update

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/.name

@@ -0,0 +1 @@
+OrderSystem.py

.idea/HTSim.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (2)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (2)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/HTSim.iml" filepath="$PROJECT_DIR$/.idea/HTSim.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

ABMfim流程00.txt

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+！按子系统功能！
+
+Oss:
+接收订单。
+计算到当期为止，未交付订单的各类产品与其库存之差，得到gap最大的产品，并将能获得该商品最大生产率的生产状态通知Iss和Pss。
+
+交付当期到期和已经出现延期的订单，先进先出。
+
+计算（env）延期平均时间：延期时间 * 延期产品数/10000
+
+Iss：
+按照Oss指定的生产状态，准备原材料并交付Pss（如果原材料不充足，就将能交付的先交付）
+
+每期末统计原材料使用情况，按照遗传算法提供的Sspolicy，选择是否订货（初始原材料备货为s），如果订货要随机生成该原材料的交货期。
+
+每期末要更新原材料剩余情况（上一次余值+当期到货量-当期使用量）
+
+更新产成品期末库存。
+
+
+Pss：
+根据Oss的指示，开始生产，如果原材料不充足，就按照gap大小进行生产；如果没有gap的产品了，就按照库存水平由少到多进行生产。
+（此处如果出现原材料余料，回交Iss）
+生成最终生产结果，产成品交付Iss。
+
+
+！按流程 ！
+
+Oss:接收订单。计算到当期为止，未交付订单的各类产品与其库存之差，得到gap最大的产品，并将能获得该商品最大生产率的生产状态通知Iss和Pss。
+
+Iss：按照Oss指定的生产状态，准备原材料并交付Pss（如果原材料不充足，就将能交付的先交付）。
+
+Pss：根据Oss的指示，开始生产，如果原材料不充足，就按照gap大小进行生产；如果没有gap的产品了，就按照库存水平由少到多进行生产。
+（此处如果出现原材料余料，回交Iss）
+生成最终生产结果，产成品交付Iss。
+
+Iss：统计原材料使用情况，按照遗传算法提供的Sspolicy，选择是否订货（初始原材料备货为s），如果订货要随机生成该原材料的交货期。
+每期末要更新原材料剩余情况（上一次余值+当期到货量-当期使用量）。
+更新产成品期末库存。
+
+Oss：交付当期到期和已经出现延期的订单，先进先出。计算（env）延期平均时间：延期时间 * 延期产品数/10000
+

