An Ant Colony Algorithm for the Capacitated Vehicle Routing (original) (raw)
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An ant colony optimization system for the capacitated vehicle routing problem
Proceedings of the XXVI Iberian Latin- …, 2005
Amongst the many problems in logistics, there is the Capacitated Vehicle Routing Problem -CVRP. This problem is present in many daily activities such as garbage collection, mail delivery, school bus transportation. CVRP includes not only the optimization of a path, but many of them simultaneously, since a fleet of evenly-capacitated vehicles have to deliver goods to geographically-distributed customers with variable demand, travelling the least distance as possible. This paper presents the first results of an ongoing project, related to the implementation of an Ant Colony Algorithm for the CVRP. This heuristic method is inspired in the behavior of real ants in the search for food. We devised a two-level optimization scheme so as to accomplish both global and local optimization. We applied the system to several benchmark instances of CVRP and the results obtained so far were very promising. Further work will include more tests to find optimized running parameters as well as experiments with other instances.
An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem
Applied Intelligence, 2010
In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers. The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.
Ant Colony Optimization Using Different Heuristic Strategies for Capacitated Vehicle Routing Problem
IOP Conference Series: Materials Science and Engineering
Capacitated Vehicle Routing Problem (CVRP) is a variant of vehicle routing problem (VRP) in which vehicles with restricted capacities required to pickup or deliver at various locations. The main constraint in CVRP is to pickup or deliver the goods for the least cost without exceeding the vehicle capacity. Therefore, the main objective of this paper is to minimize the distance travelled by vehicles. Hence, this paper proposed to use Ant Colony Optimization (ACO) with different heuristic strategies to optimize the distance travelled by the vehicles while not exceeding the vehicle capacities. Swapping, reversion, and insertion are the heuristic strategies used to examine the efficiency of neighbour creations in ACO. Christofides data sets are utilized in this paper to experiment on the solution construction in ACO with different heuristic strategies. The results showed that the use of ACO is efficient using the swap, reverse and insert strategies for distance minimization but there are possibilities for the vehicle visiting the same customer more than once. Meanwhile ACO with random combination with swap, reverse and insert are capable to solve CVRP without any possibilities for the vehicle visiting the same customer more than once.
A Hybrid Ant Colony Optimization(HACO) for Solving Capacitated Vehicle Routing Problem(CVRP)
2012
The Capacitated Vehicle Routing Problem (CVRP) is a combinatorial optimization and nonlinear problem seeking to service a number of customers with a fleet of vehicles. CVRP is an important problem in the fields of transportation, distribution and logistics. Usually, the goal is delivering goods located at a central depot to customers who have placed orders. This transportation optimization problem is NP-hard, which means that the computational effort required to solve it increases exponentially with the problem size. To solve it in an acceptable time some stochastic algorithms are needed. Here we proposed the algorithm Hybrid Ant Colony Optimization(HACO) which takes the advantage of Simulated Annealing(SA) to solve CVRP
A Hybrid Ant Colony System Approach for the Capacitated Vehicle Routing Problem
2004
In this paper we propose a hybrid approach for solving the capacitated vehicle routing problem (CVRP). We combine an Ant Colony System (ACS) with a Savings algorithm and, then, we improve solutions by a local search heuristic. The CVRP is a class of well-known NP-hard combinatorial optimization problem, which can be formally defined as a complete graph G=(V,E) where V={0, ... ,n} is a set of vertices and E is a set of arcs [4]. The vertex {0} represents the depot and the other vertices represent customers. The cost of travel between vertices i and j is denoted d ij and represents the distance or the travel time. We assume that costs are symmetric(i.e. d ij = d ji ), and an unlimited fleet of identical vehicles, each of capacity Q>0, is available. Each customer i has a demand q i , with 0<q i ≤ Q. Each customer must be served by a single vehicle and no vehicle can serve a set of customers whose demand exceeds its capacity. The task is to find a set of vehicle routes of minimum cost, where each vehicle used leaves from and returns to the depot. In the following, we explain our algorithm then, we give results and conclusions.
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.
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...
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.
2012
The Capacitated Vehicle Routing problem (CVRP) is a combinatorial optimization and nonlinear problem seeking to service a number of customers with a fleet of vehicles. CVRP isan important problem in the fields of transportation, distribution and logistics . Usually, the goal is delivering goods located at a central depot to customers who have placed orders. This transportation optimization problem is NP-hard ,which means that the computational effort required to solve it increases exponentially with the problem size. To solve it in an acceptable time some stochastic algorithms are needed. Here we proposed the algorithm Hybrid Ant Colony Optimization(HACO) which takes the advantage of Simulated Annealing(SA) to solve CVRP Keywords— CVRP,ACO ,SA,HACO.