A simulated annealing algorithm for unequal area dynamic facility layout problems with flexible bay structure

被引:13
|
作者
Hunagund, Irappa Basappa [1 ]
Pillai, V. Madhusudanan [2 ]
Kempaiah, U. N. [3 ]
机构
[1] Govt Engn Coll Ramanagar, Dept Mech Engn, Ramanagar 562159, Karnataka, India
[2] Natl Inst Technol Calicut, Dept Mech Engn, Calicut 673601, Kerala, India
[3] Univ Visvesvarayya, Coll Engn, Dept Mech Engn, Bangalore 560001, Karnataka, India
关键词
Unequal area dynamic facility; layout problems; Flexible bays; Simulated annealing; Adaptive strategy; GENETIC ALGORITHM; ASSIGNMENT PROBLEMS; SEARCH; DESIGN; OPTIMIZATION; LOCATION;
D O I
10.5267/j.ijiec.2017.8.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this article, we propose Simulated Annealing (SA) heuristic to solve Unequal Area Dynamic Facility Layout Problem (FBS) with Flexible Bay Structure (UA-DFLPs with FBS). The UA-DFLP with FBS is the problem of determining the facilities dimension and their location coordinates with flexible bays formation in the layout for various periods of the planning horizon. The UA-DFLP with FBS is more constrained than general UA-DFLP and it is an NP-complete problem. The proposed SA is tested with the available UA-DFLPs instances in the literature. The proposed SA heuristic has given new best solution or the same solution for FBS based problems as compared with the best-known reported in the UA-DFLPs with FBS literature. The proposed SA heuristic is also tested on standard UA-DFLPs used in non-FBS approaches. The SA heuristic solution is not significantly different from the best solution reported in the literature for non-FBS approaches. Equal area DFLP instances are also solved with the proposed SA and the results obtained are promising with the solutions reported in the literature. Hence the results obtained indicate that the proposed SA for UA-DFLP with FBS is effective and versatile for both equal and unequal area dynamic facility layout problems. The computational efficiency of the proposed SA heuristic is very much competitive as compared to other meta-heuristics computational timings reported in the literature. (C) 2018 Growing Science Ltd. All rights reserved
引用
收藏
页码:307 / 330
页数:24
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