Environment.py

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+import random
+import numpy as np
+import pandas as pd
+import agentpy as ap
+from datetime import datetime
+from numpy import random
+import json
+
+from Firm import Firm
+# import passive agents
+from Order import Order
+
+from fake_api import get_plan_by_pp_id, get_bom_by_prd_id
+
+
+class FMSEnv(ap.Model):
+    # put all parameters here, not in any other places
+    # xv_int_max_order: int
+    # ev_n_order_created: int
+
+    the_firm: Firm
+
+    # record data, define below
+    # op_os_n_total_order: int
+    # op_os_n_total_order_delayed: int
+    op_os_all_delay_time: list
+    # op_os_delay_ratio: float
+    # op_is_flt_material_room_left: float
+    # op_is_flt_product_room_left: float
+
+    op_ps_str_status: str
+    op_os_to_dlv: np.ndarray
+    op_is_current_product: np.ndarray
+    op_is_current_material: np.ndarray
+    op_is_trans_material: np.ndarray
+    op_ps_back_trans_material: np.ndarray
+    op_ps_produced_num: np.ndarray
+    op_is_ip_mat_id: np.ndarray
+    op_ip_prd_s: np.ndarray
+    op_ip_prd_big_s: np.ndarray
+    op_ip_prd_est_pfm: int
+
+    def __init__(self, dct_all_para, _run_id=None):
+        super().__init__()
+
+        # create agents here
+        self.the_firm = Firm(env=self, dct_all_para=dct_all_para)
+
+        # get the input parameter values from the dictionary
+        self.int_stop_time = int(dct_all_para['time'])
+        # self.xv_int_max_order = int(dct_all_para['xv_int_max_order'])
+        self.xv_dlv_product_para = np.asarray(dct_all_para['xv_dlv_product_para'])
+        # self.xv_int_dlv_period_lam = int(dct_all_para['xv_int_dlv_period_lam'])
+        # self.ev_n_order_created = 0
+
+        self.running = True
+        self.t = 0
+
+    # Creation of orders should be done in the environment
+    def create_order(self):
+        # Check if maximum number of orders has been reached
+        xv_int_create_order_num = 1
+        # xv_int_create_order_num = random.poisson(lam=xv_int_create_order_lam, size=None)
+        # if self.ev_n_order_created < xv_int_max_order:
+        for i in range(xv_int_create_order_num):
+            new_order = Order(model=self, time_created=self.t, xv_dlv_product_para=self.xv_dlv_product_para,
+                                  leadtime=max(self.the_firm.xv_ary_lead_time))
+            return new_order
+        return None
+
+    # Execute the interactions of each time step in the simulation.
+    def step(self):
+        # organize the interactions of agents in each time step here
+        new_order = self.create_order()
+        self.the_firm.the_os.accept_order(new_order)
+
+        self.the_firm.operating()
+        self.update()
+
+        if self.t >= self.int_stop_time:
+            self.running = False
+            self.stop()
+        else:
+            print(f"running the {self.t} step")
+
+    # Record data after each simulation
+    def update(self):
+        self.op_os_n_total_order = len(self.the_firm.the_os.a_lst_order)
+        # self.op_os_n_total_order_delayed = len([e for e in self.the_firm.the_os.a_lst_order if e.xv_dlv_t < self.t])
+        self.op_os_to_dlv = self.the_firm.the_os.ev_ary_to_dlv
+        # self.op_os_all_delay_time = self.the_firm.the_os.ev_lst_all_delay_time
+
+        self.op_ps_produced_num = self.the_firm.the_ps.ev_ary_produced_num
+        self.op_ps_str_status = self.the_firm.the_ps.ev_str_status
+
+        # self.op_is_current_product = self.model.the_firm.the_is.ev_ary_current_product
+        #
+        # self.op_is_current_material = self.model.the_firm.the_is.ev_ary_current_material
+        #
+        # self.op_is_trans_material= self.model.the_firm.the_is.ev_lst_trans_material
+
+        self.op_ps_back_trans_material = self.model.the_firm.the_ps.ev_lst_backtrans_material
+
+        self.record([att for att in self.__dict__.keys() if att.startswith('op_')])
+        pass
+
+
+if __name__ == '__main__':
+    dct_para = {
+        'time': 60,
+        # 'xv_int_max_order': random.randint(30, 50),
+        # 'xv_dlv_product_para': tuple([(30, 100), (30, 50)]),
+        # 'xv_dlv_product_para': tuple([30,40,30,20]), # 读取生产率 np.read.
+        # 'xv_int_dlv_period_lam': 8.5,
+        # 'xv_int_create_order_lam': 2,
+        # 'xv_ary_price_product': tuple([0.3,0.2,0.5,1]),
+        # 'xv_ary_cost_material_per': tuple([0.1,0.1,0.2,0.4]),
+        # 'xv_ary_volume_material': tuple([1.0, 1.5]),
+        # 'xv_ary_volume_product': tuple([3.0, 5.0]),
+        'xv_array_lead_time': 2, # 读取原材料表格 np.read.,暂时不读
+        # 'xv_int_lead_time_c': 3,
+        # 'xv_int_lead_time_d': 1,
+         'xv_ary_initial_product_num': pd.read_excel("initial_product.xlsx").to_numpy(),
+        'xv_ary_initial_material_num': pd.read_excel("initial_material.xlsx").to_numpy(),  # 应读取遗传算法中随机生成的s，暂写为'1' 创建两个excel分别存储产品和原材料的库存 每个excel中存系统代码和库存
+        # 'xv_flt_initial_cash': 50000.0,
+        # 'dct_status_info': json.dumps({   #需要引入生产状态表
+        #     "0": {"xv_flt_produce_rate": tuple([0.0, 0.0]),
+        #           "xv_ary_mat_material": tuple([0.0, 0.0]),
+        #           "xv_flt_broken_rate": 0,
+        #           "xv_flt_run_cost": 0.0,
+        #           "name": "wait"
+        #           },
+        #     "1": {"xv_flt_produce_rate": tuple([90.0, 0.0]),
+        #           "xv_ary_mat_material": tuple([4.0, 1.0]),
+        #           "xv_flt_broken_rate": 0.03,
+        #           "xv_flt_run_cost": 40.0,
+        #           "name": "produceA"
+        #           },
+        #     "2": {"xv_flt_produce_rate": tuple([0.0, 60.0]),
+        #           "xv_ary_mat_material": tuple([1.5, 5.0]),
+        #           "xv_flt_broken_rate": 0.05,
+        #           "xv_flt_run_cost": 50.0,
+        #           "name": "produceB"
+        #           },
+        #     "3": {"xv_flt_produce_rate": tuple([55.0, 30.0]),
+        #           "xv_ary_mat_material": tuple([2.0, 1.5]),
+        #           "xv_flt_broken_rate": 0.07,
+        #           "xv_flt_run_cost": 60.0,
+        #           "name": "produceAB"
+        #           },
+        #     "-1": {"xv_flt_produce_rate": 0.0,
+        #            "xv_ary_mat_material": tuple([0.0, 0.0]),
+        #            "xv_flt_broken_rate": 0.1,
+        #            "xv_flt_run_cost": 100.0,
+        #            "name": "failed"
+        #            }
+        # })
+
+    }
+    sample = ap.Sample(dct_para)
+
+    exp = ap.Experiment(FMSEnv, sample, iterations=1, record=True)
+    results = exp.run()
+    # results['variables']['FMSEnv'].to_excel(f"simulation-results-{datetime.today().strftime('%Y-%m-%d-%H-%M-%S')}.xlsx",
+    #                                         engine='openpyxl')

