Inventory utils

Some additional classes and functions that help building inventory management environments.

OrderPipeline

 OrderPipeline (num_units:int, lead_time_mean:Union[ddopai.utils.Parameter
                ,numpy.ndarray,List,int,float], lead_time_stochasticity:Li
                teral['fixed','gamma','normal_absolute','normal_relative']
                ='fixed', lead_time_variance:Union[ddopai.utils.Parameter,
                numpy.ndarray,List,int,float,NoneType]=None,
                max_lead_time:list[object]|None=None,
                min_lead_time:list[object]|None=1)

Class to handle the order pipeline in the inventory environments. It is used to keep track of the orders that are placed. It can account for fixed and variable lead times.

	Type	Default	Details
num_units	int		number of units (SKUs)
lead_time_mean	Union		mean lead time
lead_time_stochasticity	Literal	fixed	“fixed”, “gamma”, “normal_absolute”, “normal_relative”
lead_time_variance	Union	None	variance of the lead time
max_lead_time	list[object] \| None	None	maximum lead time in case of stochastic lead times
min_lead_time	list[object] \| None	1	minimum lead time in case of stochastic lead times
Returns	None

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OrderPipeline.get_pipeline

 OrderPipeline.get_pipeline ()

Get the current pipeline

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OrderPipeline.reset

 OrderPipeline.reset ()

Reset the pipeline

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OrderPipeline.step

 OrderPipeline.step (orders:numpy.ndarray)

Add orders to the pipeline and return the orders that are arriving

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OrderPipeline.get_orders_arriving

 OrderPipeline.get_orders_arriving ()

Get the orders that are arriving in the current period

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OrderPipeline.draw_lead_times

 OrderPipeline.draw_lead_times ()

Draw lead times for the orders

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OrderPipeline.check_stochasticity

 OrderPipeline.check_stochasticity (max_lead_time)

Check that params for stochastic lead times are set correctly

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OrderPipeline.check_max_min_mean_lt

 OrderPipeline.check_max_min_mean_lt ()

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OrderPipeline.set_param

 OrderPipeline.set_param (name:str,
                          input:Union[ddopai.utils.Parameter,int,float,num
                          py.ndarray,List], shape:tuple=(1,),
                          new:bool=False)

Set a parameter for the environment. It converts scalar values to numpy arrays and ensures that environment parameters are either of the Parameter class of Numpy arrays. If new is set to True, the function will create a new parameter or update an existing one otherwise. If new is set to False, the function will raise an error if the parameter does not exist.

	Type	Default	Details
name	str		name of the parameter (will become the attribute name)
input	Union		input value of the parameter
shape	tuple	(1,)	shape of the parameter
new	bool	False	whether to create a new parameter or update an existing one
Returns	None

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OrderPipeline.shape

 OrderPipeline.shape ()

Get the shape of the pipeline