Design of Group Technology and Improvement by using Genetic Algorithm

Design of Group Technology and Improvement by using

Genetic Algorithm

Assist. Prof. Raqeyah Jawad Najy

Foundation Technical Education-Iraq

Babylon Technical Institute, AL-Furat Alawsat Technical University

Abstract – Because of the failure of the internal

arrangement of the plant to achieve the strategic objectives of the operations and the lack of an efficient production process flow. Cellular manufacturing emerged as a production strategy capable of solving the certain problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This research presents a new approach for obtaining machine cells and product families. The approach combines a local heuristic with a genetic algorithm(GA). Computational experience produced solution with the algorithm on a set of group technology (GT) problems available in the research is also presented. It was the application of this new entrance (genetic algorithm (GA)) in plastic bags factory in Hilla-Iraq, to the families of the design of products and machinery cells and improve the efficiency of the group for nearly 59% of those problems.

Keywords – Genetic Algorithm, Generate Groups, Initial Matrix, Final Matrix.

I.

I

NTRODUCTION

Knew (Burbidge, 1979)[5]&[3]. Group technology as the optimal entrance to work in units of regulatory production is independent groups Nspaa.taty all responsibility for the production of a family of Almentojat.ahd fundamental problems in cellular manufacturing are the families of the products and the cells machines lineup, the goal is to master the Group range from (machines and products), so to not have the ability to move products from one cell to another through Almaaljh. and conceptual level, the formation of cell models ignore several manufacturing factors are discussed only mechanization of products operations, so it is a manufacturing system by a bilateral mechanism-event matrix portion ((A, a matrix (0-1) for the system (PXM) when P is equal to the number of Products and M is equal to the number of machines, the item is a (p, m) = 1, the product P goes to the machine M, except that the a (p, m) = 0.

There are several ways to form a cell, it gave each of the (Wemmerlov & Hyer, 1989) and (Selim, Askin & Vakharia, 1998)[9]&[4]. A review of the value of many of the procedures that are based on the type of general methodology gives solutions through the use of the entrance of a genetic algorithm.

Provided genetic algorithm by Holand in 1975[14]. and applied in several areas (math, engineering, biological, and social sciences) (Goldberg, 1989)[15]. The concept is based genetic algorithm on the evaluation process that

occurs in the natural biological (search based on the mechanical natural selection and natural genetic algorithms) .

II.

R

ESEARCH

M

ETHODOLOGY

A. Research Problem:

It can be summarized as follows:

1-absence means that help to implement immediate production (JIT).

2-The increase in working hours and a private (configuration and setup times and processing time). 3-absence of an efficient flow of productive process. 4-low level of skill and experience of workers. 5-poor public relations, especially between personnel. 6-failure of the internal arrangement of the factory in achieving the strategic objectives of the operations. 7-accumulation of materials and the large inventory.

B. The importance of research:

Highlights the importance of research in the possibility of the application of industrial organizations of the philosophy of group technology and cellular manufacturing, through the provision of certain tools and applying modern methods and techniques to take advantage of this technology advantages and access to the most efficient flow of work then achieve the organization's goals through quick access to the customer.

C. Research objectives:

Research seeks to achieve the following objectives: 1. Reduce setup and treatment and to reduce the quantities of materials and pieces during the stages of production time.

2. Raise the level of skill and experience of staff and improve the relations between them.

3. Achieve an efficient flow of productive process by arranging Products as families and machinery as cells and raise the efficiency of these groups through the use of entrance genetic algorithm.

D. conduct research site:

plastic bags factory in Hilla. E. the temporal boundaries of the search were searched from 21/06/2013 - until 15.9.2014.

III.

P

RACTICAL

S

IDE

reduce the input cell movement and get the maximum benefit to the machines within the cell.

2. Performance Scale:

Measure of Performance. There are several good measures of aggregates product-machine in cellular manufacturing, gave each of the [14]&[2] simulation study of the effects of some factors on the efficiency standards, as provided high new standard of quality for measuring the efficiency of the group, and the efficiency of the group is a simple application to implement the generation of country matrices of products and machines, known by the efficiency of the group [7]&[1] with all of the benefit of the machine (Machine Utilization), and movement within the cell (Inter-Cell Movement), a total weighted Daltaμ1, μ2)).

Group efficiency = μ = qμ1 + (1-q) μ2

Μ1tmthel when the ratio between the number (1) in the country, block to the total number of elements in the country, block the final matrix.