Firm.py

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+import numpy as np
+import json
+
+from InventorySystem import InventorySystem
+from OrderSystem import OrderSystem
+from ProduceSystem import ProduceSystem
+
+# Create Company Instance
+class Firm:
+    the_os: OrderSystem
+    the_is: InventorySystem
+    the_ps: ProduceSystem
+
+    def __init__(self, env, dct_all_para):
+        self.env = env
+
+        # get the parameters here
+        self.xv_ary_initial_product_num = np.asarray(dct_all_para['xv_ary_initial_product_num'])
+        self.xv_ary_initial_material_num = np.asarray(dct_all_para['xv_ary_initial_material_num'])
+        self.dct_status_info = json.loads(dct_all_para['dct_status_info'])
+        self.xv_ary_lead_time = np.array([dct_all_para['xv_int_lead_time_a'], dct_all_para['xv_int_lead_time_b']])
+
+        # create agents here
+        self.the_os = OrderSystem(env)
+        self.the_is = InventorySystem(env, xv_ary_initial_material_num=self.xv_ary_initial_material_num,
+                                      xv_ary_initial_product_num=self.xv_ary_initial_product_num,
+                                      xv_ary_lead_time=self.xv_ary_lead_time)
+        self.the_ps = ProduceSystem(env, dct_status_info=self.dct_status_info)
+        # self.the_fs = FinancialSystem(env, xv_flt_initial_cash=self.xv_flt_initial_cash)
+
+    def operating(self):
+        self.the_os.rank_order()
+        self.the_ps.change_status()
+        self.the_ps.run_produce()
+        self.the_os.do_shipment()
+        # self.the_is.inventory_cost()
+        # self.the_fs.financial_calculating()

