InitialStateFactory.py

@@ -3,6 +3,7 @@ import random
 from data.Item import Item
 from data.Order import Order
 from data.enum.ItemType import ItemType
+from data.enum.Priority import Priority
 from util.ClientParamsFactory import ClientParamsFactory
 
 
@@ -24,6 +25,36 @@ class InitialStateFactory:
 
         return order_list
 
+    @staticmethod
+    def generate_order_list_XD(output_order_list_size: int):
+        order_list: [Order] = []
+        for i in range(output_order_list_size):
+            order_list.append(InitialStateFactory.__generate_order_XD())
+
+        return order_list
+
+    @staticmethod
+    def __generate_order_XD() -> Order:
+        order_size = random.randint(1, 4)
+
+        items: [Item] = []
+        for i in range(order_size):
+            items.append(InitialStateFactory.__generate_item())
+
+        time_base = random.randint(8, 20)
+        final_time = time_base * order_size
+
+        client_params = ClientParamsFactory.get_client_params()
+
+        x = random.randint(0, 2)
+        type = Priority.LOW
+        if x == 0:
+            type = Priority.MEDIUM
+        elif x == 2:
+            type = Priority.HIGH
+
+        return Order(final_time, items, type, client_params)
+
     @staticmethod
     def __generate_order() -> Order:
         order_size = random.randint(1, 4)

data/GameConstants.py

@@ -1,5 +1,7 @@
 from typing import Dict
 
+from data.Item import Item
+from data.Order import Order
 from data.enum.ItemType import ItemType
 from util.PathDefinitions import GridLocation
 
@@ -9,16 +11,10 @@ class GameConstants:
             self,
             grid_width: int,
             grid_height: int,
-            # delivery_pos: GridLocation,
-            # order_pos: GridLocation,
-            # special_positions: Dict[ItemType, GridLocation],
             walls: [GridLocation],
             diffTerrain: [GridLocation]
     ):
         self.grid_width = grid_width
         self.grid_height = grid_height
-        # self.delivery_pos = delivery_pos
-        # self.order_pos = order_pos
-        # self.special_positions = special_positions
         self.walls = walls
         self.diffTerrain = diffTerrain

data/Order.py

@@ -15,6 +15,12 @@ class Order:
         self.items: List[Item] = items
         self.client_params = client_params
         self.priority = priority
+        self.sum = 0
+
+    # def sum_items(self, items: [Item]):
+    #     result = 0
+    #     for i in range(len(items)):
+    #         result += items[i]
 
     def __repr__(self) -> str:
         return "items: {} priority: {}".format(self.items, self.priority)

data/enum/GeneticMutationType.py

@@ -0,0 +1,7 @@
+from enum import Enum
+
+
+class GeneticMutationType(Enum):
+    MUTATION = 1
+    CROSS = 2
+    REVERSE = 3

decision/State.py

@@ -1,6 +1,7 @@
 from data.enum.Direction import Direction
 from data.Item import Item
 from data.Order import Order
+from data.enum.Priority import Priority
 from decision.ActionType import ActionType
 from util.PathDefinitions import GridLocation
 
@@ -10,7 +11,7 @@ class State:
                  action_taken: ActionType,
                  forklift_position: GridLocation,
                  forklift_rotation: Direction,
-                 pending_orders: [Order],
+                 pending_orders: [Priority, [Order]],
                  filled_orders: [Order],
                  input_items: [Item]
                  ):

decision/test/ForkliftActions.py

@@ -0,0 +1,9 @@
+from data.GameConstants import GameConstants
+
+
+class ForkliftActions:
+
+    def __init__(self, game: GameConstants,
+                 ) -> None:
+        self.game = game
+

