Merge branch 'main' into add-ci-testing

ai-for-decision-making-tue · Apr 5, 2024 · affbf08 · affbf08
2 parents b5c57bd + a31349c
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diff --git a/.gitignore b/.gitignore
diff --git a/.idea/.gitignore b/.idea/.gitignore
diff --git a/.idea/Job_Shop_Scheduling_Benchmark_Environments_and_Instances.iml b/.idea/Job_Shop_Scheduling_Benchmark_Environments_and_Instances.iml
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
diff --git a/.idea/misc.xml b/.idea/misc.xml
diff --git a/.idea/modules.xml b/.idea/modules.xml
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
diff --git a/README.md b/README.md
@@ -10,14 +10,16 @@ We aim to include a wide range of solution methods capable of solving machine sc
 
 
 
-| Solution methods | Job Shop | Flow Show | Flexible Job Shop | SDST | Assembly | Online Arrivals |
+| Solution methods | Job Shop (JSP) | Flow Show (FSP) | Flexible Job Shop (FJSP) | FJSP SDST | FAJSP | Online (F)JSP |
 | :---: | :---:| :---: | :---: | :---: | :---: | :---: |
-| Load Balancing Heuristics | ✓ | ✓ | ✓ | ✓ | ✓ | | 
 | Dispatching Rules | ✓ | ✓ | ✓ | | ✓ | ✓* | 
-| Genetic Algorithm | ✓ | ✓ | ✓ | ✓ | ✓ | |  
+| Load Balancing Heuristics | ✓ | ✓ | ✓ | ✓ | ✓ | |
+| Genetic Algorithm | ✓ | ✓ | ✓ | ✓ | ✓ | |
+| MILP | ✓ | ✓ | ✓ | ✓ | | | 
+| CP-SAT | ✓ | ✓ | ✓ | | | |
 | FJSP-DRL | ✓ | ✓ | ✓ | |  | | 
 
-*Capable of online arrivals of FJSP problems
+*Capable of online arrivals of FJSP problems 
 
 🔜 We have a few DRL-based solutions in the pipeline, which will be published here upon completion. 
 