InventorySystem.py

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+import agentpy as ap
+import numpy as np
+from collections import deque
+
+
+class InventorySystem(ap.Agent):
+
+    xv_ary_initial_material_num: np.ndarray
+    xv_ary_initial_product_num: np.ndarray
+    ev_ary_current_material: np.ndarray  #期末库存
+    ev_ary_current_product: np.ndarray  #期末库存
+    ev_lst_trans_quan_material: np.ndarray # Iss传递给Pss的原材料数量
+    ev_lst_backtrans_quan_material: np.ndarray # Pss退回的原材料数量
+    ev_ary_material_state_to_use: np.ndarray # 当期预期使用的原材料
+    ev_ary_material_to_use: np.ndarray # 当期实际消耗的原材料
+    xv_array_dlv_product: np.ndarray   # 产品交付
+
+    ev_ary_is_order_material: np.ndarray # 是否订购产品
+    ev_ary_num_material_to_order: np.ndarray # 订购数量
+    ev_ary_num_material_order_done: np.ndarray # 当期送到的数量
+
+    # set the lead time of material replenishment
+    xv_ary_lead_time: np.ndarray
+
+    # the list of materials in transit
+    ev_lst_trans_material: list  # Iss交给Pss的原材料
+    ev_list_back_trans_material: list # Pss返回的原材料
+
+    ev_lst_material_inventory: list
+    ev_lst_product_inventory: list
+
+    # Material list
+    xv_lst_material: np.ndarray
+    xv_ary_bom: np.ndarray
+
+    # 原材料订购的开始时间和到达时间
+    ev_int_order_time: np.ndarray
+    ev_int_arrive_time: np.ndarray
+    ev_ary_num_material_order_done: np.ndarray # 当期原材料到货量
+
+    def setup(self, xv_ary_initial_material_num,
+              xv_ary_initial_product_num, xv_ary_lead_time):
+
+        # Set up the initial inventory quantity
+        self.xv_ary_initial_material_num = xv_ary_initial_material_num
+        self.xv_ary_initial_product_num = xv_ary_initial_product_num
+        self.ev_ary_current_material = xv_ary_initial_material_num # 115*2
+        self.ev_ary_current_product = xv_ary_initial_product_num #23*2
+        self.ev_ary_num_material_order_done = np.zeros((115, ))
+
+        # set the lead time of materials
+        # 读取各个原材料的历史平均交货周期 bom table
+        self.xv_ary_lead_time = pd.read_excel("bom23.xlsx").iloc[:, 4].to_numpy()
+        # 切换成原材料
+        self.ev_ary_is_order_material = np.array([False for i in range(115)])
+        self.ev_ary_num_material_to_order = np.zeros((115,))  # 初始化
+        self.ev_ary_material_state_to_use = np.zeros((115,))
+
+        self.ev_lst_material_inventory = []
+        self.ev_lst_product_inventory = []
+
+        self.xv_lst_material = pd.read_excel("rawmaterial.xlsx").to_numpy()
+
+        self.xv_ary_bom = pd.read_excel("bom23.xlsx").to_numpy()
+
+        self.ev_lst_trans_quan_material = []  # Iss传递给Pss的原材料数量
+        self.ev_lst_backtrans_quan_material = []  # Pss退回的原材料数量
+
+        self.ev_int_order_time = np.zeros((115,))
+        self.ev_int_arrive_time = np.zeros((115,))
+
+    def material_state_to_use(self, ev_int_produce_type, xv_plan_excel):
+        # 读取Oss决定的生产状态，根据生产状态所需的原材料进行汇总
+        # self. ev_ary_material_state_to_use = np.multiply(self.xv_array_dlv_product, 原材料表 )
+        produce_state = ev_int_produce_type  # 获取生产状态
+        produce_plan = xv_plan_excel[xv_plan_excel[:, 0] == produce_state]
+        for plan in produce_plan:
+            for bom in self.xv_ary_bom:
+                if plan[1] == bom[0]:
+                    self.ev_ary_material_state_to_use[np.where(self.xv_lst_material == bom[1])] += plan[3] * bom[
+                        2]  # 某原材料的生产数量加等产品生产量乘单位原材料消耗量
+
+    def material_check(self, ev_ary_material_state_to_use):
+        # 根据Iss决策，核对库存是否充足，并将拥有的原材料交给Pss
+        # 可能需要一部生成原材料列表在调用
+        # Check whether materials are enough and transfer material
+        index_mapping = {value: idx for idx, value in enumerate(self.ev_ary_current_material[:, 0])}
+        sorted_indices = [index_mapping[value] for value in self.xv_lst_material]
+        self.ev_ary_current_material = self.ev_ary_current_material[sorted_indices]  # 按照原材料制定顺序进行重排
+        for i in range(115):
+            if self.ev_ary_current_material[i, 1] >= ev_ary_material_state_to_use[i]:
+                self.ev_lst_trans_quan_material[i] = ev_ary_material_state_to_use[i]
+                # return (self.ev_ary_current_material >= ev_ary_material_to_use).all()
+            else:
+                self.ev_lst_trans_quan_material[i] = self.ev_ary_current_material[i]
+
+    def consume_and_store(self, ev_ary_material_to_use, ev_ary_produced_num,ev_changed_product,
+                          ev_lst_backtrans_material):
+        self.ev_lst_backtrans_quan_material = ev_lst_backtrans_material
+        # Update the inventory after production
+        for i in range(115):
+            self.ev_ary_material_to_use[i] = self.ev_lst_trans_quan_material[i] - \
+                                             self.ev_lst_backtrans_quan_material[i]
+
+            if self.ev_int_arrive_time == self.model.t:  # 判断材料是否到达
+                self.ev_ary_num_material_order_done[i] += self.ev_ary_num_material_to_order[i]
+                self.ev_ary_num_material_to_order[i] = 0
+                self.ev_ary_is_order_material[i] = False
+
+        self.ev_ary_current_material = self.ev_ary_current_material - ev_ary_material_to_use \
+                                       + self.ev_ary_num_material_order_done
+        self.ev_ary_current_product = ev_changed_product + ev_ary_produced_num
+        return self.ev_ary_current_material, self.ev_ary_current_product
+
+    def material_replenishment(self):
+        # Check the inventory of material to decide whether to replenish
+        # 核对原材料数量是否低于s
+        # 带时间戳
+        for i in range(115):
+            if self.ev_ary_current_material[i] <= s:
+                self.ev_ary_is_order_material[i] = True
+                self.ev_ary_num_material_to_order[i] = S - self.ev_ary_current_material[i]
+                self.ev_int_order_time[i] = self.model.t
+                self.ev_int_arrive_time[i] = self.ev_int_order_time[i] + \
+                                             self.xv_ary_bom[self.xv_ary_bom[:, 1] == self.xv_lst_material[i]][0, 4]
+            else:
+                self.ev_ary_num_material_to_order[i] = 0
+

