Main methods, algorithms and applications of the Variable Neighborhood Search metaheuristic are surveyed, in view of a chapter of the Encyclopedia of Optimization.

Medical data sets consist of a huge amount of data organized in instances, where each one contains several attributes. The quality of the models obtained from a database strongly depends on the information previously stored on it. For this reason, these data sets must be preprocessed in order to have fairly information about patients. Data sets are preprocessed reducing the amount of data. For ...

although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. but, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. this paper considers the problem of scheduling no-idle hybrid flow shops. a mixed integer linear programming model is first developed to mathematically form...

Variable Neighborhood Search (VNS) is a recent metaheuristic, or framework for building heuristics, which exploits systematically the idea of neighborhood change, both in the descent to local minima and in the escape from the valleys which contain them. In this tutorial we first present the ingredients of VNS, i.e., Variable Neighborhood Descent (VND) and Reduced VNS (RVNS) followed by the basi...

Systematic change of neighborhood within a possibly randomized local search algorithm yields a simple and e€ective metaheuristic for combinatorial and global optimization, called variable neighborhood search (VNS). We present a basic scheme for this purpose, which can easily be implemented using any local search algorithm as a subroutine. Its e€ectiveness is illustrated by solving several class...

In this paper, an effective approach based on the variable neighborhood search (VNS) algorithm is presented to solve the uncapacitated multilevel lot-sizing (MLLS) problems with component commonality and multiple end-items. A neighborhood structure for the MLLS problem is defined, and two kinds of solution move policies, i.e., move at first improvement (MAFI) and move at best improvement (MABI)...

In this paper a variable neighborhood search approach as a method for solving combinatory optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algor...

Given an undirected weighted graph G = (V,E) with vertex set V, edge set E and weights wi ∈ R associated to V or to E. Minimum weighted k-Cardinality tree problem (k-CARD for short) consists of finding a subtree of G with exactly k edges whose sum of weights is minimum [4]. There are two versions of this problem: vertex-weighted and edge-weighted, if weights to V or to E are associated, respect...