- Text
- History
TSþþ extends TSþ with the addition
TSþþ extends TSþ with the addition that the cocoa farmers,
when packing the cocoa beans, already mark all bags with unique
codes and date. In this case the finished chocolate is traceable up to
the individual cocoa farmer. Alternatively, the local buying station
could mark the bags at arrival, with the information of the farmer
delivering the beans.
In Fig. 3 the different traceability systems can be seen.
3.3.3. Different product recalls
The simulation model is able to simulate two possible food
crises and corresponding recalls (R1 and R2) that could occur in the
case study supply chain. R1 simulates the product recall in case of
a contamination of the cocoa beans, which could be a chemical
contamination while farming, fermenting or drying. In this case all
chocolate bars produced with cocoa beans from a certain cocoa
farmer need to be recalled. R2 simulates the product recall in case
of a contamination of a processing batch, which could be caused by
a problem in a roasting process. In this case all chocolate bars
produced in a certain roasting process need to be recalled. The
simulation models allow to run single and multiple simulations.
Due to the importance of the roasting process it is also possible to
run single or multiple simulations automatically for different processing
batch sizes. For this paper, we simulated the food scares for
a range of processing batch sizes between 1,600 kg and 5,000 kg
(every multiple of 200 kg). Each of the sizes is then run multiple
times, while information such as number of runs, processing batch
size, recall size and number of processing batches (which reflects
the production efficiency) is registered, and average results can be
determined.
4. Results and discussion
4.1. Comparing different production strategies
In order to compare the production strategy based on production
efficiency (PS1) to the production strategy based on reduced
batch dispersion (PS2) the simulation model has been run for
different processing batch sizes. The average results are shown in
Fig. 4 as the difference in percentages between the two production
strategies (values of PS1 represent the 100%) in (i) production
efficiency, (ii) recall size in case of contamination of the raw
materials (R1) and (iii) recall size in case of contamination of
a processing batch size (R2).
PS2 is a production strategy where the batches of raw materials
are not mixed. Thus, the last processing batch from a raw material
batch might not fully occupy the batch processing equipment. That
is, the production equipment that processes this smaller batch will
only be partially utilized. The actual overall utilization therefore
depends on the processing batch size b and the raw materials batch
size n. Combined, these two factors lead to a required number of
processing batches r (with r ¼ n/b) and a utilization of
u ¼ r
QrS
100% (1)
where QrS denotes the smallest integer larger than or equal to r. For
situations in which the processing batch size is fixed (as can be
expected in industry), but raw material batch size varies, this leads
to different expected utilizations for each possible processing batch
size b:
ub ¼ 1
jNjX
i˛N
ri
QriS
(2)
where ri is the required number of batches for raw material batch
size ni, i ˛ N an index representing the different possible raw
material batch sizes used in the simulation (based on the uniform
distribution discussed in Section 2.2), and rNr the number of
elements in set N. The expected process utilization value ub for the
processing batch sizes simulated in our case study when using PS2
can be seen in Fig. 5.
Fig. 5 shows that, by simulating a large number of possible raw
material batch sizes, and thus a large set N, the expected utilization
ub varies significantly. When producing with PS1, the raw material
batches are mixed, always processing with fully occupied batch
processing equipment, thus reaching high utilization in the
production processes. Therefore, the number of processing batches
for PS1, representing production efficiency, has a decreasing trend
for an increasing processing batch size. As the decrease in the
number of processing batches for PS2 is not constant, the
percentage difference between the two production strategies is not
increasing at a constant rate in Fig. 4. This is most visible at processing
batch sizes around 4,400 kg, but smaller incontinuities can
be noticed around 2,800 kg and 3,600 kg. A chocolate manufacturer
that produces with a PS2 production strategy should therefore take
the expected utilization value ub as calculated in (2) and illustrated in Fig. 5 into account when deciding on the size of the processing
batches.
when packing the cocoa beans, already mark all bags with unique
codes and date. In this case the finished chocolate is traceable up to
the individual cocoa farmer. Alternatively, the local buying station
could mark the bags at arrival, with the information of the farmer
delivering the beans.
In Fig. 3 the different traceability systems can be seen.
3.3.3. Different product recalls
The simulation model is able to simulate two possible food
crises and corresponding recalls (R1 and R2) that could occur in the
case study supply chain. R1 simulates the product recall in case of
a contamination of the cocoa beans, which could be a chemical
contamination while farming, fermenting or drying. In this case all
chocolate bars produced with cocoa beans from a certain cocoa
farmer need to be recalled. R2 simulates the product recall in case
of a contamination of a processing batch, which could be caused by
a problem in a roasting process. In this case all chocolate bars
produced in a certain roasting process need to be recalled. The
simulation models allow to run single and multiple simulations.
