Using genetic algorithms on facilities layout problems

Hu, Michael H.; Wang, Ming-Jaan

doi:10.1007/s00170-003-1637-7

Cited by 34 publications

(11 citation statements)

References 30 publications

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“…Dunker et al (2003) used a GA to solve the FLP with unequal area fixed shape departments [31]. Michael and Wang (2004) utilized a scheme to solve both equal and unequal area department problems in facilities layout by using genetic algorithms [32]. Wang et al (2005) are focused on the unequal areas' department facilities layout problem and implement analysis of variance (ANOVA) of statistics to find out the best site size of layout by genetic algorithm [33].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The last row shows the number of the best solutions obtained from each heuristic. The bold numbers indicate the best objective function value (OFV) obtained for each test problem The bolded numbers signify the optimum answer for each problem In summary, the TS-NM, TSbasic, TSall, PTS, DP, SA_EG, GA, HAS, and SA heuristics obtained the best solutions for 32,12,16,14,13,13,10,10,16, and 16 of the 32 problems, respectively. Therefore, the TS-NM heuristic clearly outperformed all the other heuristics for this data set with respect to with respect to other heuristics…”

Section: Comparison and Validationmentioning

confidence: 99%

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Tabu search heuristic for efficiency of dynamic facility layout problem

Bozorgi

Abedzadeh

Zeinali

2014

Int J Adv Manuf Technol

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The facility layout problem (FLP) is a wellresearched problem of finding positions of departments on a plant floor such that departments do not overlap and some specific critical criteria involved in FLP are optimized. In addition, the facility layout dramatically influences the efficiency of material handling within a manufacturing system. In order to ensure optimal performance within a manufacturing system, the facility layout should reflect changes throughout time. However, the static facility layout problem with constant material flows between departments may not be a realistic scenario because a manufacturing facility is a dynamic system that constantly evolves. On the other hand, layout problem has multi-objective nature, and it is necessary to apply various criteria and goals for determining a good facility layout. As a result, this research is about determining a solution for dynamic facility layout problem (DFLP) with equal departments by applying data envelopment analysis (DEA) with consideration of some specific criteria which are cost, adjacency, and distance requested. Cost as a negative criterion is minimized and used as an input, whereas adjacency and distance requested are considered positive criteria and used as output of DEA model. In the proposed algorithm, at first, the initial layout is generated, and then its neighborhood is created by the pair exchange and reverse strategy. By applying tabu search heuristic using diversification strategy which includes "frequency-based memory," "penalty function," and "dynamic tabu list size" to the DEA model, the most desired efficient layout is obtained. Two data sets taken from the relevant literature are used to test and evaluate the performance of the proposed heuristic. Computational experiments show that the proposed heuristic outperforms other heuristics presented in relevant literature, and the most efficient layout is achieved.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Comparison and Validationmentioning

confidence: 99%

Tabu search heuristic for efficiency of dynamic facility layout problem

Bozorgi

Abedzadeh

Zeinali

2014

Int J Adv Manuf Technol

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“…In general, the level of utilization of the area is measured only in terms of free available area (Hu and Wang 2004). Likewise, Lin and Sharp (1999) use two measures to calculate the utilization area: proportion of free area, and the free distribution area in the workshop or work place.…”

Section: Productive Area Utilizationmentioning

confidence: 99%

“…As a consequence, the reduction of the activities minimizes the corresponding production time and the MHC, which are two of the objectives of an efficient layout (Hu and Wang 2004). However, there are three deficiencies in the methods:…”

Section: Proximitymentioning

confidence: 99%

Lean Manufacturing in the Developing World

Alcaráz¹,

Maldonado-Macías²,

Cortés-Robles³

2014

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“…Mir et al [18] presented a based hybrid optimization approach for unequal area facilities design and concluded that the hybrid technique can efficiently produce high-quality layouts for large-scale problems involving unequal area facilities. Hu and Wang [19] applied GA to unequal area facility layout problems to achieve the minimal total layout cost. The objective function in the current study is a multiple-objective function involving material flow factor cost, shape ratio factor, and area utilization factor.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and optimization of manufacturing facilities layouts

Balamurugan¹,

Selladurai²,

Ilamathi³

2006

Proceedings of the Institution of Mechanical Engineers, Part B:

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The present paper deals with the design of manufacturing facilities layout with the consideration of downtime of facilities and space utilization using genetic algorithm (GA). The existing approaches to solve problems of unequal area facility layout in manufacturing environments can provide optimal solutions with limitations such as single design criterion, manual revision, and approximated shape. Also, importance to practical factors such as downtime of facilities owing to planned maintenance or failure of machines, and floor space utilization are addressed to a lesser degree. A mathematical model and a heuristic solution procedure for designing manufacturing facilities layouts that address the above-mentioned shortcomings are proposed. The proposed model minimizes the cost associated with material handling during normal and breakdown periods with due consideration of compactness of the layout. A heuristic placement procedure, which is capable of changing the orientation of facilities, is proposed to place the machines in the floor area, in order to obtain maximum space utilization. The proposed design approach is evaluated by solving different layout design problems taken from the literature and by solving an industrial case study. The results of the proposed GA optimizer are compared with results obtained from greedy search and simulated annealing methods. The results show that the approach performs better in most of the cases.

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Using genetic algorithms on facilities layout problems

Cited by 34 publications

References 30 publications

Tabu search heuristic for efficiency of dynamic facility layout problem

Tabu search heuristic for efficiency of dynamic facility layout problem

Lean Manufacturing in the Developing World

Design and optimization of manufacturing facilities layouts

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