Python Tutorial : Basics of PuLP modeling

Key Takeaways

in this lesson we discussed IP and LP modeling in pole this course will focus on using the Python pulp library it is a framework for linear and integer programming problems the libraries maintained by the coin or our foundation pulp models the problems in Python but relies on a solver to compute a solution it works with many different solvers let's jump right into example that focuses on resource scheduling imagine that you are consulted for a cake bakery that sells only two types of cakes you are attempting the schedule of resources of the bakery for the next 30 days there is an oven to Baker's and a person who packages the cakes in this case we assume the person packaging will only work 22 in the next 30 days due to vacation the amount of time needed for each resource is different for each type of cake additionally the profit for the cakes are different we want to know how many of each type of cake we should make to maximize a profit remember that our profits are subject to the different constraints first the number of cakes produce must be greater than 0 the number of cakes of each type produce multiplied by the time needed on the oven gives a total number of days and this cannot exceed 30 days a similar situation exists for the Baker's however because there are two Baker's the total number of days should not exceed 60 days finally the worker packaging is only available 22 days this month to solve our example we will model it in Pulp a common modeling process involves initializing a model defining the decision variables defining the objective function defining the model constraints and finally we solve it these steps should feel familiar from the lesson on LP and IP modeling initializing the model is the first step in the modeling process and for that you will use the LP problem function it has two inputs the first is a text input for the type of problem you are modeling the second input tells if the model should look to maximize or minimize the objective function for example when modeling delivery times you will likely choose to minimize after importing the package we initialize a model with LP problem in our Python script and choose to maximize next we look at defining the decision variables for this you will use the LP variable class this class has five inputs the first is the name of the variable the next to set the lower and upper bounds of the variable their default value is none which sets the bounds to negative infinity for lower amount or positive infinity for the upper bound the cat input categorizes the variable as either an integer binary or continuous the last input is related to column based modeling which is outside the scope of this course in our example the variables are how many a and B cakes are produced we only set the lower bounds and force them to be an integer variable next we define the objective function using our variables then we define the constraints Pope is able to identify which equations are constraints because of the inequalities finally solve them all the optimized values are stored in var value here is a full script in this lesson we discussed that pulp is an LP and IP model II framework we review the five common steps in a pulp modeling process finally we worked through a resource scheduling example all right let's