μ2 ratio of the number (0) in the country, block out the total number of elements in the country, block out the final matrix. q factor weight and gave each of [12] another measure of the efficiency of the group and also come and that will be relied upon in this search.

Group efficiency = μ = N1-N1out / N1 + N0In When the total number N1 (1) in the matrix A N1out total number (1) out of the country, block N0In total number of (0) within the country, block

3. The new portal (genetic algorithm):

The New Approach (Genetic Algorithm) Using the (GA) to generate groups of machines cells, to improve the quality of the group of aggregates machines generating cells, and research indicative site is an application for aggregates machines cells that have been generated by a genetic algorithm (GA). The research indicative site for the construction of aggregates (product / machine) and

improved , The (GA) using alphabetic random numbers (and-Musumaat) between the (0,1) μ represent this hit-and-Musumaat solution to the problem.

And had algorithm (GA) calculates the corresponding value (quality) for each Chrome tags in the community, and this value (the corresponding value) is only for feedback (genetic algorithm).

Consists of hit-and-marked from three parts: the elements (component, machine, group).

The general formula c1c2c3 ... ..cp / m1m2m3 ... Mm / g1g2g3 ... Gg

When ci items or products where i = 1 to P mj machines where j = 1 to M

gk aggregates where k = 1 to G G = Max {m1, m2, m3 ... ..Mm} P Number of Products in question

M number of machines involved in the matter

G number of aggregates in solution where G <= min (M, P) agree with all of them [6]&[8].

4. actual application:

Before starting the actual details of the application should be noted that the plastic bags factory machines arranged randomly in the production hall, noting that its products are similar (17 products) and various machines (14 machine Austrian and Japanese origin) and the products are as follows:

1. filament bundles currency produces three types (large, medium, small).

2. plastic rope (to produce seven different types).

3. plastic bags (to produce seven types). Thus, the sum of the products (17) product.

Based upon the initial matrix (Initial Matrix) in the factory (a single cell containing all the machines and products are as follows (one group containing (G = (P, M) = (17,14):

Table 1: (A) Initial Matrix Machine

M14 M13 M12 M11 M10 M9 M8 M7 M6 M5 M4 M3 M2 M1 Product

1 1

1

1 1 1

1 2

1 1

1 3

1 1

1 4

1 1

1 5

1 1

1 6

1 1

7

1 1 1

1 8

1 1

1 9

1 1

1 10

1 1 1

1 11

1 1

12

1 1

13

1 1

1 14

1 1

1 1

15

1 1

16

1 1

1 17

The final matrix will be the country (diameter = 1) and the rest of the elements = Zero was created through the

distribution of similar products between machines (machines is similar), was obtained as follows:

Table 1 (B) Final Matrix Machine

M14 M13 M9 M2 M11 M4 M1 M12 M10 M7 M5 M3 M8 M6 Product

1 1 1 3

1 1 1 5

1 1 7

1 1 1 9

1 1 1

10

1 1

1 14

1 1 1 1 15

1 1

16

1 1 1 17

1 1 1

1 1 1 4

1 1 1 6

1 1 12

1 1

13

1 1 1 1 2

1 1 1 1 8

1 1 1 1 11

(Prepared by the researcher)

According to the final matrix of the units (cells) obtained are as follows:

Products Machines

Cell

3,5,7,9 M6,M8,M3

1

10,14,15,16,17 M5,M7,M10,M12

2

1,4,6,12,13 M1,M4,M11

3

2,8,11 M2,M9,M13,M14

4

5- Calculate the efficiency of the group, according to preliminary matrix (A)

GE = μa = N 1-N 1 Out / N1 + N0 = 50-0 / 50 + 188 = 50/238 = 21%

GE = μb = 50-0 / 50 + 9 = 84.74% [10].

IV.

N

OW

,

USING THE

S

TANDARD

G

ENETIC

A

LGORITHM

(GA)

ARE

:

1. Generate a hit-and-Musumaat and find the number of units (cells) and the distribution of the machines. 2. Action heuristic search my totals for the distribution of the resulting products on the machines.

3. Develop matrix accordingly (according to the step 2). 4. Extraction efficiency of the Group.

5. Return to the machines that were extracted first and finding efficiency cells.

6. Selection of top efficiency Pinha.oimkn generate a number of cells using a genetic algorithm and compared with the first and choose the top. (Good efficiency of 75% or more, and when accessed is discontinued). In the case of lack of access to the efficiency by 75%, we continue to generate new cells and re-work all the steps to get to the efficiency of 75% or more then is discontinued. [13].

