Indian J.Sci.Res. 3(3) : 149-159, 2014 ISSN : 0976-2876 (Print)
ISSN : 2250-0138 (Online)
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LAYOUT DE...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
One of ...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
manufac...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKI
Indian J.Sci.Res. 3(3) : 149-159, 2014
Cleaning, Inspection and Sorting (Depart...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
Plans b...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
That wi...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
In this...
MEHDIZADEHETAL.:LAYOUTDESIGNANDRANKINGTHEFACILITYDESIGNOFCREAM..
IndianJ.Sci.Res.3(3):149-159,2014
Decisionmatrixgainedfro...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
C1 C2 C...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
Alterna...
MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM..
Indian J.Sci.Res. 3(3) : 149-159, 2014
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LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM MANUFACTURING PLANT BY USING FUZZY TOPSIS

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Layout problems are found in a variety of production systems. Normally layout problems are related to facilitate
place (e.g., machinery, parts, etc.) in a factory. These facilities are heavily influenced by the performance of the system as well as having an effect on system performance. Several studies have been conducted related to facility design. When facilities design software or the design factory offered several final designs to the director, selecting an option for the manager will be difficult. This paper presents an approach based on Multi-Criteria Decision Method by which a director can be selected the best option of several designs. Purpose of review discussed the facility's manufacturing plant creams.
First the units and then the required facilities for each unit are introduced. Then with the engineers and designers with
software factory began layout design facility at the plant and finally, four projects were presented. Then fuzzy TOPSIS methods to rank were proposed projects.

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LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM MANUFACTURING PLANT BY USING FUZZY TOPSIS

  1. 1. Indian J.Sci.Res. 3(3) : 149-159, 2014 ISSN : 0976-2876 (Print) ISSN : 2250-0138 (Online) 1 Corresponding author LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM MANUFACTURING PLANT BY USING FUZZY TOPSIS MAHMOUD MEHDIZADEHa , LADAN HASSANIb1 , FIROUZEH RAZAVIc AND MAJID BABAIEd a MSc in Industrial Management, Department of Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran b MSc in Industrial Management, Department of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran c MSc in Information Technology Engineering,Electronic Education Center, Shiraz University, Shiraz, Iran d MSc in MBA, Carleton University, Ottawa, Canada ABSTRACT Layout problems are found in a variety of production systems. Normally layout problems arerelated to facilitate place (e.g., machinery, parts, etc.) in a factory. These facilities are heavily influenced by the performance of the system as well as having an effect on system performance. Several studies have been conducted related to facility design. When facilities design software or the design factoryoffered several final designs to the director, selecting an option for the manager will be difficult. This paper presents an approach based on Multi Criteria Decision method by which a director can be selected the best option of several designs. Purpose of review discussed the facility's manufacturing plant creams. First the units and then therequired facilities for each unit are introduced. Then with the engineers and designers with software factory began layout design facility at the plant and finally, four projects were presented. Then fuzzy TOPSIS methods to rank were proposed projects. KEYWORDS: Design Facility, Cream Manufacturing Plant, Equipment and Facilities, Methods of Multi-Criteria Decision Making, Fuzzy Topsis Method.
