FoPra Beluga Challenge - Reinforcement Learning v1.0
Deep Reinforcement Learning solution for the Beluga Challenge shipping container optimization problem using PPO and MCTS
rl.env.state.ProductionLine Class Reference

Factory production line with scheduled jigs. More...

Public Member Functions

 __init__ (self, list[int] scheduled_jigs)
 Initialize a production line.
 
 __str__ (self)
 
 copy (self)
 Create a deep copy of this production line.
 

Public Attributes

 scheduled_jigs = scheduled_jigs
 

Detailed Description

Factory production line with scheduled jigs.

Represents a production facility that processes jigs in a specific order.

Constructor & Destructor Documentation

◆ __init__()

rl.env.state.ProductionLine.__init__ ( self,
list[int] scheduled_jigs )

Initialize a production line.

Parameters
scheduled_jigsList of jig IDs scheduled for processing in order
166 def __init__(self, scheduled_jigs: list[int]):
167 """!
168 @brief Initialize a production line
169 @param scheduled_jigs List of jig IDs scheduled for processing in order
170 """
171 self.scheduled_jigs = scheduled_jigs
172

Member Function Documentation

◆ __str__()

rl.env.state.ProductionLine.__str__ ( self)
173 def __str__(self):
174 return "scheduled_jigs = " + str(self.scheduled_jigs)
175

◆ copy()

rl.env.state.ProductionLine.copy ( self)

Create a deep copy of this production line.

Returns
New ProductionLine instance with same properties
176 def copy(self):
177 """!
178 @brief Create a deep copy of this production line
179 @return New ProductionLine instance with same properties
180 """
181 return ProductionLine(self.scheduled_jigs[:])
182
183

Member Data Documentation

◆ scheduled_jigs

rl.env.state.ProductionLine.scheduled_jigs = scheduled_jigs

The documentation for this class was generated from the following file: