Some problems and their classes were solved much faster by the algorithm proposed in this paper. Relative deviations from optimal values of the objective function for the most complex tasks decrease with increasing decision time. The proposed approach can be considered to be an additional or an alternative algorithm for solving the cluster type and the long-term planning horizon problems of the VRPTW.

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