I am working on a use case, in which I have multiple vehicles as depot,delivery boys and set of customers from diverse location to serve fresh food. Customers would place an order from an application,then delivery boy receives the order and get the food from Van and then deliver it with some promised delivery time(15 mins). I want to optimize this problem so that operational cost for traveling is reduced and delivery time is minimized. Just wanted to know is there any implementation in Python so solve VRPTW problem ? Please help
You can find implementation of Dijkstra shortest path algorithm in python.
An example implementation is
http://code.activestate.com/recipes/577506-dijkstra-shortest-path-algorithm/
read some research papers on vehicle routing problem. i've seen some of the papers provides a complete algorithm on vehicle routing, and they come in different ways by considering multiple criteria. hence, it's possible to implement one or more of the algorithms provided in these papers and do a test to use the optimal solution.
If you want to solve a routing problem, the very first thing to figure out is what variant of the vehicle routing problem you're solving. I'm going to assume the vans are stationary (i.e. you're not trying to optimise the positioning of the vans themselves as well). Firstly the problem is dynamic as it's happening in realtime - i.e. it's a realtime route optimisation problem. If the delivery people are pre-assigned to a single van, then this might be considered a dynamic multi-trip vehicle routing problem (with time windows obviously). Generally speaking though it's a dynamic pickup and delivery vehicle routing problem, as presumably the delivery people can pickup from different vans (so DPDVRPTW). You'd almost certainly need soft timewindows as well, making it a
DPDVRP with soft time windows. Soft time windows are essential because in a realtime setting you generally want to deliver as fast as possible, and so want to minimise how late you are. Normal 'hard' time windows like in the VRPTW don't let you deliver after a certain time, but place no cost penalty on delivering before this time (i.e. they're binary). Therefore you can't use them to minimise lateness.
I'm afraid I don't know of any open source solver in python or any other language that solves the dynamic pickup and delivery vehicle routing problem with soft time windows.
This survey article has a good overview of the subject. We also published a white paper on developing realtime route optimisers, which is probably an easier read than the academic paper. (Disclaimer - I am the author of this white paper).
Related
How can I create such a model that automatically allocates available labors into each department according to the current workload each of them respectively.
Lets suppose in a retail store there are lot of people for billing to automatically model must allocate more labors into cashier and after sometime when the traffic become low some of the cashier must be allocated to any other department like picking or arranging things and so on with the available labors on the same day . So can we do it using Linear Programming if yes then can you please provide me the suggestion else can you please let me know what kind of problem is it and any reference towards it will be appreciated.
The goal is achievable. You will want to use pulp for this https://coin-or.github.io/pulp/
Its a fairly standard application of LP and there are a number of resources and tutorials available from the above link which should guide you to a solution but you will need to adapt to suit your particular needs.
I'm currently running a project to optimize media mixed marketing channel spend. I followed this guide and am using the code here: https://towardsdatascience.com/an-upgraded-marketing-mix-modeling-in-python-5ebb3bddc1b6.
Towards the end of the article, it states:
"We have talked about optimizing spendings and how this was not possible with the old model. Well, with the new one it is, but I will not go into detail here. In short, treat our tuned_model a function and optimize it with a program of your choice, for example, Optuna or scipy.optimize.minimize. Add some budget constraints to the optimization, such as that the sum of spendings should be less than 1,000,000 in total."
I'm not sure how I can do this.
The goal here is to optimize spend (capped at a certain amount) to hit maximum profit.
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I have a list of text like this- and I want to count the number of lines in this. I thought of splitting it by ". ", but it will create extra lines for words like "a.m.". Please help!
