Optimization of Multi-Objective Routing Models for Multiple Level and Multi-Tiered Networks

AUTHOR AND
AFFILIATION

RAVIENDER SINGH and ARIF NADEEM*
Department of Mathematics Bareilly College Bareilly (M J P R University Bareilly) (INDIA)

KEYWORDS:

Vehicle Routing, Time Windows, Raw Data

Issue Date:

August 2012

Pages:

ISSN:

2319-8044 (Online) – 2231-346X (Print)

Source:

Vol.24 – No.2

PDF

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DOI:

jusps-A

ABSTRACT:

In the present work, The authors describe two types of problem. One is the vehicle routing problem with time windows and the second problem is the pickup and delivery problem with time windows. for Optimization we use dynamic programming and branch and bound method. They will not be able to solve large-scale problems. So, we review three types of approximation algorithms. Construction methods try to build a feasible solution starting from the raw data. Iterative improvement methods start from a feasible solution and seek to improve it through a sequence of local modifications. Incomplete optimization methods use a combination of enumeration of the solution space and heuristic rules.

Copy the following to cite this Article:

RAVIENDER SINGH and ARIF NADEEM*, “Optimization of Multi-Objective Routing Models for Multiple Level and Multi-Tiered Networks”, Journal of Ultra Scientist of Physical Sciences, Volume 24, Issue 2, Page Number , 2016


Copy the following to cite this URL:

RAVIENDER SINGH and ARIF NADEEM*, “Optimization of Multi-Objective Routing Models for Multiple Level and Multi-Tiered Networks”, Journal of Ultra Scientist of Physical Sciences, Volume 24, Issue 2, Page Number , 2016

Available from: http://www.ultrascientist.org/paper/415/


In the present work, The authors describe two types of problem. One is the vehicle routing problem with time windows and the second problem is the pickup and delivery problem with time windows. for Optimization we use dynamic programming and branch and bound method. They will not be able to solve large-scale problems. So, we review three types of approximation algorithms. Construction methods try to build a feasible solution starting from the raw data. Iterative improvement methods start from a feasible solution and seek to improve it through a sequence of local modifications. Incomplete optimization methods use a combination of enumeration of the solution space and heuristic rules.