General variable neighbourhood search for cyclic bandwidth sum minimization problem

2012 ◽  
Author(s):  
Dharna Satsangi ◽  
Kamal Srivastava ◽  
Gursaran
Keyword(s):  
Minimization Problem ◽  
Variable Neighbourhood Search ◽  
Neighbourhood Search

A Variable Neighbourhood Search approach to the Cutwidth Minimization Problem

2012 ◽  
Vol 39 ◽  
pp. 67-74 ◽  
Author(s):  
Eduardo G. Pardo ◽  
Nenad Mladenović ◽  
Juan J. Pantrigo ◽  
Abraham Duarte
Keyword(s):  
Minimization Problem ◽  
Variable Neighbourhood Search ◽  
Neighbourhood Search ◽  
Search Approach

Parallel variable neighbourhood search strategies for the cutwidth minimization problem

2013 ◽  
Vol 27 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Abraham Duarte ◽  
Juan J. Pantrigo ◽  
Eduardo G. Pardo ◽  
Jesús Sánchez-Oro
Keyword(s):  
Minimization Problem ◽  
Search Strategies ◽  
Variable Neighbourhood Search ◽  
Neighbourhood Search

scholarly journals Variable Neighbourhood Search

2003 ◽  
Vol 7 (19) ◽  
Author(s):  
P. Hansen ◽  
N. Mladenovic ◽  
J.A. Moreno
Keyword(s):  
Variable Neighbourhood Search ◽  
Neighbourhood Search

Solving Vehicle Routing Problems Using Constraint Programming and Lagrangean Relaxation in a Metaheuristics Framework

2011 ◽  
Vol 4 (2) ◽  
pp. 61-81 ◽  
Author(s):  
D. Guimarans ◽  
R. Herrero ◽  
J. J. Ramos ◽  
S. Padrón
Keyword(s):  
Vehicle Routing ◽  
Constraint Programming ◽  
Complete Solution ◽  
Lagrangean Relaxation ◽  
Variable Neighbourhood Search ◽  
Relaxation Methods ◽  
Routing Problem ◽  
Neighbourhood Search ◽  
Test Instance ◽  
Initial Feasible Solution

This paper presents a methodology based on the Variable Neighbourhood Search metaheuristic, applied to the Capacitated Vehicle Routing Problem. The presented approach uses Constraint Programming and Lagrangean Relaxation methods in order to improve algorithm’s efficiency. The complete problem is decomposed into two separated subproblems, to which the mentioned techniques are applied to obtain a complete solution. With this decomposition, the methodology provides a quick initial feasible solution which is rapidly improved by metaheuristics’ iterative process. Constraint Programming and Lagrangean Relaxation are also embedded within this structure to ensure constraints satisfaction and to reduce the calculation burden. By means of the proposed methodology, promising results have been obtained. Remarkable results presented in this paper include a new best-known solution for a rarely solved 200-customers test instance, as well as a better alternative solution for another benchmark problem.

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