[
'when people hear ai they often think about sentient robots and magic boxes. ai today is much more mundane and simple—but that doesn’t mean it’s not powerful. another misconception is that high-profile research projects can be applied directly to any business situation. ai done right can create an extreme return on investments (rois)—for instance through automation or precise prediction. but it does take thought, time, and proper implementation. we have seen that success and value generated by ai projects are increased when there is a grounded understanding and expectation of what the technology can deliver from the c-suite down.',
'“artificial intelligence (ai) is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems and bodies to sense, learn, reason and take action.”3 lately there has been a big rise in the day-to-day use of machines powered by ai. these machines are wired using cross-disciplinary approaches based on mathematics, computer science, statistics, psychology, and more.4 virtual assistants are becoming more common, most of the web shops predict your purchases, many companies make use of chatbots in their customer service and many companies use algorithms to detect fraud.',
'ai and deep learning technology employed in office entry systems will bring proper time tracking of each employee. as this system tries to learn each person with an image processing technology whose data is feed forwarded to a deep learning model where deep learning isn’t an algorithm per se, but rather a family of algorithms that implements deep networks (many layers). these networks are so deep that new methods of computation, such as graphics processing units (gpus), are required to train them, in addition to clusters of compute nodes. so using deep learning we can take detect the employee using face and person recognition scan and through which login, logout timing is recorded. using an ai system we can even identify each employee’s entry time, their working hours, non-working hours by tracking the movement of an employee in the office so that system can predict and report hr for the salary for each employee based on their working hours. our system can take feed from cctv to track movements of employees and this system is capable of recognizing a person even he/she is being masked as in this pandemic situation by taking their iris scan. with this system installed inside the office, the following are some of the benefits:',
'for several countries, regulations insist that the employer must keep documents available that can demonstrate the working hours performed by each employee. in the event of control from the labor inspectorate or a dispute with an employee, the employer must be able to explain and justify the working hours for the company. this can be made easy as our system is tracking employee movements',
'this is about monitoring user connection times to detect suspicious access times. in the event where compromised credentials are used to log on at 3 a.m. on a saturday, a notification on this access could alert the it team that an attack is possibly underway.',
'to manage and react to employees’ attendance, overtime thresholds, productivity, and suspicious access times, our system records and stores detailed and interactive reporting on users’ connection times. these records allow you to better manage users’ connection times and provide accurate, detailed data required by management.',
'4)if you want to avoid paying overtime, make sure that your employees respect certain working time quotas or even avoid suspicious access. our system will alert the hr officer about each employee’s office in and out time so that they can accordingly take action.',
'5)last but not least it reduces human resource needs to keep track of the records and sending the report to hr and hr officials has to check through the report so this system will reduce times and human resource needs',
'with the use of ai and deep learning technologies, we can automate some routines stuff with more functionality which humans need more resources to keep track thereby reducing time spent on manual data entry works rather companies can think of making their position high in the competitive world.'
]
Sentence splitting is not a trivial task. I'd suggest you use a ready-made library like the NLTK.
import nltk
text = "..." # your raw text
sentences = nltk.sent_tokenize(text)
This works fairly well, but don't expect perfect results.
I have a list of text like this- and I want to count no. of lines in this. I thought of splitting it by". "
Nice idea, but if you want to count the number of lines, why don't you simply take the lenght of that list?
lenght = len(that_long_text_list)
And then you can sum this lenght to the ., removing by regex expressions like a.m.. To do this check this question about regex pattern matching for acronyms.
Since we don't know how many characters fit into a line as you define it, we can't know for sure. My suggestion would be as follows:
Split the text into paragraphs, since the start of a new paragraph will, in any case, introduce a line break. This is easy - each item in your list will be a paragraph.
For each of these paragraphs, divide it by the number of characters in a line and round up to the nearest full integer.
So my code would be as follows:
raw_text = # Your raw text in the form of a list
line_count = 0
chars_per_line = # Whatever the number of characters in a line of yours is
for paragraph in raw_text:
line_count += (paragraph // chars_per_line) + 1
Not perfect because it doesn't take the rules of syllabification into account, but close enough, I guess.
I am working on a VRP project using Google OR-Tools with Python.
Currently, I have a tight time windows constraints, high demands, and the capacity of the vehicles.
When I run the solver, the solver always chooses to deploy the vehicle that has the biggest capacity.
