TY - GEN

T1 - Vehicle routing problem using clustering algorithm by maximum neural networks

AU - Yoshiike, N.

AU - Takefuji, Y.

N1 - Publisher Copyright:
© 1999 IEEE.

PY - 1999

Y1 - 1999

N2 - The vehicle routing problem (VRP) is one of the well known optimization problems. It is used to minimize the total length of all routes of vehicles where each of the vehicle has a capacity constraint respectively. This paper proposes a self-organization neural network model for obtaining the best solution for VRP. Our method consists of two phases. In the first phase, the customers are grouped to several delivery areas for vehicles assignment by Maximum Neuron model. In the second phase, the TSP in each area is solved by Elastic net model proposed by Andrew et. al. The clustering algorithm used in the first phase is a Maximum Neuron model. Maximum Neuron model is one of the neural networks proposed by Hopfield that can minimize a cost function considering various constraints. In the second phase, Elastic net model is used to solve the problem and it can obtain good solutions of TSP. Our method improves the precision of solution, and can be extended for big size problem. Our simulation result shows that Maximum Neuron model can achieve better solutions than other methods in certain conditions.

AB - The vehicle routing problem (VRP) is one of the well known optimization problems. It is used to minimize the total length of all routes of vehicles where each of the vehicle has a capacity constraint respectively. This paper proposes a self-organization neural network model for obtaining the best solution for VRP. Our method consists of two phases. In the first phase, the customers are grouped to several delivery areas for vehicles assignment by Maximum Neuron model. In the second phase, the TSP in each area is solved by Elastic net model proposed by Andrew et. al. The clustering algorithm used in the first phase is a Maximum Neuron model. Maximum Neuron model is one of the neural networks proposed by Hopfield that can minimize a cost function considering various constraints. In the second phase, Elastic net model is used to solve the problem and it can obtain good solutions of TSP. Our method improves the precision of solution, and can be extended for big size problem. Our simulation result shows that Maximum Neuron model can achieve better solutions than other methods in certain conditions.

UR - http://www.scopus.com/inward/record.url?scp=84866845637&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866845637&partnerID=8YFLogxK

U2 - 10.1109/IPMM.1999.791534

DO - 10.1109/IPMM.1999.791534

M3 - Conference contribution

AN - SCOPUS:84866845637

T3 - Proceedings of the 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999

SP - 1109

EP - 1113

BT - Proceedings of the 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999

A2 - Veiga, Marcello M.

A2 - Meech, John A.

A2 - Smith, Michael H.

A2 - LeClair, Steven R.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999

Y2 - 10 July 1999 through 15 July 1999

ER -