Adaptive search heuristics for the generalized assignment problem.

Ramalhinho Lourenço, Helena, and Serra, Daniel. "Adaptive search heuristics for the generalized assignment problem.." Mathware and Soft Computing 9.2-3 (2002): 209-234. <http://eudml.org/doc/39244>.

@article{RamalhinhoLourenço2002,
abstract = {The Generalized Assignment Problem consists of assigning a set of tasks to a set of agents at minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the agent's resource. We present the application of a MAX-MIN Ant System (MMAS) and a greedy randomized adaptive search procedure (GRASP) to the generalized assignment problem based on hybrid approaches. The MMAS heuristic can be seen as an adaptive sampling algorithm that takes into consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of a comparative analysis of the two adaptive heuristics, followed by concluding remarks and ideas on future research in generalized assignment related problems.},
author = {Ramalhinho Lourenço, Helena, Serra, Daniel},
journal = {Mathware and Soft Computing},
keywords = {Optimización global; Algoritmo de búsqueda; Problemas combinatorios; Asignación; Heurística; generalized assignment problem},
language = {eng},
number = {2-3},
pages = {209-234},
title = {Adaptive search heuristics for the generalized assignment problem.},
url = {http://eudml.org/doc/39244},
volume = {9},
year = {2002},
}

TY - JOUR
AU - Ramalhinho Lourenço, Helena
AU - Serra, Daniel
TI - Adaptive search heuristics for the generalized assignment problem.
JO - Mathware and Soft Computing
PY - 2002
VL - 9
IS - 2-3
SP - 209
EP - 234
AB - The Generalized Assignment Problem consists of assigning a set of tasks to a set of agents at minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the agent's resource. We present the application of a MAX-MIN Ant System (MMAS) and a greedy randomized adaptive search procedure (GRASP) to the generalized assignment problem based on hybrid approaches. The MMAS heuristic can be seen as an adaptive sampling algorithm that takes into consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of a comparative analysis of the two adaptive heuristics, followed by concluding remarks and ideas on future research in generalized assignment related problems.
LA - eng
KW - Optimización global; Algoritmo de búsqueda; Problemas combinatorios; Asignación; Heurística; generalized assignment problem
UR - http://eudml.org/doc/39244
ER -