NDLI: A multi level priority clustering NN based approach for solving heterogeneous vehicle routing problem

Content Provider	IEEE Xplore Digital Library
Author	Haghighi, M.S. Zahedi, M.H. Rouhani, S.M.
Copyright Year	2009
Description	Author affiliation: Islamic Azad Univ., Iran (Rouhani, S.M.) \|\| Sadjad Inst. of Higher Educ., Iran (Haghighi, M.S.) \|\| Ferdowsi Univ. of Mashhad, Mashhad, Iran (Zahedi, M.H.)
Abstract	This research presents a two phases heuristic neural network combined algorithmic approach to solve multiple depot routing problem with heterogeneous vehicles. It has been derived from embedding a heuristic based two level clustering algorithm within a multiple depot vehicle routing problem optimization framework. In logistic applications, customers have priority based on some logistic point of view. The priority levels of customers, affect distribution strategy specially in clustering level. In this research we have developed an integrated vehicle routing problem model using heuristic clustering method with Hopfield network. In the first phase of the algorithm, a high level heuristic clustering is performed to cluster customers serviced by a special depot. Next, a low level clustering is done for each depot to find clusters serviced by a single vehicle. Despite other optimization approaches, which solve case studies involving at most 25 nodes optimally, the proposed algorithm overcomes this limitation by a preprocessing stage by applying clustering on nodes. In this approach, a hierarchical hybrid procedure involving one heuristic and one neural network phases was developed.
Sponsorship	IEEE Instrum. Meas. Soc.
Starting Page	224
Ending Page	229
File Size	549088
Page Count	6
File Format	PDF
ISBN	9781424433520
ISSN	10915281
DOI	10.1109/IMTC.2009.5168448
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-05
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Vehicles Routing Clustering algorithms Logistics Evolutionary computation Impedance Heuristic algorithms Artificial neural networks Particle swarm optimization Hopfield Vehicle Routing Problem Clustering Algorithm Neural Networks
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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