Simulated annealing heuristics for the dynamic facility layout problem

McKendall, Alan; Shang, Jin; Kuppusamy, Saravanan

doi:10.1016/j.cor.2005.02.021

Cited by 163 publications

(66 citation statements)

References 74 publications

(138 reference statements)

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“…En los objetivos cualitativos se encuentran la seguridad de la planta, la flexibilidad de la distribución, el ruido y la estética (Singh & Sharma, 2006). El objetivo más usado para encontrar la distribución óptima es minimizar el costo de manejo de materiales (McKendall, Shang, & Kuppusamy, 2006;Meller & KaiYin, 1996). En los últimos años se ha incluido un criterio diferente que considera otras medidas de rendimiento operativo, como el inventario de trabajo en proceso (WIP) o el tiempo de ciclo (LT).…”

Section: Introductionunclassified

“…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%

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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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Section: Introductionunclassified

Section: Antecedentesmentioning

confidence: 99%

El WIP como objetivo estratégico en la distribución de planta

Cardona

2012

Ingenium

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“…Simulated annealing has been applied successfully to a wide variety of highly complicated combinatorial optimization problems [18][19][20][21]. It was successfully applied to the TTRP by Yu et al [14].…”

Section: Simulated Annealing Heuristic For the Rttrpmentioning

confidence: 99%

A note on the truck and trailer routing problem

Lin

Chou

2010

Expert Systems with Applications

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This study considers a relaxation of the truck and trailer routing problem (TTRP), called the relaxed truck and trailer routing problem (RTTRP). In the TTRP, the number of available vehicles, namely trucks and trailers, is limited. Since there are no fixed costs associated with the trucks or trailers, it is reasonable to relax this fleet size constraint. Besides, most benchmark instances for the capacitated vehicle routing problem (VRP) do not have fleet size constraints. We developed a simulated annealing heuristic for solving the RTTRP and tested it on 21 TTRP benchmark problems. Computational results indicate that the solution for the RTTRP is generally better than the best solutions in the literature for the TTRP. Among the 21 benchmark TTRP instances, we found better solutions to 18 of them. The solutions for the remaining three problems are tied. The average improvement is about 1.33%. Considering the ever rising crude oil price, even small reduction in the route length is significant.

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“…The dynamic environment QAP is still more complex than static QAP due to an introduction of periods in the dynamic QAP. Hence researchers use different meta-heuristic methods to solve equal area DFLPs (Conway & Venkataramanan, 1994;Balakrishnan & Cheng, 2000;Baykasoglu & Gindy, 2001;Balakrishnan et al, 2003;McKendall et al, 2006;Yang et al, 2011;Pourvaziri & Naderi, 2014;Bozorgi et al, 2015). Pillai et al (2011) and Forghani et al (2013) used the robust approach to solve equal area DFLPs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unlike other metaheuristics, SA is simple to implement, and it is sequential search algorithm hence it is computationally more efficient one. Application of SA is made for solving equal area static and dynamic facility layout problems (Whim & Ward, 1987;Baykasoglu & Gindy, 2001;McKendall Jr et al, 2006;Pillai et al, 2011). Tam (1992) used SA for general type UA-FLPs but not for UA-DFLP with FBS.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Simulated annealing heuristics for the dynamic facility layout problem

Cited by 163 publications

References 74 publications

El WIP como objetivo estratégico en la distribución de planta

El WIP como objetivo estratégico en la distribución de planta

A note on the truck and trailer routing problem

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

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