1-Chromosome Generation

As the number of machines = 14, we extract 15 chromosome because the latter is used to extract the number of cells, as follows:

Chromosome =

0.72,0.88,0.12,0.62,0,38,0.79,0.42,0.19,0.93,0.65,0.69,0.3 7,0.25,0.22,0.29))

The number of cells = 0.29 * number of machines = 0.29 * 14 = 4 cell

• 0.42 --- M7 go to a cell (0.42 * 4) = 2 • 0.19 --- M8 go to a cell (0.19 * 4) = 1 • 0.93 --- M9 go to a cell (0.93 * 4) = 4 • 0.65 --- M10 go to a cell (0.65 * 4) = 3 • 0.69 --- M11 go to a cell (0.69 * 4) = 3 • 0.37 --- M12 go to a cell (0.37 * 4) = 2 • 0.25 --- M13 go to a cell (0.25 * 4) = 1 • 0.22 --- M14 go to a cell (0.22 * 4) = 1 [12] & [7].

Based upon the aggregates (machines resulting cells) are: Cell 1 = {M3, M8, M13, M14}

Cell 2 = {M5, M7, M12} Cell 3 = {M1, M4, M10, M11} Cell 4 = {M2, M6, M9}

2. conduct research and extension in situ extraction efficiency of each machine and each cell product according to the following equation: μc = {N1-N1.cout / N1 + N0.c} As in Table (2).

Which represents: step (1) calculate the efficiency of the machinery of the cells indicative search site.

Table 2: Calculate the efficiency of the Machines cells Machines Cells

(M2,M6,M9) (M1,M4,M10,M11)

(M5,M7,M12) (M3,M8,M13, M14)

µc(cell efficiency) µc(cell efficiency)

µc(cell efficiency) µc(cell efficiency)

Product Machines Product (50-2)/(50+3)=90.5% (50-0)/(50+2)=96% (50-2)/(50+3)=90.5% (50-2)/(50+4)=88.8% M1,M4 1 (50-2)/(50+1)=94% (50-4)(50+4)=85% (50-4)/(50+3)=86.7% (50-2)/(50+2)=92% M2,M9,M13,M14 2 (50-2)/(50+2)=92% (50-3)/(50+4)=87% (50-3)/(50+3)=88.6% (50-1)(50+2)=94% M3,M6,M8 3 (50-3)/(50+3)=88.6% (50-0)/(50+1)=98% (50-3)/(50+3)=88.6% (50-3)/(50+4)=87% M1,M4,M11 4 (50-2)/(50+2)=92% (50-3)/(50+4)=87% (50-3)/(50+3)=88.6% (50-1)/(50+2)=94% M3,M6,M8 5 (50-3)/(50+3)=88.6% (50-0)/(50+1)=98% (50-3)/(50+3)=88.6% (50-3)/(50+4)=87% M1,M4,M11 6 (50-2)/(50+3)=90.5% (50-2)/(50+4)=88.8% (50-2)/(50+3)=90.5% (50-0)/(50+2)=96% M3,M8 7 (50-2)/(50+1)=94% (50-4)/(50+4)=85% (50-4)/(50+3)=86.7% (50-2)/(50+2)=92%

M2, M9, M13, M14 8 (50-2)/(50+2)=92% (50-3)/(50+4)=87% (50-3)/(50+3)=88.6% (50-1)/(50+2)=94% M3,M6,M8 9 (50-3)/(50+3)=88.6% (50-2)/(50+3)=90.5% (50-1)/(50+1)=96% (50-3)/(50+4)=87% M5,M10,M12 10 (50-2)/(50+1)=94% (50-4)/(50+4)=85% (50-4)/(50+3)=86.7% (50-2)/(50+2)=92% M2,M9,M13, M14 11 (50-2)/(50+3)=90.5% (50-0)/(50+2)=96% (50-2)/(50+3)=90.5% (50-2)/(50+4)=88.8% M4,M11 12 (50-2)/(50+3)=90.5% (50-0)/(50+2)=96% (50-2)/(50+3)=90.5% (50-2)/(50+4)=88.8% M1,M11 13 (50-3)/(50+3)=88.6% (50-3)/(50+4)=87% (50-0)/(50+0)=100% (50-3)/(50+4)=87% M5,M7,M12 14 (50-4)/(50+3)=86.7% (50-3)/(50+3)=88.6% (50-1)/(50+0)=98% (50-4)/(50+4)=85% M5,M7,M10, M12 15 (50-2)/(50+3)=90.5% (50-1)/(50+3)=92% (50-1)/(50+2)=94% (50-2)/(50+4)=88.8% M5,M10 16 (50-3)/(50+3)=88.6% (50-2)/(50+3)=90.5% (50-1)/(50+1)=96% (50-3)/(50+4)=87% M5,M7,M10 17

1 0 1 1

1 2

1 0 1 1

1 8

1 0 1 1

1 11

The table comes (4), which represents accounts (Step 2) Find indicative site.