  2. 2. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 One of the most important activities that Industrial Engineering and Industrial Management are dealing with is Management and Planning Plant Science. The purpose of planning of the factory is a complex and broad and has many related to the issues of the others science. A proper and suitable plan to help lead managers who intend to provide a favorable environment for production, raw materials more quickly into the production process in less time, with a minimum of loss capital, the factory to have a product made. It is obvious that this goal will not be achieved; unless any of the activities that take place of plant studied and survived and scientifically, the terms necessary for activity, determined in accordance with certain criteria comes to action. Layout problem is a strategic issue and has a significant impact on the performance of manufacturing systems. Most of the existing methods in the investigation of a representative function for flow distance or for simplified objectives may be used, fall in the trap of local optimal solutions and thus, given that decisions about layout design, essentially a type of multiple attribute decision making (MADM) can lead to poor design layout. In this paperthe major activity by researchers in order to investigate the importance of the facility layout design, layout and finally selected according to the criteria specified. LITERATURE REVIEW Facility Lay Out Facility lay out is planningoffacilities, departments or equipment needed to produce goods or provide services. Facility is an entity that facilitates the performance of every job. Can a machine tool, a business center, a manufacturing cell, division, office, shop etc. (Heragu, 1997). Facility lay out is related to arranging cars, offices, workstations, storage areas corridors and public areas in a place refer to existing or proposed. Facility lay outis implicated for quality, productivity and competitiveness are a company. Layout decisions significantly affect the degree of effectiveness of the workers in their work, the speed of commodity producers, hard-machining andresponsiveness a system of changing a system or product design services, product mix and volume affect demand. The main purpose of the arrangement is to ensure that the smooth flow of work and materials, People and information between systems. Also influenced by: 1) Minimize material handling costs. 2) It makes efficient use of space. 3) Takes the efficient advantage of the works. 4) Eliminates the bottlenecks. 5) Communication and interaction between workers and their supervisorsor between workers to customers facilitated. 6) Reduce Manufacturing cycle or customer service times. 7) Deletes additional moves or redundant. 8) Gives arrival, departure and replacement of materials, products or people readily. 9) Join together measures of safety and security. 10) Qualities or service of a product will promote. 11) Encourages appropriate conservation activities. 12) Offers a dimensional control of the operations or activities. 13) Provides the flexibility to adapt to changing conditions. RESEARCH BACKGROUND Houshyar presented an exact optimal solution for facility with the decisions that must be layout together by each pair (Houshyar, 1993). Shang is provided a unified approach to solve multi-criteria facility layout (Shang, 1993). Yang et al. AHP method, data envelopment analysis (DEA) to address the factory layout design (Yang &Kuo, 2003). Young and Hung using multiple attributes decision-making method TOPSIS and Fuzzy TOPSIS factory layout design options were considered for selection (Yang & Hung, 2007). Huang and colleagues using gray relation analysis to solve multiple attribute decision to review the case study facility layout design options studied (Huang et al., 2008). Introduction of Plants and Production Processes In this paper, as an application example to design the layout of the factory's products has been studied.In Continue will present the cream
  3. 3. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 manufacturing process equipment and machinery used in the factory and sample in each section. The Five Steps of Soft cream Manufacturing Gelatin Preparation (Department A) The process of blending and heating granulated gelatin and other ingredients in warm water in a gelatin melting tank. With appropriate Heat, mixing and vacuum, the ingredients form a thick syrup called a "cream mass" for use in encapsulation. Color may be added during the melting process or in a separate machine. Figure 1: Gelatin preparation Encapsulation (Department C) The process of converting the cream mass into a thin layer of gelatin and wrapping it around the fill material to form a soft cream. Click here to see how an encapsulation machine works. Figure 3: Encapsulation machine Fill Material Preparation (Department B) The process of preparing the non-aqueous oil or paste that will be encapsulated. Preparation equipment may include: processing tanks, mixers, vacuum homogenizers, sieves and mills. Heated and unheated transfer tanks may be used for the fill material and gelatin while waiting for encapsulation. Figure 2: Fill material preparation Drying (Department D) The process which removes excess moisture from the gelatin shell to shrink and firm up the soft cream. Drying occurs either by tumbling or by a combination of tumbling and tray drying. Figure 4: Drying machine
