Efficient Algorithms for Solving Facility Layout Problem Using a New Neighborhood Generation Method Focusing on Adjacent Preference

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were stu...

Full description

Saved in:
Bibliographic Details
Published inIndustrial Engineering & Management Systems Vol. 8; no. 1; pp. 22 - 28
Main Authors Tatsuya Fukushi, Hisashi Yamamoto, Atsushi Suzuki, Yasuhiro Tsujimura
Format Journal Article
LanguageEnglish
Published 대한산업공학회 01.03.2009
Subjects
Online AccessGet full text
ISSN1598-7248
2234-6473

Cover

More Information
Summary:We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms. KCI Citation Count: 1
Bibliography:G704-002162.2009.8.1.006
ISSN:1598-7248
2234-6473