Due to the importance of the roasting process it is also possible to
run single or multiple simulations automatically for different processing
batch sizes. For this paper, we simulated the food scares for
a range of processing batch sizes between 1,600 kg and 5,000 kg
(every multiple of 200 kg). Each of the sizes is then run multiple
times, while information such as number of runs, processing batch
size, recall size and number of processing batches (which reflects
the production efficiency) is registered, and average results can be
determined.
4. Results and discussion
4.1. Comparing different production strategies
In order to compare the production strategy based on production
efficiency (PS1) to the production strategy based on reduced
batch dispersion (PS2) the simulation model has been run for
different processing batch sizes. The average results are shown in
Fig. 4 as the difference in percentages between the two production
strategies (values of PS1 represent the 100%) in (i) production
efficiency, (ii) recall size in case of contamination of the raw
materials (R1) and (iii) recall size in case of contamination of
a processing batch size (R2).
PS2 is a production strategy where the batches of raw materials
are not mixed. Thus, the last processing batch from a raw material
batch might not fully occupy the batch processing equipment. That
is, the production equipment that processes this smaller batch will
only be partially utilized. The actual overall utilization therefore
depends on the processing batch size b and the raw materials batch
size n. Combined, these two factors lead to a required number of
processing batches r (with r ¼ n/b) and a utilization of
u ¼ r
QrS
100% (1)
where QrS denotes the smallest integer larger than or equal to r. For
situations in which the processing batch size is fixed (as can be
expected in industry), but raw material batch size varies, this leads
to different expected utilizations for each possible processing batch
size b:
ub ¼ 1
jNjX
i˛N
ri
QriS
(2)
where ri is the required number of batches for raw material batch
size ni, i ˛ N an index representing the different possible raw
material batch sizes used in the simulation (based on the uniform
distribution discussed in Section 2.2), and rNr the number of
elements in set N. The expected process utilization value ub for the
processing batch sizes simulated in our case study when using PS2
can be seen in Fig. 5.
Fig. 5 shows that, by simulating a large number of possible raw
material batch sizes, and thus a large set N, the expected utilization
ub varies significantly. When producing with PS1, the raw material
batches are mixed, always processing with fully occupied batch
processing equipment, thus reaching high utilization in the
production processes. Therefore, the number of processing batches
for PS1, representing production efficiency, has a decreasing trend
for an increasing processing batch size. As the decrease in the
number of processing batches for PS2 is not constant, the
percentage difference between the two production strategies is not
increasing at a constant rate in Fig. 4. This is most visible at processing
batch sizes around 4,400 kg, but smaller incontinuities can
be noticed around 2,800 kg and 3,600 kg. A chocolate manufacturer
that produces with a PS2 production strategy should therefore take
the expected utilization value ub as calculated in (2) and illustrated in Fig. 5 into account when deciding on the size of the processing
batches.
0/5000
TSþþ extends TSþ with the addition that the cocoa farmers,when packing the cocoa beans, already mark all bags with uniquecodes and date. In this case the finished chocolate is traceable up tothe individual cocoa farmer. Alternatively, the local buying stationcould mark the bags at arrival, with the information of the farmerdelivering the beans.In Fig. 3 the different traceability systems can be seen.3.3.3. Different product recallsThe simulation model is able to simulate two possible foodcrises and corresponding recalls (R1 and R2) that could occur in thecase study supply chain. R1 simulates the product recall in case ofa contamination of the cocoa beans, which could be a chemicalcontamination while farming, fermenting or drying. In this case allchocolate bars produced with cocoa beans from a certain cocoafarmer need to be recalled. R2 simulates the product recall in caseof a contamination of a processing batch, which could be caused bya problem in a roasting process. In this case all chocolate barsproduced in a certain roasting process need to be recalled. Thesimulation models allow to run single and multiple simulations.Due to the importance of the roasting process it is also possible torun single or multiple simulations automatically for different processingbatch sizes. For this paper, we simulated the food scares fora range of processing batch sizes between 1,600 kg and 5,000 kg(every multiple of 200 kg). Each of the sizes is then run multiple
times, while information such as number of runs, processing batch
size, recall size and number of processing batches (which reflects
the production efficiency) is registered, and average results can be
determined.