  4. 4. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKI Indian J.Sci.Res. 3(3) : 149-159, 2014 Cleaning, Inspection and Sorting (Department E) Quality Control (Department F) After packing soft cream it would be sent to the laboratory to be checked by the quality control supervisors. This control is very essential for product Layout Planning In order to design the layout required, information were available to manufacturing unit to the desired layout or suitable layouts to design. It contains information on building size, number of Figure 7: The Diagram Shows the Importance of ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Cleaning, Inspection and Sorting (Department E) Often required prior to packaging based upon the intended use of the soft cream. Figure 5: A Sorting Machine After packing soft cream it would be sent to the laboratory to be checked by the quality control supervisors. This control is very essential for product because the end customer is using cream exactly after this certification. Figure 6: Laboratory Equipment design the layout required, manufacturing unit to layouts to design. It contains information on building size, number of departments, and departments are required near and far. Building Size: 2000 m² Number of Departments: 6 The importance and necessity of departments close to each other: The Diagram Shows the Importance of Units Close to Each Other Often required prior to packaging based upon the intended use of the soft cream. because the end customer is using cream exactly after departments, and departments are required near and : 6 The importance and necessity of o Each Other
  5. 5. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 Plans by industry specialists units with usingmanagers’opinion and by using different software has been designed. These plansused for the layout and facilities departments, and equipment. Figure 8: Lay Out Plan 1 Figure 9: Lay Out Plan 2 Figure 10: Lay Out Plan 3 Figure 11: Lay Out Plan 4 TOPSIS METHOD Imagine that A1, A2 … Am are m alternatives that supposed to be ranked by k decision makers based on n criteria (C1, C2 … Cn). Xij is the rating score of Ai related to jth criteria and is defined: šCD ?šCD šCD I C Weights of criteria are defined as w1, w2 … wn that wj is the weight of cj. We can define a MADM problem with interval numbers briefly in a decision making matrix like the table 2. Step 1: in TOPSIS method with interval numbers we have to normalize decision making matrix as we show it below: I ˲ ˲ $ - ˲ $ (# ˩ ŵ Ŷ J ˪ ŵ Ŷ ˭ I ˲ ˲ $ - ˲ $ (# ˩ ŵ Ŷ J ˪ ŵ Ŷ ˭ Now ?˲ ˲ C are normalized and the calculated domain ?I I C is belonging to{Ŵ ŵ{. Because of differences in importance of each criterion, in the next step we will calculate weighted normalized decision matrix with interval numbers as below: ˰ ˱ I ˩ ŵ Ŷ J ˪ ŵ Ŷ ˭ ˰ ˱ I ˩ ŵ Ŷ J ˪ ŵ Ŷ ˭
  6. 6. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 That wi is the weight of ith criterion and ˱(# ŵ. Then we are describing the ideal positive and negative solutions as: ˓ {˰# ˰ { ƒš ˰ ˩ HF ‹ ˰ ˩ HF< ˓ {˰# ˰ { ‹ ˰ ˩ HF ƒš ˰ ˩ HF< That "I" is referred to benefit criterion and "J" is referred to cost. Distances of each alternative from positive and negative solutions have to be calculated by the concepts of n dimensions Euclidean distance method: ˤ ˰ . ˰ $ - ˰ . ˰ $ = # $9 ˪ ŵ Ŷ ˭ ˤ ˰ . ˰ $ - ˰ . ˰ $ = # $9 ˪ ŵ Ŷ ˭ For determining rank of each alternative we are calculating the closeness coefficient as below: Iˬ ˤ ˤ - ˤ ˪ ŵ Ŷ ˭ Based on closeness coefficient we can rank alternatives and select the best one. NUMERICAL EXAMPLE Identifying the Factors, Options in the Model As it was mentioned in literature review, after analyzing the literature of the subject and identifying the factors, the most important factors of facility lay out planning were selected. These factors are taken from the manufacturing plant managers and experts. These factors are: Row Criteria Code 1 Cost C1 2 Time C2 3 Transportation C3 4 Capacity C4 5 Ease of Use C5 6 Flexibility C6 Table 1: Identified Criteria Form Literature and Experts' Viewpoint The abbreviations of decision makers are: Plans Code Decision Maker Code Layout Plan 1 A1 DM 1 D1 Layout Plan 2 A2 DM 2 D2 Layout Plan 3 A3 DM 3 D3 Layout Plan 4 A4 DM 4 D4 DM 5 D5 Table 2: Abbreviation of model components Decision Making Matrix In the real world we are utilizing some fuzzy and vague statements rather than some crisp terms (Zimmermann, 1997). Very low, low, middle, high and very high are some examples of linguistic terms. Fuzzy numbers can stand for these linguistic terms in a mathematical model.
