TY  - CONF
AV  - none
SP  - 142
CY  - Iasi
EP  - 147
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-83655165380&partnerID=40&md5=fe446bf5a9d6e5f4e220a5a9e89d3f76
A1  - Masrom, S.
A1  - Abidin, S.Z.Z.
A1  - Nasir, A.M.
A1  - Rahman, A.S.Abd.
N1  - cited By 3; Conference of 13th WSEAS International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems, MAMECTIS'11, 10th WSEAS International Conference on Non-Linear Analysis, Non-Linear Systems and Chaos, NOLASC'11, CONTROL'11, WAMUS'11 ; Conference Date: 1 July 2011 Through 3 July 2011; Conference Code:87756
ID  - scholars1436
Y1  - 2011///
TI  - Hybrid Particle Swarm Optimization for vehicle routing problem with Time Windows
KW  - Hybridization; Mutation; Particle swarm; Solution quality; Vehicle routing problem with time windows
KW  -  Chaotic systems; Convergence of numerical methods; Fleet operations; Genetic algorithms; Intelligent systems; Linear systems; Mathematical techniques; Network routing; Nonlinear systems; Routing algorithms; Vehicle routing; Vehicles
KW  -  Particle swarm optimization (PSO)
SN  - 9781618040114
N2  - Vehicle routing problem with Time Window (VRPTW) has received much attention by researchers in solving many scheduling applications for transportation and logistics. The objective of VRPTW is to use a fleet of vehicles with specific capacity to serve a number of customers with various demands and time window constraints. As a non-polynomial (NP) hard problem, the VRPTW is complex and time consuming, especially when it involves a large number of customers and constraints. This paper presents a hybrid approach between Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving VRPTW. The reason for hybridization is to overcome the problem of premature convergence that exists in standard PSO. Premature convergence often yields partially optimized solutions because of particles stagnation. The proposed hybrid PSO implements a mechanism that automatically trigger swarm condition which will liberate particles from sub-optimal solutions hence enabling progress toward the maximum best solution. A computational experiment has been carried out by running the hybrid PSO with the VRPTW benchmark data set. The results indicate that the algorithm can produce some improvement when compared to the original PSO.
ER  -