Computational intelligence approach to real-world cooperative vehicle dispatching problem

Hirota, Kaoru; Chen, Ke-Wei; Dong, Fangyan

doi:10.1109/is.2004.1344628

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the metaheuristic approaches are very efficient for escaping from local optimum, they are one of the best group algorithms for solving combinatorial optimization problems. Some of the most popular metaheuristics applied to the VRP are simulated annealing (SA) [11], genetic algorithm (GA) [12], tabu search (TS) [11], Computational Intelligence Approach [13], large neighborhood search [3], ant colony optimization (ACO) [14], hybrid ant colony optimization [15] and particle swarm optimization [16]. The VRP is intrinsically a multiple objective optimization problem (MOP) in nature that has received much attention because of its practical application in industrial and service problems [17].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

Yousefikhoshbakht¹,

Didehvar²,

Rahmati³

2014

INT J COMPUT COMMUN

View full text Add to dashboard Cite

The vehicle routing problem (VRP) is a well-known NP-Hard problem in operation research which has drawn enormous interest from many researchers during the last decades because of its vital role in planning of distribution systems and logistics. This article presents a modified version of the elite ant system (EAS) algorithm called HEAS for solving the VRP. The new version mixed with insert and swap algorithms utilizes an effective criterion for escaping from the local optimum points. In contrast to the classical EAS, the proposed algorithm uses only a global updating which will increase pheromone on the edges of the best (i.e. the shortest) route and will at the same time decrease the amount of pheromone on the edges of the worst (i.e. the longest) route. The proposed algorithm was tested using fourteen instances available from the literature and their results were compared with other well-known meta-heuristic algorithms. Results show that the suggested approach is quite effective as it provides solutions which are competitive with the best known algorithms in the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

Yousefikhoshbakht¹,

Didehvar²,

Rahmati³

2014

INT J COMPUT COMMUN

View full text Add to dashboard Cite

show abstract

“…Maria et al [4] defined an auxiliary network and utilized Tabu search to solve a combined location-routing problem. Kaoru Hirota et al [5] decomposed the problem into three layers: Atomic layer, Molecular layer and Individual layer. After the initial routes were constructed, trip moves and exchange operations were performed between tours in order to improve the quality of solutions.…”

Section: Introductionmentioning

confidence: 99%

An Improved PSO for the Multi-Depot Vehicle Routing Problem with Time Windows

Wen

Meng

2008

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application

View full text Add to dashboard Cite

The distribution of finished products from depots to customers is a practical and challenging problem in logistics management. Better routing and scheduling decisions can result in higher level of customer satisfaction. The distribution problem is generally formulated as the vehicle routing problem (VRP). Nevertheless, there is a rigid assumption that there is only one depot. In cases, for instance, where a logistics company has more than one depot, the VRP is not suitable. To resolve this limitation, this paper focuses on the VRP with multiple depots with time windows, (MDVRPTW). The MDVRP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To deal with the problem efficiently, improved PSO is developed in this paper. A computational study is carried out to verify the algorithms. It is proved that the performance of improved PSO can solve this problem efficiently. V V ).2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application 978-0-7695-3490-9/08 $25.00

show abstract

Particle swarm optimization for multi-depots single vehicle routing problem

Wang

2008

2008 Chinese Control and Decision Conference

View full text Add to dashboard Cite

Computational intelligence approach to real-world cooperative vehicle dispatching problem

Cited by 4 publications

References 11 publications

An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

An Improved PSO for the Multi-Depot Vehicle Routing Problem with Time Windows

Particle swarm optimization for multi-depots single vehicle routing problem

Contact Info

Product

Resources

About