An Improved Ant Colony Optimization for the Vehicle Routing Problem in Logistics Distribution

2010 ◽  
Vol 121-122 ◽  
pp. 1006-1011
Author(s):  
Cheng Ming Qi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Distribution Systems ◽  
Transition Probability ◽  
Ant Colony ◽  
Logistic Distribution ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Local Optimum ◽  
Routing Problem

The routing of a fleet of vehicles to service a set of customers is important in logistic distribution systems. The main objective of Vehicle routing problem (VRP) is to minimize the total required fleet size for serving all customers. Secondary objectives are to minimize the total distance traveled or to minimize the total route duration of all vehicles. In this paper, we present a hybrid ant colony System, named PACS, coupled with a pareto local search (PLS) algorithm and apply to the VRP and its variant, the VRP with Time Windows (VRPTW). The algorithm only chooses partial customers randomly to compute the transition probability and PLS can help to escape local optimum. Experiments on various aspects of the algorithm and computational results for some benchmark problems are reported. We compare our approach with some classic, powerful meta-heuristics and show that the proposed approach can obtain the better quality of the solutions.

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.

The Research on VRP Based on Max-Min Ant Colony Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 1285-1288 ◽  
Author(s):  
Chang Min Chen ◽  
Wei Cheng Xie ◽  
Song Song Fan
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Ant Colony ◽  
Local Optimum ◽  
Simulation Data ◽  
Routing Problem ◽  
Np Hard Problem ◽  
The Cost

Vehicle routing problem (VRP) is the key to reducing the cost of logistics, and also an NP-hard problem. Ant colony algorithm is a very effective method to solve the VRP, but it is easy to fall into local optimum and has a long search time. In order to overcome its shortcomings, max-min ant colony algorithm is adopted in this paper, and its simulation system is designed in GUI of MATLAB7.0. The results show that the vehicle routing problem can well achieves the optimization of VRP by accessing the simulation data of database.

Ant Colony Optimisation Model for Vehicle Routing Problem With Simultaneous Pickup and Delivery

2016 ◽  
Author(s):  
Robin Scanlon ◽  
Qing Wang ◽  
Jie Wang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Complex Problem ◽  
Ant Colony ◽  
Benchmark Problems ◽  
Pickup And Delivery ◽  
Ant Colony Optimisation ◽  
Routing Problem ◽  
Route Length ◽  
Simultaneous Pickup And Delivery

Reverse logistics is an area that has come under increased scrutiny in recent years as legislators and companies try to increase the amount of goods that businesses reuse and recycle. The vehicle routing problem with simultaneous pickup and delivery arises when firms want to reduce handling costs by dealing with deliveries and returns in one operation. This is a complex problem for planners who aim to minimise the vehicle route length as the vehicle load rises and falls during a tour of facilities. This paper investigates the use of Ant Colony Optimisation to find solutions to this problem. An algorithm combining elements of three different studies is proposed. The algorithm finds results within 0.2% of the best known results and performs well for half of the benchmark problems, but needs further work to reach the same level on the other half. It is found that the proposed changes can have up to a 3.1% improvement in results when compared to previous methods run on this algorithm.

The Application of a Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem

2014 ◽  
Vol 556-562 ◽  
pp. 4693-4696
Author(s):  
Yue Li Li ◽  
Ai Hua Ren
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony System ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
Constrained Vehicle Routing ◽  
Capacity Constrained

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.

Sign in / Sign up

Export Citation Format

Share Document