Order.py

@@ -0,0 +1,36 @@
+import agentpy as ap
+import random
+import numpy as np
+from numpy import random
+import pandas as pd
+
+
+class Order(ap.Agent):
+
+    xv_time_created: int # 订单创建时间
+    xv_time_circle: int # 随机生成的交货周期
+    xv_dlv_t: int # 客户希望的交货时间
+    ev_actual_dlv_t: int # 实际交付时间
+
+    ev_is_delivered: bool # 订单是否已交付
+    ev_is_accepted: bool # 订单是否被接受
+    ev_int_delay_time: int # total delay time
+
+    xv_ary_dlv_product: np.ndarray
+    ev_ary_dlv_product: np.ndarray
+
+    def setup(self, time_created):
+        self.xv_time_created = time_created
+        # read the demand of 23 productions
+        df = pd.read_excel("demand23.xlsx")
+        self.xv_ary_dlv_product = df.to_numpy()
+        df = df.iloc[:, 1]
+        self.ev_ary_dlv_product = df.to_numpy()
+        self.xv_time_circle = np.random.randint(7, 11, 1)
+        self.xv_dlv_t = self.xv_time_created + self.xv_time_circle
+        self.ev_actual_dlv_t = self.xv_dlv_t
+        self.ev_int_delay_time = self.ev_actual_dlv_t - self.xv_dlv_t
+
+        # Set the initial status of order to be undelivered, accepted
+        self.ev_is_delivered = False
+        self.ev_is_accepted = False