Table 4: Step (2) the efficiency of aggregates products in accounts Products groups

(2,8,11) (1,4,6,12,13)

(10,14,15,16,17) (3,5,7,9)

µF(efficiency) µF(efficiency)

µF(efficiency) µF(efficiency)

Products Machine

(50-4)/(50+3)=86.7% (50-0)/(50+1)=98%

(50-4)/(50+5)=83.6% (50-4)/(50+4)=85%

1,4,6,13 M1

(50-0)/(50+0)=100% (50-3)(50+5)=85%

(50-3)/(50+5)=85% (50-3)/(50+4)=87%

2,8,11 M2

(50-4)/(50+3)=86.7% (50-4)/(50+5)=83.6%

(50-4)/(50+5)=83.6% (50-0)(50+0)=100%

3,5,7,9 M3

(50-4)/(50+3)=86.7% (50-0)/(50+1)=98%

(50-4)/(50+5)=83.6% (50-4)/(50+4)=85%

1,4,6,12 M4

(50-5)/(50+3)=84.9% (50-5)/(50+5)=81.8%

(50-0)/(50+0)=100% (50-5)/(50+4)=83.3%

10,14,15,16,17 M5

(50-3)/(50+3)=88.6% (50-3)/(50+5)=85%

(50-3)/(50+5)=85% (50-0)/(50+1)=98%

3,5,9 M6

(50-3)/(50+3)=88.6% (50-3)/(50+5)=85%

(50-0)/(50+2)=96% (50-3)/(50+4)=87%

14,15,17 M7

(50-4)/(50+3)=86.7% (50-4)/(50+5)=83.6%

(50-4)/(50+5)=83.6% (50-0)/(50+0)=100%

3,5,7,9 M8

(50-0)/(50+0)=100% (50-3)/(50+5)=85%

(50-3)/(50+5)=85% (50-3)/(50+4)=87%

2,8,11 M9

(50-3)/(50+2)=90% (50-4)/(50+5)=83.6%

(50-1)/(50+2)=94% (50-4)/(50+4)=85%

11,15,16,17 M10

(50-4)/(50+3)=86.7% (50-0)/(50+1)=98%

(50-4)/(50+5)=83.6% (50-4)/(50+4)=85%

4,6,12,13 M11

(50-3)/(50+3)=82.6% (50-3)/(50+5)=85%

(50-0)/(50+2)=96% (50-3)/(50+4)=87%

10,14,15 M12

(50-0)/(50+0)=100% (50-3)/(50+5)=85%

(50-3)/(50+5)=85% (50-3)/(50+4)=87%

2,8,11 M13

(50-0)/(50+0)=100% (50-3)/(50+5)=85%

(50-3)/(50+5)=85% (50-3)/(50+4)=87%

2,8,11 M14

Table 5: Distribution of products on the machines are as follows:

Products Machines

Cell

3,5,7,9 M3,M8,M13,M14

1

10,14,15,16,17 M5,M7,M12

2

1,4,6,12,13 M1,M4,M10,M11

3

2,8,11 M2,M6,M9

4

In other words, efficiency, according to the schedule (5) are:

2 1

 = (50-0) / (50+9)=84.74%

But according to schedule efficiency (3) are: 1

1

 = (50-13) / (50+23) µ=MAX 2

1  , 1

1

 )(According to the following law (will be selected top efficiency = 84.74% ₁2 µ=

V.

C

ONCLUSIONS AND

R

ECOMMENDATIONS

A. Conclusions own factory plastic bags.

1. The application of the GT and improve their performance according to the GA did not add any costs of the plant.

2. led to the palace-made cycle Palace row materials and purchases as well.

3. decreases in the time of preparation tools and treatment. 4. Improve the internal arrangement of the plant.

5. streamline production planning and control.

B. Recommendations for the manufacturer of plastic

bags:

1. Develop the skills of workers and capabilities through education and training and make great effort in trying to earn their loyalty and dedication to work and organization by clarifying business goals to them in detail and motivate them financially and morally and remove high barriers between management and supervision, including through making strong relationships based on mutual between the parties confidence.

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