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Is there a better way to make assignments for discrete placement?
The Next CEO of Stack OverflowIs there a way to run Python on Android?What's the canonical way to check for type in Python?Nicest way to pad zeroes to a stringHow can I make a time delay in Python?Is there a way to substring a string?How to make a chain of function decorators?How to make a flat list out of list of lists?Proper way to declare custom exceptions in modern Python?Way to create multiline comments in Python?Most elegant way to check if the string is empty in Python?
I'm trying to write a program to pair up some barges with some boats. Each barge has a certain volume. Each boat can hold up to 4 barges and push up to a certain total volume (varies by boat). The end goal is to use as few boats as possible while placing every barge.
I have tried using scipy.optimize.differential_evolution with various strategies and parameters and initial guesses. I just turn the numbers it spits out into integers so that they can be used to link to specific barges. After taking the args from the optimizer, the objective function does some work (not shown) and then sees if the conditions are satisfied and returns a value based on that. Here is some code that generally shows what I'm doing in the fobj (I don't include the full code because it is hard to understand without the structure of the classes I have set up etc):
bargeSpecsMet = True
boatSpecsMet = True
dumbBarge = 0
dumbBoat = 0
#sums up how many barges have either no assignment or more than 1 assignement
#each barge can only be in one place
for each in barges:
dumbBarge += abs(barge.numAssignments - 1)
if dumbBarge > 0:
bargeSpecsMet = False
#checks each boat to see if it over capacity
for each in boats:
if boats.totalBBLs > boats.maxBBLs:
boatSpecsMet = False
dumbBoat += 1
#returns a very large number based on how many barges aren't correct first
#does this until the barges have exactly 1 assignment each
if bargeSpecsMet == False:
return dumbBarge * 1e20
#returns a still large but smaller number based on how many boats are overcapacity
#does this until each barge is under capacity
if boatSpecsMet == False:
return dumbBoat * 1E10
#there will be more code for minimizing number of boats but i haven't gotten there yet
I used differential_evolution mainly because I'm familiar with it from using it on another project that minimized a more traditional math function. I don't think it's probably meant to be used for discrete placement of things but it sorta works. Unfortunately it doesn't want to stray far from my initial guesses. Does anyone have any tips for this? Is anyone familiar with another solver or method that would be better suited? I'm relatively new to python and also this kind of problem so I just don't know what I don't know is out there...
The only other method I've come up with so far is to brute force it by generating a combination of assignments, see if that works, if not rinse and repeat. I haven implemented it yet because I know that will take forever to find a solution.
python python-3.x scipy
add a comment |
I'm trying to write a program to pair up some barges with some boats. Each barge has a certain volume. Each boat can hold up to 4 barges and push up to a certain total volume (varies by boat). The end goal is to use as few boats as possible while placing every barge.
I have tried using scipy.optimize.differential_evolution with various strategies and parameters and initial guesses. I just turn the numbers it spits out into integers so that they can be used to link to specific barges. After taking the args from the optimizer, the objective function does some work (not shown) and then sees if the conditions are satisfied and returns a value based on that. Here is some code that generally shows what I'm doing in the fobj (I don't include the full code because it is hard to understand without the structure of the classes I have set up etc):
bargeSpecsMet = True
boatSpecsMet = True
dumbBarge = 0
dumbBoat = 0
#sums up how many barges have either no assignment or more than 1 assignement
#each barge can only be in one place
for each in barges:
dumbBarge += abs(barge.numAssignments - 1)
if dumbBarge > 0:
bargeSpecsMet = False
#checks each boat to see if it over capacity
for each in boats:
if boats.totalBBLs > boats.maxBBLs:
boatSpecsMet = False
dumbBoat += 1
#returns a very large number based on how many barges aren't correct first
#does this until the barges have exactly 1 assignment each
if bargeSpecsMet == False:
return dumbBarge * 1e20
#returns a still large but smaller number based on how many boats are overcapacity
#does this until each barge is under capacity
if boatSpecsMet == False:
return dumbBoat * 1E10
#there will be more code for minimizing number of boats but i haven't gotten there yet
I used differential_evolution mainly because I'm familiar with it from using it on another project that minimized a more traditional math function. I don't think it's probably meant to be used for discrete placement of things but it sorta works. Unfortunately it doesn't want to stray far from my initial guesses. Does anyone have any tips for this? Is anyone familiar with another solver or method that would be better suited? I'm relatively new to python and also this kind of problem so I just don't know what I don't know is out there...
