New project architecture for scaling up

Author: AnasNeumann

data/results/Material use policy loss.png

@@ -1,5 +1,5 @@
 import argparse
-from common import init_several_1D, init_2D, init_several_2D, init_3D, load_instance, directory
+from tools.common import init_several_1D, init_2D, init_several_2D, init_3D, load_instance, directory
 from model.instance import Instance
 from model.solution import Solution
 import pandas as pd

data/results/Outsoucing policy loss.png

@@ -1,7 +1,7 @@
 import pickle
 import os
 from model.instance import Instance
-from common import directory
+from tools.common import directory
 import torch
 torch.autograd.set_detect_anomaly(True)
 import random

data/results/Scheduling policy loss.png

@@ -2,15 +2,15 @@
 from model.instance import Instance
 from model.graph import GraphInstance, NO, NOT_YET, YES
 from model.gnn import L1_EmbbedingGNN, L1_MaterialActor, L1_OutousrcingActor, L1_SchedulingActor, L1_CommonCritic
-from common import load_instance, to_bool, directory
+from tools.common import load_instance, to_bool, directory
 import torch
 torch.autograd.set_detect_anomaly(True)
 import pandas as pd
 import time as systime
 from typing import Callable
 from torch import Tensor
 from torch.nn import Module
-from instance2graph_translator import translate
+from translators.instance2graph_translator import translate
 from debug.debug_gns import check_completeness, debug_printer
 from gns_ppo_trainer import reward, PPO_train
 from model.agent import MultiAgent_OneInstance

data/results/Shared value loss.png

@@ -2,7 +2,7 @@
 import random
 import pickle
 from model.instance import Instance
-from common import init_several_1D, init_several_2D, init_2D, init_1D, init_several_3D, directory
+from tools.common import init_several_1D, init_several_2D, init_2D, init_1D, init_several_3D, directory
 
 # ==============================================================
 # =*= RANDOM INSTANCE GENERATOR =*=

exact_solver.py

@@ -4,7 +4,7 @@
 from typing import Tuple
 import numpy as np
 from torch.nn import Module
-from tensors import add_into_tensor
+from tools.tensors import add_into_tensor
 
 # ===========================================================
 # =*= DATA MODEL FOR PPO AGENT CONFIGURATION AND RESULTS =*=

gns_ppo_trainer.py

@@ -3,8 +3,8 @@
 from torch_geometric.data.storage import EdgeStorage
 from torch_geometric.utils import to_dense_adj
 from torch import Tensor
-from common import num_feature
-from tensors import features2tensor, id2tensor
+from tools.common import num_feature
+from tools.tensors import features2tensor, id2tensor
 from .instance import Instance
 
 # =============================================================

gns_solver.py

@@ -289,6 +289,12 @@ def loop_projects(self):
 
     def loop_items(self, p: int):
         return range(self.E_size[p])
+    
+    def loop_resources(self):
+        return range(self.nb_resources)
+    
+    def loop_rts(self):
+        return range(self.nb_resource_types)
 
     def loop_operations(self, p: int):
         return range(self.O_size[p])

instance_generator.py

@@ -1,3 +1,7 @@
+from model.instance import Instance
+from tools.common import init_several_1D, init_2D, init_several_2D, init_3D
+import random
+
 # ====================================================================
 # =*= EXACT (Google OR-Tool) SOLUTION DATA STRUCTURE =*=
 # ====================================================================
@@ -12,4 +16,34 @@ def __init__(self):
         self.precedes = [] # Relation between operations (p1, p2, o1, o2, feasible r)
         self.D_setup = [] # Design setup (p, o, r, s)
         self.Cmax = -1 # Cmax and objective
-        self.obj = []
+        self.obj = []
+
+class HeuristicSolution(Solution):
+    def __init__(self):
+        Solution.__init__(self)
+        self.sequences = [] # Sequences of each resource rt = (p,o)
+        self.selection = [] # Selection of resourses for each operations and feasible resource type p, o, rt = r
+
+    def random_start_from_instance(self, i: Instance):
+        nb_projects = i.get_nb_projects()
+        elts_per_project = range(i.E_size[0])
+        self.sequences = [[(p,o) for p, o in i.operations_by_resource_type(rt)] for rt in i.loop_rts()]
+        for rt in i.loop_rts():
+            random.shuffle(self.sequences[rt])
+        self.selection = [[[random.choice(i.resources_by_type(rt)) for rt in i.required_rt(p,o)] for o in i.loop_operations(p)] for p in i.loop_projects()]
+        self.E_start = [[0 for e in elts_per_project] for p in i.loop_projects()]
+        self.E_outsourced = [[False for e in elts_per_project] for p in i.loop_projects()]
+        self.E_prod_start = [[0 for e in elts_per_project] for p in i.loop_projects()]
+        self.E_end = [[0 for e in elts_per_project] for p in i.loop_projects()]
+        self.E_validated = [[0 for e in elts_per_project] for p in i.loop_projects()]
+        self.O_uses_init_quantity, self.O_start, self.O_setup, self.O_end, self.O_executed, self.D_setup = init_several_1D(nb_projects, None, 6)
+        self.precedes = init_2D(nb_projects, nb_projects, None)
+        self.Cmax = 0
+        for p in i.loop_projects():
+            nb_ops = i.O_size[p]
+            self.O_uses_init_quantity[p], self.O_setup[p], self.O_executed[p] = init_several_2D(nb_ops, i.nb_resources, False, 3)
+            self.O_start[p], self.O_end[p] = init_several_2D(nb_ops, i.nb_resources, 0, 2)
+            self.D_setup[p] = init_3D(nb_ops, i.nb_resources, i.nb_settings, False)
+            for p2 in i.loop_projects():
+                nb_ops2 = i.O_size[p2]
+                self.precedes[p][p2] = init_3D(nb_ops, nb_ops2, i.nb_resources, False)

model/agent.py

@@ -2,7 +2,7 @@
 import argparse
 import glob
 import os
-from common import directory
+from tools.common import directory
 import pickle
 from model.agent import MAPPO_Losses
 from torch import Tensor

model/graph.py

@@ -0,0 +1,6 @@
+# =====================================================================
+# =*= Tabu Search Solver (generate or improve an existing solution) =*=
+# =====================================================================
+__author__ = "Anas Neumann - [email protected]"
+__version__ = "1.0.0"
+__license__ = "Apache 2.0 License"

model/instance.py

@@ -69,4 +69,4 @@ def search_object_by_id(objects: list[generic_object], id: int):
     for obj in objects:
         if obj['id'] == id:
             return obj
-    return None
+    return None

model/solution.py

@@ -0,0 +1,9 @@
+
+# =============================================================================
+# =*= TRANSLATE GRPAH 2 SOLUTION =*=
+# Complete code to translate a model.GraphInstance to a model.HeuristicSolution
+# =============================================================================
+__author__ = "Anas Neumann - [email protected]"
+__version__ = "1.0.0"
+__license__ = "Apache 2.0 License"
+