OrderSystem.py

@@ -0,0 +1,103 @@
+import agentpy as ap
+import numpy as np
+from Order import Order
+import pandas as pd
+
+# 输出一个生产状态指令，一个交付情况，一个延期时间计算
+class OrderSystem(ap.Agent):
+    a_lst_order: ap.AgentList[Order]
+    ev_int_produce_type: int
+    ev_ary_to_dlv: np.ndarray # 当前order list内所有产品的综合
+    ev_ary_product_to_produce: np.ndarray  # 用于计算 production gap
+    ev_lst_all_delay_time: list
+    ev_ave_delay_time: float
+    xv_plan_excel: np.ndarray
+    ev_lst_flag: list
+    ev_changed_product: np.ndarray
+    # ev_ary_dlv_product: np.ndarray??????
+
+    def setup(self):
+        # Create a list of order
+        self.a_lst_order = ap.AgentList(self, [])
+        self.ev_ary_to_dlv = np.zeros((23, 1))
+        self.ev_int_produce_type = 0
+        self.ev_ary_product_to_produce = np.zeros((23,))
+        self.ev_lst_all_delay_time = []
+        self.ev_ave_delay_time = 0
+        # self.ev_ary_dlv_product = np.zeros((23,))  ????
+        self.ev_changed_product = np.zeros((23, ))
+        self.xv_plan_excel = pd.read_excel("plan.xlsx").to_numpy()
+        pass
+
+    def accept_order(self, new_order):
+        # Determine whether the order is received and all are currently received
+        if new_order is not None:
+            new_order.ev_is_accepted = True
+            self.a_lst_order.append(new_order)
+
+    def rank_order(self):
+        # Sort order
+        self.a_lst_order = self.a_lst_order.sort('xv_dlv_t', reverse=False)
+
+    def produce_status(self):
+        # 计算a_lst_order内所有订单包含的产品总量与当期库存的gap，排序最大缺口量，选定生产状态
+        self.ev_ary_to_dlv = np.zeros((23,))
+        for order in self.model.the_firm.the_os.a_lst_order:
+            self.ev_ary_to_dlv += order.ev_ary_dlv_product  # 当前天之前所有的还未满足的需求求和
+        self.ev_ary_product_to_produce[:, 1] = self.ev_ary_to_dlv - self.model.the_firm.the_is.ev_ary_current_product
+        # self.ev_ary_product_to_produce = self.model.the_firm.the_is.ev_ary_current_product - self.ev_ary_to_dlv
+        # self.ev_ary_product_to_produce > 0:
+        # 选出这些产品，按照数值大小进行分类，数值最大的产品对应的能带来最大生产率的状态，则选定为ev_int_produce_type
+        # 如果均<=0, 按照产品库存ev_ary_current_material进行排序，选择能给库存最低的产品带来最高生产率的状态
+        sorted_indices = np.argsort(self.ev_ary_product_to_produce[:, 1])[::-1]
+        sorted_data = self.ev_ary_product_to_produce[sorted_indices]
+        if sorted_data[0, 1] > 0:  # 判断是否存在库存不足
+            pass
+        else:
+            sorted_indices = np.argsort(self.model.the_firm.the_is.ev_ary_current_product[:, 1])  # 对库存进行从小到大排序
+            sorted_data = self.model.the_firm.the_is.ev_ary_current_product[sorted_indices]
+
+        a = self.xv_plan_excel[self.xv_plan_excel[:, 1] == sorted_data[0, 0]]  # 检索最大值的产品的四种方案或者最小库存产品的四种方案
+        sorted_indices = np.argsort(a[:, 3])[::-1]
+        sorted_data = a[sorted_indices]
+        # return sorted_data[0, 0]
+        self.ev_int_produce_type = sorted_data[0, 0]
+        return self.ev_int_produce_type
+
+    def do_shipment(self,ev_ary_current_product):
+        # Make shipments based on ranked order list
+        # 交付两次，第一次未交付完成的订单要标记，交货的时候先便利delay的订单，如果能够满足全部剩余量，就交货，如果不能
+        # 就继续在列表中存在
+        # 只有完全交付的订单，才计算delay time = order.ev_int_delay_time * 第二次交付的订单内的各类产品之和
+        # 需要更新 order.ev_ary_dlv_product
+        # Make shipments based on ranked order list
+        self.ev_lst_all_delay_time = []
+        self.ev_ave_delay_time = 0
+        self.ev_changed_product = ev_ary_current_product  # 23x1 ndarray 存储本次库存的改变
+        for order in self.a_lst_order:
+            if order.xv_dlv_t == self.model.t:  # 第一次交付
+                # Check and make shipment
+                order.ev_is_delivered = True
+                for i in range(len(order.ev_ary_dlv_product)):
+                    if order.ev_ary_dlv_product[i] <= self.ev_changed_product[i]:
+                        order.ev_ary_dlv_product[i] = 0
+                        self.ev_changed_product[i] -= order.ev_ary_dlv_product[i]
+                    else:
+                        order.ev_is_delivered = False
+            elif order.xv_dlv_t < self.model.t and order.ev_is_delivered == False:  # 第二次交付
+                order.ev_is_delivered = True
+                for i in range(len(order.ev_ary_dlv_product)):
+                    if order.ev_ary_dlv_product[i] > self.ev_changed_product[i]:  # 先判断能不能一次性交付
+                        order.ev_is_delivered = False
+
+                if order.ev_is_delivered:  # 如果一次性交付
+                    delay_num = np.sum(order.ev_ary_dlv_product)
+                    order.ev_ary_dlv_product = np.zeros((23,))
+                    self.ev_changed_product = self.ev_changed_product - order.ev_ary_dlv_product
+
+                    order.ev_actual_dlv_t = self.model.t
+                    order.ev_int_delay_time = order.ev_actual_dlv_t - order.xv_dlv_t
+
+                    self.ev_ave_delay_time += order.ev_int_delay_time * delay_num / 10000
+
+        return self.ev_changed_product, self.ev_ave_delay_time