4. Results and discussion
4.1. Comparing different production strategies
In order to compare the production strategy based on production
efficiency (PS1) to the production strategy based on reduced
batch dispersion (PS2) the simulation model has been run for
different processing batch sizes. The average results are shown in
Fig. 4 as the difference in percentages between the two production
strategies (values of PS1 represent the 100%) in (i) production
efficiency, (ii) recall size in case of contamination of the raw
materials (R1) and (iii) recall size in case of contamination of
a processing batch size (R2).
PS2 is a production strategy where the batches of raw materials
are not mixed. Thus, the last processing batch from a raw material
batch might not fully occupy the batch processing equipment. That
is, the production equipment that processes this smaller batch will
only be partially utilized. The actual overall utilization therefore
depends on the processing batch size b and the raw materials batch
size n. Combined, these two factors lead to a required number of
processing batches r (with r ¼ n/b) and a utilization of
u ¼ r
QrS
100% (1)
where QrS denotes the smallest integer larger than or equal to r. For
situations in which the processing batch size is fixed (as can be
expected in industry), but raw material batch size varies, this leads
to different expected utilizations for each possible processing batch
size b:
ub ¼ 1
jNjX
i˛N
ri
QriS
(2)
where ri is the required number of batches for raw material batch
size ni, i ˛ N an index representing the different possible raw
material batch sizes used in the simulation (based on the uniform
distribution discussed in Section 2.2), and rNr the number of
elements in set N. The expected process utilization value ub for the
processing batch sizes simulated in our case study when using PS2
can be seen in Fig. 5.
Fig. 5 shows that, by simulating a large number of possible raw
material batch sizes, and thus a large set N, the expected utilization
ub varies significantly. When producing with PS1, the raw material
batches are mixed, always processing with fully occupied batch
processing equipment, thus reaching high utilization in the
production processes. Therefore, the number of processing batches
for PS1, representing production efficiency, has a decreasing trend
for an increasing processing batch size. As the decrease in the
number of processing batches for PS2 is not constant, the
percentage difference between the two production strategies is not
increasing at a constant rate in Fig. 4. This is most visible at processing
batch sizes around 4,400 kg, but smaller incontinuities can
be noticed around 2,800 kg and 3,600 kg. A chocolate manufacturer
that produces with a PS2 production strategy should therefore take
the expected utilization value ub as calculated in (2) and illustrated in Fig. 5 into account when deciding on the size of the processing
batches.
times, while information such as number of runs, processing batch
size, recall size and number of processing batches (which reflects
the production efficiency) is registered, and average results can be
determined.
4. Results and discussion
4.1. Comparing different production strategies
In order to compare the production strategy based on production
efficiency (PS1) to the production strategy based on reduced
batch dispersion (PS2) the simulation model has been run for
different processing batch sizes. The average results are shown in
Fig. 4 as the difference in percentages between the two production
strategies (values of PS1 represent the 100%) in (i) production
efficiency, (ii) recall size in case of contamination of the raw
materials (R1) and (iii) recall size in case of contamination of
a processing batch size (R2).
PS2 is a production strategy where the batches of raw materials
are not mixed. Thus, the last processing batch from a raw material
batch might not fully occupy the batch processing equipment. That
is, the production equipment that processes this smaller batch will
only be partially utilized. The actual overall utilization therefore
depends on the processing batch size b and the raw materials batch
size n. Combined, these two factors lead to a required number of
processing batches r (with r ¼ n/b) and a utilization of
u ¼ r
QrS
100% (1)
where QrS denotes the smallest integer larger than or equal to r. For
situations in which the processing batch size is fixed (as can be
expected in industry), but raw material batch size varies, this leads
to different expected utilizations for each possible processing batch
size b:
ub ¼ 1
jNjX
i˛N
ri
QriS
(2)
where ri is the required number of batches for raw material batch
size ni, i ˛ N an index representing the different possible raw
material batch sizes used in the simulation (based on the uniform
distribution discussed in Section 2.2), and rNr the number of
elements in set N. The expected process utilization value ub for the
processing batch sizes simulated in our case study when using PS2
can be seen in Fig. 5.
