ดาวน์โหลด All Proceeding - AS Nida

ดาวน์โหลด All Proceeding - AS Nida ดาวน์โหลด All Proceeding - AS Nida

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Figure 3 Chromosome representation. 3. Machine Layout Design Problem In manufacturing contexts, machine layout design is involved in the process of arranging machines, most of which are in different sizes and rectangular shape, on shop floor area in multiple row layout configuration. The material handling device is the automated guided vehicles (AGV). An example of multiple-row machine layout design was shown in figure 4. Machines are arranged row by row by starting in row 1 (R1) from left to right based on F L and gap between machines (G). When there is not enough area for the next machine, it will then be placed in the next row. Movement of AGV between rows can be conducted either by moving to the left or the right side of the row and then moving up or down to the destination row. The distance of In this work, the following assumptions have been made in order to simplify and formulate the problem: i) machine alignment is fixed or non-rotatable; ii) the material handling distance between machines is determined from the centroid of machine; iii) there are enough sizes of shop floor area for machine arrangement; iv) the movement of AGV is a straight line; v) a gap between machines is similar and vi) the quantity of products, processing time and moving time are not taken into consideration. 4. Experimental results In this work, the computational experiment was conducted using four MLD benchmarking datasets (shown in table 1) adopted from literature [24]. The datasets are in various sizes according to the 254 material handling is evaluated from the shortest distance such as transportation of materials from M12 to M4. There are two choices: route (3) or (4). Because route (3) is shorter than (4), thus they are transported with route (3). The operated point of each machine is centroid. The objective function is to minimise the material handling distance as equation (1). = z M M ∑∑ j= 1 i= 1 Figure 4 Example of multiple-row machine layout design [23]. ij ij d f ; i ≠ j (1) M is a number of machines, i and j is machine sequences (i and j = 1, 2, 3,…, M), f ij is frequency of material flow between machine i and j, d ij is distance between machine i and j. number of machines and products, for example, dataset M10P3 means that there are three products to be processed on ten non-identical rectangular machines. The machine layout designing program was developed and coded in modular style using the Tool Command Language and Tool Kit (Tcl/Tk) programming language [25]. An experiment was designed and conducted on personal computer with Intel Core i5 2.8 GHz and 4 GB DDR3 RAM. The probabilities of crossover and mutation adopted in this work were set at 0.9 and 0.5 respectively. These values have been investigated in previous work [26]. The population size and number of generation had a considerable effect on the amount of search in the solution space. It should therefore be related to the problem size.

Figure 3 Chromosome representation.<br />

3. Machine Layout Design Problem<br />

In manufacturing contexts, machine layout design is<br />

involved in the process of arranging machines, most of which are in<br />

different sizes and rectangular shape, on shop floor area in multiple row<br />

layout configuration. The material handling device is the automated<br />

guided vehicles (AGV). An example of multiple-row machine layout<br />

design was shown in figure 4. Machines are arranged row by row by<br />

starting in row 1 (R1) from left to right based on F L and gap between<br />

machines (G). When there is not enough area for the next machine, it<br />

will then be placed in the next row. Movement of AGV between rows<br />

can be conducted either by moving to the left or the right side of the<br />

row and then moving up or down to the destination row. The distance of<br />

In this work, the following assumptions have been made in<br />

order to simplify and formulate the problem: i) machine alignment is<br />

fixed or non-rotatable; ii) the material handling distance between<br />

machines is determined from the centroid of machine; iii) there are<br />

enough sizes of shop floor area for machine arrangement; iv) the<br />

movement of AGV is a straight line; v) a gap between machines is<br />

similar and vi) the quantity of products, processing time and moving<br />

time are not taken into consideration.<br />

4. Experimental results<br />

In this work, the computational experiment was conducted<br />

using four MLD benchmarking datasets (shown in table 1) adopted<br />

from literature [24]. The datasets are in various sizes according to the<br />

254<br />

material handling is evaluated from the shortest distance such as<br />

transportation of materials from M12 to M4. There are two choices:<br />

route (3) or (4). Because route (3) is shorter than (4), thus they are<br />

transported with route (3). The operated point of each machine is<br />

centroid. The objective function is to minimise the material handling<br />

distance as equation (1).<br />

= z<br />

M<br />

M<br />

∑∑<br />

j=<br />

1 i=<br />

1<br />

Figure 4 Example of multiple-row machine layout design [23].<br />

ij ij d f<br />

; i ≠ j (1)<br />

M is a number of machines, i and j is machine sequences (i<br />

and j = 1, 2, 3,…, M), f ij is frequency of material flow between machine<br />

i and j, d ij is distance between machine i and j.<br />

number of machines and products, for example, dataset M10P3 means<br />

that there are three products to be processed on ten non-identical<br />

rectangular machines. The machine layout designing program was<br />

developed and coded in modular style using the Tool Command<br />

Language and Tool Kit (Tcl/Tk) programming language [25]. An<br />

experiment was designed and conducted on personal computer with<br />

Intel Core i5 2.8 GHz and 4 GB DDR3 RAM.<br />

The probabilities of crossover and mutation adopted in this<br />

work were set at 0.9 and 0.5 respectively. These values have been<br />

investigated in previous work [26]. The population size and number of<br />

generation had a considerable effect on the amount of search in the<br />

solution space. It should therefore be related to the problem size.

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