Paper Info
Title
Self-organizing feature maps for the vehicle routing problem with backhauls
Abstract
In the Vehicle Routing Problem with Backhauls (VRPB), a central depot, a fleet of homogeneous vehicles, and a set of customers are given. The set of customers is divided into two subsets. The first (second) set of linehauls (backhauls) consists of customers with known quantity of goods to be delivered from (collected to) the depot. The VRPB objective is to design a set of minimum cost routes; originating and terminating at the central depot to service the set of customers. In this paper, we develop a self-organizing feature maps algorithm, which uses unsupervised competitive neural network concepts. The definition of the architecture of the neural network and its learning rule are the main contribution. The architecture consists of two types of chains: linehaul and backhaul chains. Linehaul chains interact exclusively with linehaul customers. Similarly, backhaul chains interact exclusively with backhaul customers. Additonal types of interactions are introduced in order to form feasible VRPB solution when the algorithm converges. The generated routes are then improved using the well-known 2-opt procedure. The implemented algorithm is compared with other approaches in the literature. The computational results are reported for standard benchmark test problems. They show that the proposed approach is competitive with the most efficient metaheuristics.
Year
DOI
Venue
2006
10.1007/s10951-006-6774-z
J. Scheduling
Keywords
Field
DocType
Vehicle routing problem with backhauls,Self-organizing feature maps,Neural networks,Metaheuristics
Vehicle routing problem,Mathematical optimization,Architecture,Backhaul (telecommunications),Computer science,Homogeneous,Learning rule,Artificial neural network,Metaheuristic
Journal
Volume
Issue
ISSN
9
2
1094-6136
Citations 
PageRank 
References 
12
0.58
21
Authors
2
Name
Order
Citations
PageRank
Hassan Ghaziri1120.92
Ibrahim H. Osman281594.23