@@ -37,4 +39,3 @@ Robbert Reijnen, Kjell van Straaten, Zaharah Bukhsh, and Yingqian Zhang (2023) [
       primaryClass={cs.AI}
 }
 ```
-🔜 Supporting paper and corresponding reference
diff --git a/configs/FJSP_DRL.toml b/configs/FJSP_DRL.toml
@@ -1,16 +1,16 @@
 [env_parameters]
-num_jobs = 10           # Number of jobs in the environment
-num_mas = 5             # Number of machine agents
-batch_size = 20          # Batch size for training
-ope_feat_dim = 6       # Dimension of operation features
-ma_feat_dim = 3         # Dimension of machine agent features
-valid_batch_size = 5    # Batch size for validation
-show_mode = "print"     # Mode for displaying information (e.g., "print")
+num_jobs = 10               # Number of jobs in the environment
+num_mas = 5                 # Number of machine agents
+batch_size = 20             # Batch size for training
+ope_feat_dim = 6            # Dimension of operation features
+ma_feat_dim = 3             # Dimension of machine agent features
+valid_batch_size = 5        # Batch size for validation
+device = "cpu"              # Device for training ("cpu" or "cuda")
 
 [model_parameters]
 in_size_ma = 3              # Input size for machine agent
 out_size_ma = 8             # Output size for machine agent
-in_size_ope = 6           # Input size for operation
+in_size_ope = 6             # Input size for operation
 out_size_ope = 8            # Output size for operation
 hidden_size_ope = 128       # Hidden size for operation model
 num_heads = [1, 1]          # Number of attention heads
@@ -20,6 +20,7 @@ n_latent_critic = 64        # Number of latent units for critic
 n_hidden_actor = 3          # Number of hidden layers for actor
 n_hidden_critic = 3         # Number of hidden layers for critic
 action_dim = 1              # Dimension of action space
+device = "cpu"              # Device for training ("cpu" or "cuda")
 
 [train_parameters]
 lr = 0.0002                 # Learning rate
@@ -33,18 +34,20 @@ entropy_coeff = 0.01        # Coefficient for entropy loss
 max_iterations = 1000       # Maximum number of iterations
 save_timestep = 20          # Timestep for saving model checkpoints
 update_timestep = 1         # Timestep for model updates
-viz = true                 # Visualize the training process --> when True: first launch local server with: python -m visdom.server
-viz_name = 'training_visualisation' # Name of visualization env
 minibatch_size = 512        # Mini-batch size for training
-parallel_iter = 3          # Number of parallel iterations
-device = "cpu"              # Device for training ("cpu" or "cuda")
+parallel_iter = 20          # Number of parallel iterations
+viz = false                 # Visualize the training process --> when true: first launch local server with: python -m visdom.server (and set to: online)
+viz_name = 'training_visualisation' # Name of visualization env
 validation_folder = "/fjsp/song/dev/1005/"  # Folder for validation data
+device = "cpu"              # Device for training ("cpu" or "cuda")
 
 
 # Configuration for test parameters
 [test_parameters]
-problem_instance = "/fjsp/1_Brandimarte/Mk02.fjs"               # Problem instance for testing
-trained_policy = "./solutions/FJSP_DRL/models/song_10_5.pt"          # Load pretrained policy
-sample = false                                                  # Sampling flag for testing
-num_sample = 5                                                  # Number of samples for testing (nr )
-device = "cpu"                                                  # Device for testing ("cpu" or "cuda")
+seed = 1
+problem_instance = "/fjsp/1_Brandimarte/Mk02.fjs"                       # Problem instance for testing
+trained_policy = "/solution_methods/FJSP_DRL/save/train_20240314_192906/song_10_5.pt"   # Load pretrained policy
+sample = false                                                          # Sampling flag for testing
+num_sample = 1                                                          # Number of samples for testing (nr )
+plotting = true                                                         # plot instance representation and ganttchart
+device = "cpu"                                                          # Device for testing ("cpu" or "cuda")
diff --git a/configs/milp.toml b/configs/milp.toml
@@ -1,5 +1,5 @@
 [instance]
-problem_instance = "/fjsp/2_Hurink/edata/abz5.fjs" # configure problem instance to be scheduled
+problem_instance = "/jsp/adams/abz5" # configure problem instance to be scheduled
 
 [solver]
-time_limit = 3600 # time limit for the gurobi solver, in seconds
+time_limit = 64800 # time limit for the gurobi solver, in seconds
diff --git a/configs/or_tools.toml b/configs/or_tools.toml
@@ -0,0 +1,5 @@
+[instance]
+problem_instance = "/fjsp/2_Hurink/edata/abz5.fjs" # configure problem instance to be scheduled
+
+[solver]
+time_limit = 3600 # time limit for the OR-tools CP-SAT solver, in seconds
diff --git a/data_parsers/parser_fajsp.py b/data_parsers/parser_fajsp.py
@@ -1,8 +1,8 @@
-from pathlib import Path
 import re
+from pathlib import Path
 
-from scheduling_environment.machine import Machine
 from scheduling_environment.job import Job
+from scheduling_environment.machine import Machine
 from scheduling_environment.operation import Operation
 
 

diff --git a/data_parsers/parser_fjsp.py b/data_parsers/parser_fjsp.py
@@ -1,8 +1,8 @@
-from pathlib import Path
 import re
+from pathlib import Path
 
-from scheduling_environment.machine import Machine
 from scheduling_environment.job import Job
+from scheduling_environment.machine import Machine
 from scheduling_environment.operation import Operation
 
 

diff --git a/data_parsers/parser_fjsp_sdst.py b/data_parsers/parser_fjsp_sdst.py
@@ -1,8 +1,8 @@
 from pathlib import Path
 
-from scheduling_environment.operation import Operation
-from scheduling_environment.machine import Machine
 from scheduling_environment.job import Job
+from scheduling_environment.machine import Machine
+from scheduling_environment.operation import Operation
 
 
 def parse(JobShop, instance, from_absolute_path=False):