  7. 7. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 In this paper the importance weights of the ratings of qualitative criteria are considered as linguistic variables. Abbreviation Importance Fuzzy Number a b c d VL Very Low (0,0,0.1,0.2) 0 0 1 2 L Low (0.1,0.2,0.2,0.3) 1 2 2 3 ML Modest Low (0.2,0.3,0.4,0.5) 2 3 4 5 M Medium (0.4,0.5,0.5,0.6) 4 5 5 6 MH Modest High (0.5,0.6,0.7,0.8) 5 6 7 8 H High (0.7,0.8,0.8,0.9) 7 8 8 9 VH Very High (0.9,0.9,1,1) 8 9 10 10 Table 3: Linguistic Variables and Fuzzy Numbers The following tables are showing the calculated results from solving the model based on mentioned steps in TOPSIS Method. Judgments of Experts about Criteria: Crite ria D1 D2 D3 D4 D5 C1 (0.4,0.5,0.5,0. 6) M (0.7,0.8,0.8 ,0.9) H (0.7,0.8,0.8 ,0.9) H (0.4,0.5,0.5 ,0.6) M (0.7,0.8,0.8 ,0.9) H C2 (0.9,0.9,1,1) M (0.4,0.5,0.5 ,0.6) M (0.9,0.9,1,1 ) VH (0.7,0.8,0.8 ,0.9) H (0.4,0.5,0.5 ,0.6) M C3 (0.9,0.9,1,1) VH (0.9,0.9,1,1 ) VH (0.4,0.5,0.5 ,0.6) M (0.7,0.8,0.8 ,0.9) H (0.9,0.9,1,1 ) VH C4 (0.1,0.2,0.2,0. 3) L (0.4,0.5,0.5 ,0.6) M (0.7,0.8,0.8 ,0.9) H (0.9,0.9,1,1 ) V H (0.7,0.8,0.8 ,0.9) H C5 (0.9,0.9,1,1) VH (0,0,0.1,0.2 ) VL (0.1,0.2,0.2 ,0.3) L (0.4,0.5,0.5 ,0.6) M (0.4,0.5,0.5 ,0.6) M C6 (0.7,0.8,0.8,0. 9) H (0.7,0.8,0.8 ,0.9) H (0.7,0.8,0.8 ,0.9) H (0.1,0.2,0.2 ,0.3) L (0.4,0.5,0.5 ,0.6) M Table 4: Judgments of Experts about Criteria Judgments of Experts about Alternatives: ˓˓%˓$˓# ˖'˖˖%˖$˖#˖'˖˖%˖$˖#˖'˖˖%˖$˖#˖'˖˖%˖$˖# F M G FFFPFFF M P P M P P M P M P FFFPMP˕# M P FPP M G F M P FF M G F M P F M P M G FPF M P F˕$ M G F M G FGF M P FFFF M P M P M P M P FFF M P F˕% M P M G M G FF M P FFF M P PFF M G M G PPFPMG˕ G M G FF M G FF M G FFPF M G F M G M G M G FFMG˕' M G FGFF M P FF M P M P F M P M G M G G M P M G FFG˕ Table 5: Judgments of Experts about Alternatives
  8. 8. MEHDIZADEHETAL.:LAYOUTDESIGNANDRANKINGTHEFACILITYDESIGNOFCREAM.. IndianJ.Sci.Res.3(3):149-159,2014 Decisionmatrixgainedfromexperts'judgments: ˓˓%˓$˓# ˖'˖˖%˖$˖#˖'˖˖%˖$˖#˖'˖˖%˖$˖#˖'˖˖%˖$˖# (4,5,5,6) (5,6,7,8) (4,5,5,6) (4,5,5,6) (4,5,5,6) (1,2,2,3) (4,5,5,6) (4,5,5,6) (4,5,5,6) (2,3,4,5) (1,2,2,3) (2,3,4,5) (1,2,2,3) (2,3,4,5) (2,3,4,5) (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) ˕# (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) (5,6,7,8) (4,5,5,6) (2,3,4,5) (4,5,5,6) (4,5,5,6) (5,6,7,8) (4,5,5,6) (2,3,4,5) (4,5,5,6) (2,3,4,5) (5,6,7,8) (4,5,5,6) (1,2,2,3) (4,5,5,6) (2,3,4,5) (4,5,5,6) ˕$ (5,6,7,8) (4,5,5,6) (5,6,7,8) (4,5,5,6) (7,8,8,9) (4,5,5,6) (2,3,4,5) (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) (2,3,4,5) (4,5,5,6) (4,5,5,6) (4,5,5,6) (4,5,5,6) (2,3,4,5) (4,5,5,6) ˕% (2,3,4,5) (5,6,7,8) (5,6,7,8) (4,5,5,6) (4,5,5,6) (2,3,4,5) (4,5,5,6) (4,5,5,6) (4,5,5,6) (2,3,4,5) (2,3,4,5) (4,5,5,6) (4,5,5,6) (5,6,7,8) (5,6,7,8) (4,5,5,6) (4,5,5,6) (4,5,5,6) (1,2,2,3) (5,6,7,8) ˕ (7,8,8,9) (5,6,7,8) (4,5,5,6) (4,5,5,6) (5,6,7,8) (4,5,5,6) (4,5,5,6) (5,6,7,8) (4,5,5,6) (4,5,5,6) (5,6,7,8) (4,5,5,6) (5,6,7,8) (4,5,5,6) (5,6,7,8) (5,6,7,8) (5,6,7,8) (4,5,5,6) (4,5,5,6) (5,6,7,8) ˕' (5,6,7,8) (4,5,5,6) (7,8,8,9) (4,5,5,6) (4,5,5,6) (2,3,4,5) (4,5,5,6) (4,5,5,6) (5,6,7,8) (2,3,4,5) (4,5,5,6) (2,3,4,5) (5,6,7,8) (5,6,7,8) (7,8,8,9) (4,5,5,6) (5,6,7,8) (4,5,5,6) (4,5,5,6) (7,8,8,9) ˕ Table6:DecisionMatrixGainedFromExperts'Judgments Findings ˓˓%˓$˓# (4,5,5,6)(4,5,5,6)(2,3,4,5)(2,3,4,5)˕# (4,5,5,6)(4,5,5,6)(5,6,7,8)(5,6,7,8)˕$ (5,6,7,8)(4,5,5,6)(4,5,5,6)(2,3,4,5)˕% (4,5,5,6)(2,3,4,5)(5,6,7,8)(4,5,5,6)˕ (4,5,5,6)(4,5,5,6)(4,5,5,6)(5,6,7,8)˕' (5,6,7,8)(4,5,5,6)(2,3,4,5)(5,6,7,8)˕ Table7:DecisionMatrixGainedfromtheAverageOfExperts'Judgments
  9. 9. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 C1 C2 C3 C4 C5 C6 Weight s (0.8,0.9,1,1) (0.7,0.8,0.8,0.9 ) (0.7,0.87,0.93,1 ) (0.7,0.8,0.8,0.9 ) (0.8,0.9,1,1 ) (0.7,0.8,0.8,0.9 ) A1 (8,9,10,10) (5,7,8,10) (7,8,8,9) (8,9,10,10) (7,8,8,9) (5,7,8,10) A2 (7,8.33,8.67,10 ) (8,9,10,10) (7,8,8,9) (7,8.67,9.33,10 ) (8,9,10,10) (5,7,8,10) A3 (8,9,10,10) (7,8.33,8.67,10 ) (5,7,8,10) (8,9,10,10) (5,7,8,10) (7,8.67,9.33,10 ) A4 (7,8,8,9) (5,7.33,7.67,9) (8,9,10,10) (7,8,8,9) (5,7,8,10) (7,8,8,9) Table 8: Fuzzy Decision Matrix C1 C2 C3 C4 C5 C6 A1 (0.8,0.9,1,1) (0.8,0.9,1,1) (0.7,0.8,0.8,0 .9) (0.7,0.833,0.86 7,1) (0.7,0.8,0.8,0.9) (0.8,0.9,1,1) A2 (0.7,0.8,0.8,0.9) (0.8,0.9,1,1) (0.8,0.9,1,1) (0.7,0.867,0.93 3,1) (0.8,0.9,1,1) (0.7,0.867,0.93 3,1) A3 (0.7,0.867,0.93 3,1) (0.7,0.833,0.867, 1) (0.5,0.6,0.7,0 .8) (0.8,0.9,1,1) (0.7,0.833,0.86 7,1) (0.7,0.867,0.93 3,1) A4 (0.7,0.867,0.93 3,1) (0.5,0.733,0.767, 0.9) (0.7,0.8,0.8,0 .9) (0.8,0.9,1,1) (0.7,0.833,0.86 7,1) (0.7,0.8,0.8,0.9) Table 9: Normalized Decision Matrix Positive and negative ideal solutions: A+ (0.9,0.9,0.9,0.9) (1,1,1,1) (1,1,1,1) (0.9,0.9,0.9,0.9) A- (0.35,0.35,0.35,0.35) (0.4,0.4,0.4,0.4) (0.35,0.35,0.35,0.35) (0.35,0.35,0.35,0.35) Table 10: Positive and Negative Ideal Solutions Criteria distances from positive ideal solutions: C1 C2 C3 C4 C5 C6 d(A1,A+ ) 0.36 0.39 0.27 0.41 0.36 0.27 d(A2,A+ ) 0.38 0.28 0.33 0.38 0.22 0.41 d(A3,A+ ) 0.29 0.38 0.33 0.27 0.38 0.21 d(A4,A+ ) 0.25 0.47 0.28 0.31 0.41 0.33 Table 11: Criteria Distances from Positive Ideal Solutions Criteria distances from negative ideal solutions: C1 C2 C3 C4 C5 C6 d(A1,A- ) 0.26 0.27 0.31 0.26 0.25 0.39 d(A2,A- ) 0.37 0.36 0.41 0.28 0.29 0.30 d(A3,A- ) 0.28 0.28 0.36 0.37 0.31 0.21 d(A4,A- ) 0.28 0.26 0.26 0.29 0.28 0.41 Table 12: Criteria Distances from Negative Ideal Solutions
  10. 10. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 Alternative distances from positive and negative ideal solutions: Positive ideal solutions Negative ideal solutions S1 + 2.013 S1 - 1.156 S2 + 2.118 S2 - 2.198 S3 + 1.933 S3 - 1.591 S4 + 1.856 S4 - 1.401 Table 13: Alternative Distances from Positive and Negative Ideal Solutions Calculating CCI of layout planning: CCI CC1 0.381 CC2 0.761 CC3 0.573 CC4 0.439 Table 14: Calculating CCI of Layout Planning Final