Fig. 5 shows that, by simulating a large number of possible raw
material batch sizes, and thus a large set N, the expected utilization
ub varies significantly. When producing with PS1, the raw material
batches are mixed, always processing with fully occupied batch
processing equipment, thus reaching high utilization in the
production processes. Therefore, the number of processing batches
for PS1, representing production efficiency, has a decreasing trend
for an increasing processing batch size. As the decrease in the
number of processing batches for PS2 is not constant, the
percentage difference between the two production strategies is not
increasing at a constant rate in Fig. 4. This is most visible at processing
batch sizes around 4,400 kg, but smaller incontinuities can
be noticed around 2,800 kg and 3,600 kg. A chocolate manufacturer
that produces with a PS2 production strategy should therefore take
the expected utilization value ub as calculated in (2) and illustrated in Fig. 5 into account when deciding on the size of the processing
batches.
Being translated, please wait..
TSþþ meluas TSþ dengan penambahan bahwa petani kakao,
jika menyimpan biji kakao, sudah menandai semua tas dengan unik
kode dan tanggal. Dalam hal ini cokelat selesai dapat dilacak hingga
petani kakao individu. Atau, stasiun pembelian lokal
bisa menandai tas pada kedatangan, dengan informasi dari petani
memberikan kacang.
Pada Gambar. 3 sistem traceability yang berbeda dapat dilihat.
3.3.3. Produk yang berbeda kenang
Model simulasi dapat mensimulasikan dua makanan mungkin
krisis dan penarikan yang sesuai (R1 dan R2) yang dapat terjadi dalam
rantai kasus pasokan studi. R1 mensimulasikan penarikan kembali produk dalam kasus
kontaminasi dari biji kakao, yang bisa menjadi bahan kimia
kontaminasi sementara pertanian, fermentasi atau pengeringan. Dalam hal ini semua
bar cokelat diproduksi dengan biji kakao dari kakao tertentu
petani perlu diingat. R2 mensimulasikan penarikan kembali produk dalam kasus
dari kontaminasi dari batch processing, yang dapat disebabkan oleh
masalah dalam proses pemanggangan. Dalam hal ini semua bar cokelat
diproduksi dalam proses pemanggangan tertentu harus ingat. The
model simulasi memungkinkan untuk menjalankan simulasi tunggal dan ganda.
Karena pentingnya proses pemanggangan itu juga memungkinkan untuk
menjalankan simulasi satu atau beberapa otomatis untuk pengolahan yang berbeda
ukuran batch. Untuk tulisan ini, kita simulasi makanan takut untuk
berbagai pengolahan ukuran batch antara 1.600 kg dan 5.000 kg
(setiap kelipatan 200 kg). Setiap ukuran kemudian menjalankan beberapa
kali, sementara informasi seperti jumlah deret, batch processing
ukuran, ukuran recall dan jumlah pengolahan batch (yang mencerminkan
efisiensi produksi) terdaftar, dan hasil rata-rata dapat
ditentukan.
4. Hasil dan diskusi
4.1. Membandingkan strategi produksi yang berbeda
Untuk membandingkan strategi produksi berdasarkan produksi
efisiensi (PS1) untuk strategi produksi berdasarkan berkurang
dispersi bets (PS2) model simulasi telah dijalankan untuk
ukuran batch processing yang berbeda. Hasil rata-rata ditunjukkan pada
Gambar. 4 sebagai perbedaan persentase antara dua produksi
strategi (nilai PS1 mewakili 100%) di (i) produksi
efisiensi, (ii) ukuran ingat dalam kasus kontaminasi baku
bahan (R1) dan (iii) ukuran recall di kasus kontaminasi
ukuran batch processing (R2).
PS2 adalah strategi produksi di mana batch bahan baku
tidak tercampur. Dengan demikian, yang terakhir batch processing dari bahan baku
bets tidak mungkin sepenuhnya menempati peralatan pengolahan batch. Itu
adalah, peralatan produksi yang memproses batch yang lebih kecil ini akan
hanya menjadi sebagian dimanfaatkan. Pemanfaatan keseluruhan sebenarnya karena
tergantung pada ukuran batch processing b dan bahan baku bets
ukuran n. Dikombinasikan, kedua faktor ini menyebabkan sejumlah diperlukan
pengolahan batch r (dengan r ¼ n / b) dan pemanfaatan
u ¼ r
QRS
? 100% (1)
di mana QRS menunjukkan bilangan bulat terkecil yang lebih besar dari atau sama dengan r. Untuk
situasi di mana ukuran batch processing adalah tetap (seperti yang dapat
diharapkan dalam industri), tetapi ukuran bahan batch yang baku bervariasi, ini mengarah
ke pemanfaatan diharapkan berbeda untuk setiap kemungkinan batch processing
ukuran b:
ub ¼ 1
jNjX
di
ri
QriS
( 2)
dimana ri jumlah yang diperlukan batch untuk bahan baku bets
ukuran ni, i ° N indeks yang mewakili mungkin berbeda baku
ukuran bahan batch yang digunakan dalam simulasi (berdasarkan seragam
distribusi dibahas dalam Bagian 2.2), dan RNR nomor dari
unsur-unsur di set N. diharapkan nilai pemanfaatan proses ub untuk
ukuran batch processing disimulasikan dalam studi kasus kami ketika menggunakan PS2
dapat dilihat pada Gambar. 5.