genetic_order/GeneticOrder.py

@@ -0,0 +1,175 @@
+import itertools
+import random
+
+from data.Order import Order
+from data.enum.GeneticMutationType import GeneticMutationType
+from data.enum.Priority import Priority
+
+
+class GeneticOrder:
+    mutation_chance = 50
+    reverse_chance = 20
+    cross_chance = 10
+    best_fit_special = 40
+    best_fit_special_2 = 20
+    population_size = 500
+
+    def __init__(self, orders: [Order]) -> None:
+        self.number_of_populations = 10000
+        self.orders = orders
+
+    def get_mutation_type(self) -> GeneticMutationType:
+        x = random.randint(0, self.mutation_chance + self.cross_chance + self.reverse_chance)
+
+        if (x < self.mutation_chance):
+            return GeneticMutationType.MUTATION
+
+        if (x > self.mutation_chance + self.cross_chance):
+            return GeneticMutationType.REVERSE
+
+        return GeneticMutationType.CROSS
+
+    def mutation(self, population: [int]) -> [int]:
+        x = random.randint(0, len(population)-1)
+        y = random.randint(0, len(population)-1)
+        while x == y:
+            y = random.randint(0, len(population)-1)
+
+        result = population
+
+        pom = population[x]
+        result[x] = population[y]
+        result[y] = pom
+
+        if(result[x] == result[y]):
+            print("PIZDA I CHUJ")
+
+        return result
+
+    def cross(self, population: [int]) -> [int]:
+        x = random.randint(1, len(population)-1)
+
+        result = []
+
+        for i in range(len(population)):
+            result.append(population[(i + x) % len(population)])
+
+        return result
+
+    def reverse(self, population: [int]) -> [int]:
+        x = random.randint(0, len(population))
+        y = random.randint(0, len(population)-1)
+        while x >= y:
+            x = random.randint(0, len(population))
+            y = random.randint(0, len(population)-1)
+
+        result = []
+        # print("X: ", x, " y: ", y)
+
+        for i in range(len(population)):
+            if x <= i <= y:
+                new_i = i - x
+                # print("len:", len(population), " new_i: ", new_i)
+                result.append(population[y - new_i])
+            else:
+                result.append(population[i])
+
+        return result
+
+    def generate_first_population(self, k: int) -> [[int]]:
+        result = []
+
+        s = range(len(self.orders))
+        p = itertools.permutations(s)
+        while len(result) < k:
+            n = p.__next__()
+            if n not in result:
+                result.append(n)
+
+        return [list(x) for x in result]
+
+        # result = itertools.permutations(range(len(self.orders)))
+        #
+        # return [list(x) for x in result]
+
+    def evaluate(self, member: [int]) -> int:
+        result = 0
+        for i in range(len(self.orders) - 1):
+            x: Order = self.orders[member[i]]
+            y: Order = self.orders[member[i + 1]]
+
+            if ((x.priority == Priority.MEDIUM or x.priority == Priority.LOW) and y.priority == Priority.HIGH) or (x.priority == Priority.LOW and y.priority == Priority.MEDIUM):
+                result += 5000
+            elif x.sum / x.time < y.sum / y.time:
+                result += y.sum * 5 + y.time
+
+        return result
+
+    def mutate_population(self, order_population: [[int]]) -> [[int]]:
+        result = []
+
+        for i in range(len(order_population)):
+            member: [int] = order_population[i]
+            operation: GeneticMutationType = self.get_mutation_type()
+
+            if operation == GeneticMutationType.MUTATION:
+                member = self.mutation(member)
+            elif operation == GeneticMutationType.REVERSE:
+                member = self.reverse(member)
+            else:
+                member = self.cross(member)
+
+            result.append(member)
+
+        return result
+
+    def get_next_population(self, population: [[int]]) -> [[int]]:
+        result = []
+
+        result = population
+
+        # for i in range(len(population) - self.best_fit_special):
+        #     result.append(population[i])
+        #
+        # k = len(population) - self.best_fit_special
+        # while k < len(population) - self.best_fit_special_2:
+        #     n = random.randint(0, self.best_fit_special)
+        #     result.append(population[n])
+        #
+        # left_size = len(population) - k
+        # while left_size < len(population):
+        #     n = random.randint(0, self.best_fit_special_2)
+        #     result.append(population[n])
+
+        return result
+
+    def get_orders_sorted(self, orders: [Order]) -> [Order]:
+        self.orders = orders
+
+        population: [[int]] = self.generate_first_population(self.population_size)
+        print(population)
+
+        population.sort(key=self.evaluate)
+        best_fit: [int] = population[0]
+
+        for i in range(self.number_of_populations):
+            # print("population: ", i)
+            population = self.mutate_population(population)
+            population.sort(key=self.evaluate)
+
+            if self.evaluate(best_fit) > self.evaluate(population[0]):
+                best_fit = population[0]
+
+            # population = self.get_next_population(population).sort(key=self.evaluate)
+
+            if self.evaluate(best_fit) < self.evaluate(population[0]):
+                population[0] = best_fit
+
+
+        best: [int] = population[0]
+        result: [Order] = []
+
+        for i in range(len(best)):
+            result.append(self.orders[best[i]])
+
+        return result

main.py

@@ -5,11 +5,16 @@ from mesa.visualization.modules import CanvasGrid
 
 from ForkliftAgent import ForkliftAgent
 from GameModel import GameModel
+from InitialStateFactory import InitialStateFactory
 from PatchAgent import PatchAgent
 from PatchType import PatchType
 from PictureVisualizationAgent import PictureVisualizationAgent
+from data.Order import Order
 from data.enum.Direction import Direction
 from tensorflow import keras
+
+from data.enum.Priority import Priority
+from genetic_order.GeneticOrder import GeneticOrder
 from util.PathDefinitions import GridWithWeights
 from visualization.DisplayAttributeElement import DisplayAttributeElement
 from visualization.DisplayItemListAttribute import DisplayItemListAttributeElement
@@ -96,5 +101,43 @@ if __name__ == '__main__':
                            "Automatyczny Wózek Widłowy",
                            dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=model))
 
-    server.port = 8888
-    server.launch()
+    def pizda(order: Order):
+        return order.id
+
+    def evaluate(member: [Order]) -> int:
+        result = 0
+        for i in range(len(member) - 1):
+            x: Order = member[i]
+            y: Order = member[i + 1]
+
+            if ((x.priority == Priority.MEDIUM or x.priority == Priority.LOW) and y.priority == Priority.HIGH) or (x.priority == Priority.LOW and y.priority == Priority.MEDIUM):
+                result += 5000
+
+        return result
+
+    orders = InitialStateFactory.generate_order_list_XD(20)
+    test: GeneticOrder = GeneticOrder(orders)
+    print("SIEMA: ", evaluate(orders))
+    # for i in orders:
+    #     print("id: {}, priority: {}", i.id, i.priority)
+
+    newOrders = test.get_orders_sorted(orders)
+
+    print("NAURA:", evaluate(newOrders))
+    # for i in newOrders:
+    #     print("id: {}, priority: {}", i.id, i.priority)
+
+    # orders.sort(key=pizda)
+    # newOrders.sort(key=pizda)
+    #
+    # print("SIEMA:")
+    # for i in orders:
+    #     print("id: {}, priority: {}", i.id, i.priority)
+    #
+    # print("NAURA:")
+    # for i in newOrders:
+    #     print("id: {}, priority: {}", i.id, i.priority)
+
+
+    # server.port = 8888
+    # server.launch()