Rank of layout plans: Third Plan A3 0.733 First Plan A1 0.701 Second Plan A2 0.624 Fourth Plan A4 0.598 Table 15: Final Rank of Plans DISCUSSION AND CONCLUSIONS Facility lay out is one of the basic elements of design and choice of plants is required rigorous and comprehensive evaluation. The current method used in this company is accurate and documented, and sometimes causes personal opinions in order to be involved and causes the target to be generated. Therefore, the evaluation and selection of appropriate design criteria of the company needed a system that has a predetermined amount of choices and decisions to pursue certain. The proposed method is tried to apply both criteria and managers’ options so that a certain pattern for choose the best plan to be there. As the results are shown, the third highest rated program, has won first place from among four designs Rank obtained indicate that the third design criteria relative to other proposals are in the best position. If the administrator wants to set the criteria set by the best cost, time, capacity, performance and achieved, we plan to use. The plan, first, second and fourth, respectively, allocated to the next station. Given that the design layout is opportunity which rarely obtained and to replace and redesign frequently not possible, managers should try to Choose the best layout possible through which they can achieve their goals. REFERENCES Gonzalez-Cruz M.C. and Martinez E. G-S., An entropy-based algorithm to solve the facility layout design problem, Roboticsand Computer-Integrated Manufacturing., 27(1):88-100, 2011. Heragu S.S. and Kusiak A., Efficient models for the facility layout problem, European journal of operational research. 53(1):1-13, 1991. Houshyar A., Computer aided facility layout: An interactive multi-goal approach, Computers and Industrial Engineering ., 20(2):177-186, 1993. Karray F., Zaneldin E., Hegazy T., Shabeeb A.H.M., Elbeltagi E., Tools of soft computing as applied to the problem offacilities layout planning, IEEE Transactions On Fuzzy Systems, 8(4):367-379, 2000. Shang J.S., Multi-criteria facility layout problem: An integrated approach, European Journal
  11. 11. MEHDIZADEH ET AL.: LAYOUT DESIGN AND RANKING THE FACILITY DESIGN OF CREAM.. Indian J.Sci.Res. 3(3) : 149-159, 2014 of Operational Research., Vol. 66, No. 3, pp. 291-304, 1993. Yang T., Hung Chih-Ching., Multiple-attribute decision making methods for plant layout design problem, Robotic and Computer- Integrated Manufacturing., Vol. 23, No. 1, pp. 126-137, 2007. Yang T., KuoChunwei., A hierarchical AHP/DEA methodology for the facilities layout design problem, European Journal ofOperational research. Vol. 147, No. 1, pp128-136, 2003. Zimmermann H-J. Fuzzy Mathematical Programming, chapter 15. In: Gal T, Greenberg HJ, eds. Advances in Sensitivity Analysis and Parametric Programming. Boston: Kluwer; 1997.

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