ProduceSystem.py

@@ -0,0 +1,60 @@
+import agentpy as ap
+import numpy as np
+import random
+
+
+class ProduceSystem(ap.Agent):
+    ev_str_status: str # 0,6,8,10 pp_id
+    ev_lst_backtrans_material: list  # 退回Iss的原材料
+    ev_ary_produce_number: np.ndarray
+
+    def setup(self, dct_status_info):
+        self.ev_str_status = 0
+        self.ev_lst_backtrans_material = []
+
+    def change_status(self):
+        self.ev_str_status = self.model.the_firm.the_os.produce_status()
+
+
+    def run_produce(self, ev_lst_material, ev_lst_trans_quan_material, ev_ary_product_to_produce, xv_plan_excel,
+                    xv_ary_bom):  # ev_ary_product_to_produce 是 需求和库存的gap 23x2
+        # 生产状态由Iss确定，这个函数需要计算生产数量和原材料消耗量。
+        # 如果原材料不足，就按照production gap(ev_ary_product_to_produce) 大小进行生产；如果没有gap的产品了，就按照库存水平由少到多进行生产。
+        # 输出生产结果和原材料消耗结果
+        produce_plan = xv_plan_excel[xv_plan_excel[:, 0] == self.ev_str_status]
+        sorted_indices = np.argsort(ev_ary_product_to_produce[:, 1])[::-1]
+        sorted_data = ev_ary_product_to_produce[sorted_indices]  # 对gap进行从大到小排序
+        sorted_indices_product = np.argsort(self.model.the_firm.the_is.ev_ary_current_product[:, 1])  # 对库存进行从小到大排序
+        sorted_data_product = self.model.the_firm.the_is.ev_ary_current_product[sorted_indices_product]
+        self.ev_lst_backtrans_material = ev_lst_trans_quan_material
+        self.ev_ary_produce_number = []
+        for product in sorted_data:
+            if product[1] > 0:  # gap存在
+                product_material = xv_ary_bom[xv_ary_bom[:, 0] == product[0]]
+                produce_number = produce_plan[produce_plan[:, 1] == product[0]][3]
+                for material in product_material:
+                    produce_number = min(produce_number, int(
+                        self.ev_lst_backtrans_material[np.where(ev_lst_material == material[1])] / material[
+                            2]))  # 取能生产的最小个数
+                for material in product_material:
+                    self.ev_lst_backtrans_material[np.where(ev_lst_material == material[1])] -= produce_number * material[
+                        2]  # 更新原材料消耗情况
+                self.ev_ary_produce_number.append([product[0], produce_number])
+                sorted_data_product = sorted_data_product[sorted_data_product[:, 0] != product[0]]
+            else:
+                for current_product in sorted_data_product:
+                    product_material = xv_ary_bom[xv_ary_bom[:, 0] == current_product[0]]
+                    produce_number = produce_plan[produce_plan[:, 1] == current_product[0]][3]
+                    for material in product_material:
+                        produce_number = min(produce_number, int(
+                            self.ev_lst_backtrans_material[np.where(ev_lst_material == material[1])] / material[
+                                2]))  # 取能生产的最小个数
+                    for material in product_material:
+                        self.ev_lst_backtrans_material[np.where(ev_lst_material == material[1])] -= produce_number * \
+                                                                                                material[2]  # 更新原材料消耗情况
+                    self.ev_ary_produce_number.append([current_product[0], produce_number])
+                    break
+
+        self.ev_ary_produce_number = np.array(self.ev_ary_produce_number)
+
+        return self.ev_ary_produce_number, self.ev_lst_backtrans_material