Gambar. 5 menunjukkan bahwa, dengan mensimulasikan sejumlah besar kemungkinan baku
ukuran batch bahan, dan dengan demikian satu set besar N, pemanfaatan diharapkan
ub bervariasi secara signifikan. Ketika memproduksi dengan PS1, bahan baku
batch dicampur, selalu memproses dengan batch yang penuh diduduki
peralatan pengolahan, sehingga mencapai pemanfaatan yang tinggi dalam
proses produksi. Oleh karena itu, jumlah pengolahan batch
untuk PS1, mewakili efisiensi produksi, memiliki kecenderungan menurun
untuk meningkatkan ukuran batch processing. Seperti penurunan
jumlah batch pengolahan untuk PS2 tidak konstan,
persentase perbedaan antara dua strategi produksi tidak
meningkat pada tingkat yang konstan pada Gambar. 4. Hal ini paling terlihat pada pengolahan
ukuran batch sekitar 4.400 kg, tapi incontinuities lebih kecil dapat
diperhatikan sekitar 2.800 kg dan 3.600 kg. Sebuah pabrik coklat
yang menghasilkan dengan strategi produksi PS2 karena itu harus mengambil
nilai pemanfaatan ub yang diharapkan yang dihitung dalam (2) dan diilustrasikan pada Gambar. 5 memperhitungkan ketika memutuskan pada ukuran pengolahan
batch.
jika menyimpan biji kakao, sudah menandai semua tas dengan unik
kode dan tanggal. Dalam hal ini cokelat selesai dapat dilacak hingga
petani kakao individu. Atau, stasiun pembelian lokal
bisa menandai tas pada kedatangan, dengan informasi dari petani
memberikan kacang.
Pada Gambar. 3 sistem traceability yang berbeda dapat dilihat.
3.3.3. Produk yang berbeda kenang
Model simulasi dapat mensimulasikan dua makanan mungkin
krisis dan penarikan yang sesuai (R1 dan R2) yang dapat terjadi dalam
rantai kasus pasokan studi. R1 mensimulasikan penarikan kembali produk dalam kasus
kontaminasi dari biji kakao, yang bisa menjadi bahan kimia
kontaminasi sementara pertanian, fermentasi atau pengeringan. Dalam hal ini semua
bar cokelat diproduksi dengan biji kakao dari kakao tertentu
petani perlu diingat. R2 mensimulasikan penarikan kembali produk dalam kasus
dari kontaminasi dari batch processing, yang dapat disebabkan oleh
masalah dalam proses pemanggangan. Dalam hal ini semua bar cokelat
diproduksi dalam proses pemanggangan tertentu harus ingat. The
model simulasi memungkinkan untuk menjalankan simulasi tunggal dan ganda.
Karena pentingnya proses pemanggangan itu juga memungkinkan untuk
menjalankan simulasi satu atau beberapa otomatis untuk pengolahan yang berbeda
ukuran batch. Untuk tulisan ini, kita simulasi makanan takut untuk
berbagai pengolahan ukuran batch antara 1.600 kg dan 5.000 kg
(setiap kelipatan 200 kg). Setiap ukuran kemudian menjalankan beberapa
kali, sementara informasi seperti jumlah deret, batch processing
ukuran, ukuran recall dan jumlah pengolahan batch (yang mencerminkan
efisiensi produksi) terdaftar, dan hasil rata-rata dapat
ditentukan.
4. Hasil dan diskusi
4.1. Membandingkan strategi produksi yang berbeda
Untuk membandingkan strategi produksi berdasarkan produksi
efisiensi (PS1) untuk strategi produksi berdasarkan berkurang
dispersi bets (PS2) model simulasi telah dijalankan untuk
ukuran batch processing yang berbeda. Hasil rata-rata ditunjukkan pada
Gambar. 4 sebagai perbedaan persentase antara dua produksi
strategi (nilai PS1 mewakili 100%) di (i) produksi
efisiensi, (ii) ukuran ingat dalam kasus kontaminasi baku
bahan (R1) dan (iii) ukuran recall di kasus kontaminasi
ukuran batch processing (R2).
