Parallel Ant Colony Optimization for the Electric Vehicle Routing Problem (original) (raw)
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2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
Freight transportation is evolving with the development of electric vehicle to carry out goods delivery rounds. In addition to the technological developments that make electric vehicles more and more suitable for long-distance traffic, the legislative framework also imposes these developments, such as the announced end of thermal vehicles in France in 2040. However, it is not possible to use an electric vehicle in the same way as an internal combustion vehicle. This technology impose to take into account the aging of the battery in order to define its use.
Electric Vehicle Fleet Management Using Ant Colony Optimisation
International Journal of Strategic Engineering
This research is focused on implementation of the ant colony optimisation (ACO) technique to solve an advanced version of the vehicle routing problem (VRP), called the fleet management system (FMS). An optimum solution of VRP can bring benefits for the fleet operators as well as contributing to the environment. Nowadays, particular considerations and modifications are needed to be applied in the existing FMS algorithms in response to the rapid growth of electric vehicles (EVs). For example, current FMS algorithms do not consider the limited range of EVs, their charging time or battery degradation. In this study, a new ACO-based FMS algorithm is developed for a fleet of EVs. A simulation platform is built in order to evaluate performance of the proposed FMS algorithm under different simulation case-studies. The simulation results are validated against a well-established method in the literature called nearest-neighbour technique. In each case-study, the overall mileage of the fleet i...
Ant_VRP: ant-colony-based meta-heuristic algorithm to solve the vehicle routing problem
International Journal of Advanced Intelligence Paradigms, 2018
Vehicle routing problem is one of the most important combinatorial optimisation problems and is very important for researchers and scientists today. In this kind of problems, the aim is to determine the minimum cost needed to move the vehicles, which start simultaneously from the warehouse and returned to it after visiting customers. There are two constraints for costumers and vehicles, first, each node must be visited by only one vehicle and second, each vehicle must not load more than its capacity. In this paper, a combination of ant colony algorithm and mutation operation named Ant_VRP is proposed to solve the vehicle routing problem. The performance of the algorithm is demonstrated by comparing with other heuristic and meta-heuristic approaches.
Ant Colony Optimisation for vehicle routing problems: from theory to applications
2004
Abstract Ant Colony Optimisation is a metaheuristic for combinatorial optimisation problems. In this paper we show its successful application to the Vehicle Routing Problem (VRP). First, we introduce VRP and its many variants, such as VRP with Time Windows, Time Dependent VRP, Dynamic VRP, VRP with Pickup and Delivery. These variants have been formulated in order to bring the VRP as close as possible to the kind of situations encountered in real-world distribution processes.
Review on Vehicle Routing Problem using Ant Colony Optimization
International Journal of Advanced Research in Computer Science, 2014
Vehicle routing problem (VRP) concerns the transport of items from a depot to its number of customers using group of vehicles. VRP needs method to solve problem in terms of best route to service the customers. The solution must ensure that all the customer s are served under the operational constraints and minimizing the overall cost. The solution can be obtained using one of the metaheuristic techniques Ant Colony Optimization (ACO). In Ant colony optimization a colony of artificial ants altogether find good solutions to difficult discrete optimization problems. It is not possible for each ant to find a solution to the problem under consideration which is probably a poor one, good-quality solutions can only emerge only if there is collective interaction among the ants. They act concurrently and independently and there, by their decision making process, finds the most efficient route. ACO helps in finding the best optimal path in vehicle routing as well. This paper presents a review...
A Benchmark Test Suite for the Electric Capacitated Vehicle Routing Problem
2020 IEEE Congress on Evolutionary Computation (CEC), 2020
Severa1 logistic companies started utilizing electric vehicles (EVs) in their daily operations to reduce greenhouse gas pollution. However, the limited driving range of EVs may require visits to recharging stations during their operation. These potential visits have to be addressed, avoiding unnecessary long detours. We formulate the electric capacitated vehicle routing problem (E-CVRP), which incorporates the possibility of EVs visiting a recharging station while satisfying the delivery demands of customers. The energy consumption of the EVs is proportional to their cargo load which is an important constraint in real-world logistics applications. A new set of benchmark instances is proposed for the E-CVRP. As solution methods to these new benchmarks, we apply the ant colony optimization metaheuristic method and an exact method. Experimental results on the ECVRP demonstrate the high complexity of the problem and the efficiency of the applied metaheuristic solution method.
An Ant Colony Algorithm for the Capacitated Vehicle Routing
Electronic Notes in Discrete Mathematics, 2004
The Vehicle Routing Problem (VRP) requires the determination of an optimal set of routes for a set of vehicles to serve a set of customers. We deal here with the Capacitated Vehicle Routing Problem (CVRP) where there is a maximum weight or volume that each vehicle can load. We developed an Ant Colony algorithm (ACO) for the CVRP based on the metaheuristic technique introduced by Colorni, Dorigo and Maniezzo. We present preliminary results that show that ant algorithms are competitive with other metaheuristics for solving CVRP.
Ant Colony Optimization for vehicle routing in advanced logistics systems
Proceedings of the International Workshop on Modelling and Applied Simulation (MAS 2003), 2003
Many distribution companies service their customers with non homogeneous fleets of trucks. Their problem is to find a set of routes minimising the number of travelled kilometres and the number of used vehicles, while satisfying customer demand. There are three major problems why traditional Operations Research techniques are not enough to deal with this problem, which is known as the Vehicle Routing Problem. First of all, it is inherently combinatorial, and exact algorithms fail when the dimension of the problem ( ...
Solving the Routing Problem by Ant Colony Optimization Algorithms
International Journal of Computing, 2016
The use of ant colony optimization algorithms for solving the routing problem in a process of products delivery taking into account a city transport infrastructure has shown in this research. The vehicle routing problem belongs to NP-hard task and its solution requires significant computing resources. Therefore, it is recommended to use metaheuristic methods to solve such problems including ant colony optimization algorithms. Solution of the Vehicle Routing Problem will cause a decrease of enterprises non-productive resources consumption and will promote the increase of their efficiency and competitiveness. The test example, consisting of eight consumers of freight and two transportation means with unlimited load capacity, moving around the certain city, is used for the implementation of the model. It can be further refined by taking into account various parameters besides transport infrastructure, including limitations on carrying capacity, a number of vehicles an working hours, an...