Can I make the solver deploy the smaller vehicle even though it will deploy more vehicles? Because in reality deploying bigger vehicles will cost higher.
And is there any function inside that can allow re-departure of vehicle?
Thank you!
1) You can set a fixed cost for each vehicle.
ref: https://github.com/google/or-tools/blob/5ff76b487a6c2006326765d6417964599eedc8c9/ortools/constraint_solver/routing.h#L844-L848
2) to "redeploy" you can duplicate the depot and use "reload".
see: https://github.com/google/or-tools/blob/master/ortools/constraint_solver/samples/cvrp_reload.py
I found another strategy that we can do in the or-tools.
In my case, the or-tools will deploy the big vehicle first as the first solution and there is a minimum number of the biggest vehicle for some 'tight' problems (tight time windows or really high weight demands). So what if, we do not have enough resources which are the big vehicles?
As long as the resources still have a lot of smaller vehicles, I can use 'dummy vehicles' to reach the first solution. The cost for these 'dummy vehicle' should be really high compared to the real vehicle, I set it like 1000 times higher.
After the solver reaches the first solution, you can give it a time to improve the solution. After a while, the solver will deploy the real smaller vehicle and will not use the 'dummy vehicle' anymore.
I am searching for a python package that I can use to simulate molecular dynamics in non-equilibrium situations. I need a setup that can handle a fairly large number of molecules in a primarily kinetic theory manner, and that can handle having solid surfaces present. With regards to the surfaces, I would need to be able to create arbitrary shapes and monitor pressure and other variables resulting from the molecular action. Alternatively, I could add the surface parts myself if I had molecules that could handle it.
Does anyone know of any packages that might be suitable?
Have you considered SimPy? SimPy is a rather generic Discrete Event Simulation package, but could feasibly meet your needs.
Better yet the Molecular Modelling ToolKit (MMTK) seems more specialized...
I have used neither, but this sounds like fun. Python, as a language, seems to be in privileged position for use in simulation software, whereby people can script the specific details of their model while relying on the framework for all the common logic, such as scheduling, visualization, monitoring etc. The unknown is how well such toolkits scale when fed with agent counts commensurate with biology models (BTW, how "big" is that?)
Lampps and gromacs are two well known molecular dynamics codes. These codes both have some python based wrapper stuff, but I am not sure how much functionality the wrappers expose. They may not give you enough control over the simulation.
Google for "GromacsWrapper" or google for "lammps" and "pizza.py"
Digital material and ASE are two molecular dynamics codes that expose a lot of functionality, but last time I looked, they were both fairly specialized. They may not allow you to use the force potentials that you want
Google for "digital material" and "cornell" or google for "ase" and dtu
Note to MJV: Normal MD-codes take one time step at a time, and they move all particles in each time step. Most of the time is spend calculating the total force on each atom. This involves iterating over a list of pairs of neighboring atoms. I think the best idea is to do the force calculation and a few more basics in c++ or fortran and then wrap that functionality in python. (But it could be fun to see how far one can get by using numpy matrices)
The following programs can be used to run MD symulations:
Gromacs
AMBER
charmm
OpenMM
many others...
The following Python packages are useful for preparing and analysing MD trajectories:
MDtraj and the OMNIA ecosystem
MDAnalysis
ProDy
MMTK
Another generic simulations framework is my own GarlicSim. You can try that. I could help you get a simpack up if you're serious about it.
I don't know if that programs does all the features you need but there is avogadro in the kde programs, i think it is extendable and since it is open source you could do anything with it. http://www.kde-apps.org/content/show.php/Avogadro?content=59521
It is really advanced and programmed by a friend of mine
I second MMTK, but take a look at VMD, which is the best MD software I'm aware of, and is Python-scriptable (in addition to Tk). See this for examples and tutorials.
I recommend to use molecular dynamics software to run MD simulations like Gromacs. This software is highly optimized for that particular purpose. You can also run on GPU's and you will be able to run larger systems in less time.
Afterwards, you run only the analysis with python packages using the generated trajectories.
mdtraj
pmx