Heuristics for the dynamic facility layout problem with unequal-area departments

McKendall, Alan; Hakobyan, Artak

doi:10.1016/j.ejor.2009.02.028

Cited by 111 publications

(70 citation statements)

References 26 publications

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“…The pre-processing and branch and bound strategy of Lacksonen (1997) is tested on Sun SparcStation 2 with a Weitek board and 64Mb RAM. The GA and dynamic programming of Dunker et al (2005) is tested on Pentium IV 1.5 GHz PC, and boundary search and tabu search heuristics of McKendall and Hakobyan (2010) is tested on Pentium IV 3.6 GHz PC. The PSO of Derakhshan Asl and Wong (2015) executed the heuristic using Intel Core i3-2320M with 2.10 GHz and 4 GB of RAM.…”

Section: Proposed Sa Heuristic Computational Effort Discussionmentioning

confidence: 99%

“…With the information from the first stage, the shape and size of facilities are varied in the second stage 2 to arrive at the better solution. Some researchers use fixed dimension facilities to solve the UA-DFLP in continuous space (Dunker et al, 2005;McKendall & Hakobyan, 2010;Derakhshan Asl & Wong 2017). Fixing the dimension of facilities eliminates the non-linear area constraints in the UA-DFLP formulation, which in turn makes the model computationally tractable.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These problems have fixed dimension for the facilities. These two problems were also used by Dunker et al (2005), Mckendall & Hakobyan (2010) and Derakhshan Asl and Wong (2017) to test the performance of their solution approaches. Kulturel- Konak and Konak (2015) used P6 and P12 problems in their study by relaxing the assumption of fixed dimensions of facilities and used common maximum aspect ratio as two but their solution approach is not for the flexible bay structure arrangement.…”

Section: Proposed Sa Heuristic For Unequal Area Dflps Data Setmentioning

confidence: 99%

“…Lacksonen (1997) uses pre-processing and branch and bound strategy to solve aspect ratio constraint problems DFLP-12-3, DFLP-12-5 and DFLP-20-3. Researchers (Dunker et al, 2005;McKendall & Hakobyan, 2010;Derakhshan Asl & Wong 2017) used various meta-heuristics like combined GA and dynamic programming, boundary search and tabu search heuristics, and particle swarm optimization to solve fixed shape facilities problem of P6 and P12. Kulturel- Konak and Konak (2015) apply large scale simulated annealing algorithm as solution methodology to solve P6, P12, P15, DFLP-12-3, DFLP-12-5 and DFLP-20-3 problems with aspect ratio constraint for the facilities and without FBS in layout.…”

Section: Performance Comparison Of the Proposed Sa Heuristic For Ua-dmentioning

confidence: 99%

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

Hunagund¹,

Pillai²,

Kempaiah³

2018

10.5267/j.ijiec

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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.

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Section: Proposed Sa Heuristic Computational Effort Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Sa Heuristic For Unequal Area Dflps Data Setmentioning

confidence: 99%

Section: Performance Comparison Of the Proposed Sa Heuristic For Ua-dmentioning

confidence: 99%

See 2 more Smart Citations

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

Hunagund¹,

Pillai²,

Kempaiah³

2018

10.5267/j.ijiec

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show abstract

“…Un nivel de WIP demasiado alto alarga los tiempos de ciclo y puede disminuir el rendimiento de entregas a tiempo, lo 1 (Abedzadeh, Mazinani, Moradinasab, & Roghanian, 2012;Aderoba, 1997;Balakrishan, 1998;Balakrishan & Cheng, 2009;Balakrishan & Cheng, 2005;Balakrishan, Cheng, & Conway, 2000;Baykasoglu & Gindy, 2001;Benjaafar, 1998;Braglia, Zanoni, & Zavanella, 2005;Chen M. , 1998;Chen G. Y., 2007;Chen, Chen, & Chuang, 2011;Corry & Kozan, 2004;da Silva, Bahiense, Ochi, & Boaventura-Netto, 2012;Dong, Wu, & Hou, 2009;Dunker, Radons, & Westkamper, 2005;El-Rayes & Said, 2009;Erel, Ghosh, & Simon, 2003;Fu & Kaku, 1997;Jaramillo & McKendall Jr., 2010;Jaramillo & McKendall Jr., 2004;Jithavech & Krishnan, 2010;Jolai, Taghipour, & Javadi, 2011;Kia, 2011;Krishnan, Jithavech, & Liao, 2009;Krishnan, Cheragi, & Nayak, 2008;Kulturel-Konak, 2007;Kulturel-Konak, Smith, & Norman, 2007;Lacksonen, 1997;Lacksonen & Enscore, 1993;Ma & Zhang, 2010;Mazinani, Abedzadeh, & Mohebali, 2012;McKendall Jr. & Hakobyan, 2010;McKendall Jr., Shang, & Kuppusamy, 2006;Meng, Heragu, & Zijm, 2004;Moslemipour & Lee, 2011;P...…”

Section: Antecedentesmentioning

confidence: 99%

El WIP como objetivo estratégico en la distribución de planta

Cardona

2012

Ingenium

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. El WIP como objetivo estratégico en la distribución de planta. Ingenium, 6(14) ResumenEste artículo presenta una manera de obtener una distribución de planta que minimiza los niveles de inventario de trabajo en proceso (WIP) para un sistema de producción donde el flujo de materiales se lleva a cabo por medio de diferentes sistemas de manejo de materiales. El WIP como valor objetivo del problema de distribución de planta plantea una diferencia con la mayoría de modelos de optimización existentes y brinda información sobre una medida de rendimiento operativo que por lo general no es considerada, pero que impacta de manera directa los costos asociados a la producción. Se demostró que el modelo presentado permite encontrar diseños de planta con mejor desempeño en este indicador que los encontrados por medio de un modelo que considere un solo sistema de manejo de materiales. Palabras ClaveDistribución de planta; WIP; optimización; modelo de colas. AbstractThis paper presents a model to generate the layout of a facility, minimizing the work in process (WIP) for a production system that uses multiple material handling systems. Using WIP as an objective in a facility layout problem is a novel approach. WIP is an operating performance measure that impacts directly in the production costs, and is generally not considered in facility layout problems. As a result, we present a model to design facility layouts with a better WIP performance than the ones designed by a model with a single material handling system

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Extensions and Related Problems

Anjos

Vieira

2021

EURO Advanced Tutorials on Operational Research

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Heuristics for the dynamic facility layout problem with unequal-area departments

Cited by 111 publications

References 26 publications

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

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

El WIP como objetivo estratégico en la distribución de planta

Extensions and Related Problems

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