In this paper a variable neighborhood search (VNS) approach for the task assignment problem (TAP) is considered. An appropriate neighborhood scheme along with a shaking operator and local search procedure are constructed specifically for this problem. The computational results are presented for the instances from the literature, and compared to optimal solutions obtained by the CPLEX solver and heuristic solutions generated by the genetic algorithm. It can be seen that the proposed VNS approach reaches all optimal solutions in a quite short amount of computational time.* This research was partially supported by the Serbian Ministry of Science and Ecology under project 144007
The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to m...
Abstract. Motivated by an application in project portfolio analysis un-der uncertainty, we develop a...
AbstractMany optimization problems of practical interest are computationally intractable. Therefore,...
A Variable Neighborhood Search algorithm that employs new neighbourhoods is proposed for solving a t...
A Variable Neighborhood Search algorithm is proposed for solving a task allocation problem whose mai...
AbstractWe introduce a heuristic for the Multi-Resource Generalized Assignment Problem (MRGAP) based...
: A variable depth search procedure (abbreviated as VDS) is a generalization of the local search met...
Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for problem solvin...
Variable neighborhood search is a local search metaheuristic that uses sequentially different neighb...
This paper presents variable neighborhood search (VNS) for the problem of finding the global minimum...
Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for solving combin...
AbstractThis paper presents variable neighborhood search (VNS) for the problem of finding the global...
This paper describes a Variable Neighbourhood Search (VNS) combined with simulated annealing to tack...
This work presents the application of Variable Neighborhood Search (VNS) based algorithms to the Hig...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Resea...
The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to m...
Abstract. Motivated by an application in project portfolio analysis un-der uncertainty, we develop a...
AbstractMany optimization problems of practical interest are computationally intractable. Therefore,...
A Variable Neighborhood Search algorithm that employs new neighbourhoods is proposed for solving a t...
A Variable Neighborhood Search algorithm is proposed for solving a task allocation problem whose mai...
AbstractWe introduce a heuristic for the Multi-Resource Generalized Assignment Problem (MRGAP) based...
: A variable depth search procedure (abbreviated as VDS) is a generalization of the local search met...
Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for problem solvin...
Variable neighborhood search is a local search metaheuristic that uses sequentially different neighb...
This paper presents variable neighborhood search (VNS) for the problem of finding the global minimum...
Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for solving combin...
AbstractThis paper presents variable neighborhood search (VNS) for the problem of finding the global...
This paper describes a Variable Neighbourhood Search (VNS) combined with simulated annealing to tack...
This work presents the application of Variable Neighborhood Search (VNS) based algorithms to the Hig...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Resea...
The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to m...
Abstract. Motivated by an application in project portfolio analysis un-der uncertainty, we develop a...
AbstractMany optimization problems of practical interest are computationally intractable. Therefore,...