bom23.csv

@@ -0,0 +1,208 @@
+prd_id,mtr_id,qty,unit
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+AVSR3R3V0609,YHBH421H,0.00011,千克
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+AVSR3R3V0609,P06-20I,0.0001,万只
+AVSR3R3V0609,EF063C,0.000034,千克
+ACSR4R7V0812,Z530V12001070,0.000623,平方米
+ACSR4R7V0812,F30VC20A070,0.000728,平方米
+ACSR4R7V0812,EHD450Z085,0.000094,千克
+ACSR4R7V0812,D12087F2126,0.0001,万对
+ACSR4R7V0812,YG00001,2.07E-05,卷
+ACSR4R7V0812,YHBH421H,0.00019,千克
+ACSR4R7V0812,N080-130Y,0.0001,万支
+ACSR4R7V0812,P08-30I,0.0001,万只
+ACSR4R7V0812,GS080T,0.000072,千克
+AMSR1R0M0609VI,Z480V12002050,0.000115,平方米
+AMSR1R0M0609VI,F30VC30A050,0.000165,平方米
+AMSR1R0M0609VI,EHD450Z065,0.000018,千克
+AMSR1R0M0609VI,D12073TL2126b,0.0001,万对
+AMSR1R0M0609VI,YG00002,9.33E-06,卷
+AMSR1R0M0609VI,YHBH421H,0.00011,千克
+AMSR1R0M0609VI,T063-100Y,0.0001,万支
+AMSR1R0M0609VI,P06-26T,0.0001,万只
+AMSR1R0M0609VI,MS063T,0.000039,千克
+AGTR4R7V0812V,Z510V12001070,0.000623,平方米
+AGTR4R7V0812V,F30VC20A070,0.000784,平方米
+AGTR4R7V0812V,W19020K085,0.000094,千克
+AGTR4R7V0812V,D12087F2126,0.0001,万对
+AGTR4R7V0812V,YG00001,2.07E-05,卷
+AGTR4R7V0812V,YHBH421H,0.00019,千克
+AGTR4R7V0812V,N080-130Y,0.0001,万支
+AGTR4R7V0812V,P08-30I,0.0001,万只
+AGTR4R7V0812V,GT080C,0.000071,千克
+WL1V477M10016,Z047V10401100,0.00153,平方米
+WL1V477M10016,F00VC40A100,0.00202,平方米
+WL1V477M10016,SM26050B120,0.00017,千克
+WL1V477M10016,D15131G2328,0.0001,万对
+WL1V477M10016,YG00004,0.00003,卷
+WL1V477M10016,YHCP50A,0.00049,千克
+WL1V477M10016,L100-175+,0.0001,万支
+WL1V477M10016,GNH-10F,0.0001,万只
+WL1V477M10016,AW100T,0.000101,千克

fake_api.py

@@ -0,0 +1,20 @@
+import pandas as pd
+
+df_bom = pd.read_excel('bom23.xlsx', engine='openpyxl')
+
+df_plan = pd.read_excel('plan.xlsx', engine='openpyxl')
+
+
+def get_bom_by_prd_id(prd_id):
+    df_sub = df_bom.loc[df_bom['prd_id'] == prd_id]
+    return df_sub[['mtr_id', 'qty']]
+
+
+def get_plan_by_pp_id(pp_id):
+    df_sub = df_plan.loc[df_plan['pp_id'] == int(pp_id)]
+    return df_sub[['prd_id', 'size', 'rate']]
+
+
+if __name__ == '__main__':
+    print(get_bom_by_prd_id('ADTB331M0611'))
+    print(get_plan_by_pp_id('0'))  # 0, 6, 8, 10