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Estimating box size from the contents
2019 Community Moderator ElectionChoosing best methods for estimating the unknown parameters in a linear regression modelEstimating destination according to previous dataEstimating probability using boltzmann machineCan the size of a pooling layer be learned?Estimating expected revenue generationEstimating data set size for grammar extractionClassifier training long time due to the size dataEntropy in a closed boxTraining detector without bounding box dataSize of Output vector from AvgW2V Vectorizer is less than Size of Input data
$begingroup$
I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.
Here's an example of the input data:
box # | contents | box size
----- | --------------------------------- | ---------
1 | a widget, a doodad, and a trinket | 20x12x8
2 | 3 widgets | 12x12x12
However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:
rank | object count | object type
---- | ------------ | -----------
1 | 500,000 | doodad
2 | 350,000 | trinket
3 | 300,000 | widget
--- | snip | ---
50 | 6,000 | whatyoumacallits
--- | snip | ---
300 | 5 | quarts of blinker fluid
Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).
It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.
machine-learning
asked Mar 8 at 16:14
SlotharioSlothario
101
$endgroup$
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.
Here's an example of the input data:
box # | contents | box size
----- | --------------------------------- | ---------
1 | a widget, a doodad, and a trinket | 20x12x8
2 | 3 widgets | 12x12x12
However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:
rank | object count | object type
---- | ------------ | -----------
1 | 500,000 | doodad
2 | 350,000 | trinket
3 | 300,000 | widget
--- | snip | ---
50 | 6,000 | whatyoumacallits
--- | snip | ---
300 | 5 | quarts of blinker fluid
Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).
It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.
machine-learning
asked Mar 8 at 16:14
SlotharioSlothario
101
$endgroup$
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.
Here's an example of the input data:
box # | contents | box size
----- | --------------------------------- | ---------
1 | a widget, a doodad, and a trinket | 20x12x8
2 | 3 widgets | 12x12x12
However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:
rank | object count | object type
---- | ------------ | -----------
1 | 500,000 | doodad
2 | 350,000 | trinket
3 | 300,000 | widget
--- | snip | ---
50 | 6,000 | whatyoumacallits
--- | snip | ---
300 | 5 | quarts of blinker fluid
Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).
It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.
machine-learning
asked Mar 8 at 16:14
SlotharioSlothario
101
$endgroup$
I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.
Here's an example of the input data:
box # | contents | box size
----- | --------------------------------- | ---------
1 | a widget, a doodad, and a trinket | 20x12x8
2 | 3 widgets | 12x12x12
However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:
rank | object count | object type
---- | ------------ | -----------
1 | 500,000 | doodad
2 | 350,000 | trinket
3 | 300,000 | widget
--- | snip | ---
50 | 6,000 | whatyoumacallits
--- | snip | ---
300 | 5 | quarts of blinker fluid
Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).
It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.
machine-learning
machine-learning
asked Mar 8 at 16:14
SlotharioSlothario
101
asked Mar 8 at 16:14
SlotharioSlothario
101
asked Mar 8 at 16:14
SlotharioSlothario
101
asked Mar 8 at 16:14
SlotharioSlothario
101
asked Mar 8 at 16:14
SlotharioSlothario
101
101
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
This is known as "3D Bin Packing Problem" in literature.
https://en.wikipedia.org/wiki/Bin_packing_problem
Since this is NP-Hard; Some of the approaches are :
- Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem
- Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930
- Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
$endgroup$
1
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
add a comment |
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1 Answer
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1 Answer
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active
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votes
$begingroup$
This is known as "3D Bin Packing Problem" in literature.
https://en.wikipedia.org/wiki/Bin_packing_problem
Since this is NP-Hard; Some of the approaches are :
- Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem
- Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930
- Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
$endgroup$
1
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
add a comment |
$begingroup$
This is known as "3D Bin Packing Problem" in literature.
https://en.wikipedia.org/wiki/Bin_packing_problem
Since this is NP-Hard; Some of the approaches are :
- Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem
- Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930
- Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
$endgroup$
1
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
add a comment |
$begingroup$
This is known as "3D Bin Packing Problem" in literature.
https://en.wikipedia.org/wiki/Bin_packing_problem
Since this is NP-Hard; Some of the approaches are :
- Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem
- Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930
- Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
$endgroup$
This is known as "3D Bin Packing Problem" in literature.
https://en.wikipedia.org/wiki/Bin_packing_problem
Since this is NP-Hard; Some of the approaches are :
- Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem
- Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930
- Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
answered Mar 8 at 18:43
Shamit VermaShamit Verma
1,4291214
1,4291214
1
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
add a comment |
1
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
1
1
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
$endgroup$
– Slothario
Mar 8 at 19:09
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
$begingroup$
Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
$endgroup$
– Slothario
Mar 8 at 19:12
add a comment |
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