The only other method I've come up with so far is to brute force it by generating a combination of assignments, see if that works, if not rinse and repeat. I haven implemented it yet because I know that will take forever to find a solution.
python python-3.x scipy
add a comment |
I'm trying to write a program to pair up some barges with some boats. Each barge has a certain volume. Each boat can hold up to 4 barges and push up to a certain total volume (varies by boat). The end goal is to use as few boats as possible while placing every barge.
I have tried using scipy.optimize.differential_evolution with various strategies and parameters and initial guesses. I just turn the numbers it spits out into integers so that they can be used to link to specific barges. After taking the args from the optimizer, the objective function does some work (not shown) and then sees if the conditions are satisfied and returns a value based on that. Here is some code that generally shows what I'm doing in the fobj (I don't include the full code because it is hard to understand without the structure of the classes I have set up etc):
bargeSpecsMet = True
boatSpecsMet = True
dumbBarge = 0
dumbBoat = 0
#sums up how many barges have either no assignment or more than 1 assignement
#each barge can only be in one place
for each in barges:
dumbBarge += abs(barge.numAssignments - 1)
if dumbBarge > 0:
bargeSpecsMet = False
#checks each boat to see if it over capacity
for each in boats:
if boats.totalBBLs > boats.maxBBLs:
boatSpecsMet = False
dumbBoat += 1
#returns a very large number based on how many barges aren't correct first
#does this until the barges have exactly 1 assignment each
if bargeSpecsMet == False:
return dumbBarge * 1e20
#returns a still large but smaller number based on how many boats are overcapacity
#does this until each barge is under capacity
if boatSpecsMet == False:
return dumbBoat * 1E10
#there will be more code for minimizing number of boats but i haven't gotten there yet
I used differential_evolution mainly because I'm familiar with it from using it on another project that minimized a more traditional math function. I don't think it's probably meant to be used for discrete placement of things but it sorta works. Unfortunately it doesn't want to stray far from my initial guesses. Does anyone have any tips for this? Is anyone familiar with another solver or method that would be better suited? I'm relatively new to python and also this kind of problem so I just don't know what I don't know is out there...
The only other method I've come up with so far is to brute force it by generating a combination of assignments, see if that works, if not rinse and repeat. I haven implemented it yet because I know that will take forever to find a solution.
python python-3.x scipy
I'm trying to write a program to pair up some barges with some boats. Each barge has a certain volume. Each boat can hold up to 4 barges and push up to a certain total volume (varies by boat). The end goal is to use as few boats as possible while placing every barge.
I have tried using scipy.optimize.differential_evolution with various strategies and parameters and initial guesses. I just turn the numbers it spits out into integers so that they can be used to link to specific barges. After taking the args from the optimizer, the objective function does some work (not shown) and then sees if the conditions are satisfied and returns a value based on that. Here is some code that generally shows what I'm doing in the fobj (I don't include the full code because it is hard to understand without the structure of the classes I have set up etc):
bargeSpecsMet = True
boatSpecsMet = True
dumbBarge = 0
dumbBoat = 0
#sums up how many barges have either no assignment or more than 1 assignement
#each barge can only be in one place
for each in barges:
dumbBarge += abs(barge.numAssignments - 1)
if dumbBarge > 0:
bargeSpecsMet = False
#checks each boat to see if it over capacity
for each in boats:
if boats.totalBBLs > boats.maxBBLs:
boatSpecsMet = False
dumbBoat += 1
#returns a very large number based on how many barges aren't correct first
#does this until the barges have exactly 1 assignment each
if bargeSpecsMet == False:
return dumbBarge * 1e20
#returns a still large but smaller number based on how many boats are overcapacity
#does this until each barge is under capacity
if boatSpecsMet == False:
return dumbBoat * 1E10
#there will be more code for minimizing number of boats but i haven't gotten there yet
I used differential_evolution mainly because I'm familiar with it from using it on another project that minimized a more traditional math function. I don't think it's probably meant to be used for discrete placement of things but it sorta works. Unfortunately it doesn't want to stray far from my initial guesses. Does anyone have any tips for this? Is anyone familiar with another solver or method that would be better suited? I'm relatively new to python and also this kind of problem so I just don't know what I don't know is out there...
The only other method I've come up with so far is to brute force it by generating a combination of assignments, see if that works, if not rinse and repeat. I haven implemented it yet because I know that will take forever to find a solution.
python python-3.x scipy
python python-3.x scipy
asked Mar 8 at 17:33
Ryan BoncheffRyan Boncheff
11
11
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