PS2 adalah strategi produksi di mana batch bahan baku
tidak tercampur. Dengan demikian, yang terakhir batch processing dari bahan baku
bets tidak mungkin sepenuhnya menempati peralatan pengolahan batch. Itu
adalah, peralatan produksi yang memproses batch yang lebih kecil ini akan
hanya menjadi sebagian dimanfaatkan. Pemanfaatan keseluruhan sebenarnya karena
tergantung pada ukuran batch processing b dan bahan baku bets
ukuran n. Dikombinasikan, kedua faktor ini menyebabkan sejumlah diperlukan
pengolahan batch r (dengan r ¼ n / b) dan pemanfaatan
u ¼ r
QRS
? 100% (1)
di mana QRS menunjukkan bilangan bulat terkecil yang lebih besar dari atau sama dengan r. Untuk
situasi di mana ukuran batch processing adalah tetap (seperti yang dapat
diharapkan dalam industri), tetapi ukuran bahan batch yang baku bervariasi, ini mengarah
ke pemanfaatan diharapkan berbeda untuk setiap kemungkinan batch processing
ukuran b:
ub ¼ 1
jNjX
di
ri
QriS
( 2)
dimana ri jumlah yang diperlukan batch untuk bahan baku bets
ukuran ni, i ° N indeks yang mewakili mungkin berbeda baku
ukuran bahan batch yang digunakan dalam simulasi (berdasarkan seragam
distribusi dibahas dalam Bagian 2.2), dan RNR nomor dari
unsur-unsur di set N. diharapkan nilai pemanfaatan proses ub untuk
ukuran batch processing disimulasikan dalam studi kasus kami ketika menggunakan PS2
dapat dilihat pada Gambar. 5.
Gambar. 5 menunjukkan bahwa, dengan mensimulasikan sejumlah besar kemungkinan baku
ukuran batch bahan, dan dengan demikian satu set besar N, pemanfaatan diharapkan
ub bervariasi secara signifikan. Ketika memproduksi dengan PS1, bahan baku
batch dicampur, selalu memproses dengan batch yang penuh diduduki
peralatan pengolahan, sehingga mencapai pemanfaatan yang tinggi dalam
proses produksi. Oleh karena itu, jumlah pengolahan batch
untuk PS1, mewakili efisiensi produksi, memiliki kecenderungan menurun
untuk meningkatkan ukuran batch processing. Seperti penurunan
jumlah batch pengolahan untuk PS2 tidak konstan,
persentase perbedaan antara dua strategi produksi tidak
meningkat pada tingkat yang konstan pada Gambar. 4. Hal ini paling terlihat pada pengolahan
ukuran batch sekitar 4.400 kg, tapi incontinuities lebih kecil dapat
diperhatikan sekitar 2.800 kg dan 3.600 kg. Sebuah pabrik coklat
yang menghasilkan dengan strategi produksi PS2 karena itu harus mengambil
nilai pemanfaatan ub yang diharapkan yang dihitung dalam (2) dan diilustrasikan pada Gambar. 5 memperhitungkan ketika memutuskan pada ukuran pengolahan
batch.
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- اَي كشط او تعديل على هذه الشهادة يلغيها
- Efficiency
- Vietnamese will be wrong cause this is b
- 春高出場おめでとう！皆さん頑張れ！いつも応援してるよ！出典: 成徳バレー部応援団
- High Loading Efficiency
- Mission Control Systems, Inc. exists to
- ทำให้อิทธิพลของวัฒนธรรม
- 春高出場おめでとう！皆さん頑張れ！いつも応援してるよ！出典: 成徳バレー部応援団
- You're
- เดี๋ยวจะไม่สบาย
- Het is dus een tafel die erg makkelijk t
- 이용약관(필수)약관 보기
- thôi đừng có nói gì nữa, thích làm gì th
- 삭제공백없이 입력해 주세요.
- ทำให้อิทธิพลของวัฒนธรรมและอำนาจของเศรษฐก
- กรุ๊ปโอ โกหกไม่เก่ง เป็นคนตรงไปตรงมา ไม่
- Guest room display, room service and pre
- Vaak is de beplanting in een klassieke t
- even telling people the size of the piec
- 인증번호
- ทำให้อิทธิพลของวัฒนธรรมและอำนาจของเศรษฐก
- switch
- กรุ๊ปเลือดของคุณyou're blood group
- -The keyword in context index system acc
