225 lines
10 KiB
Python
225 lines
10 KiB
Python
import random
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import numpy as np
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import pandas as pd
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import agentpy as ap
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from datetime import datetime
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from numpy import random
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import json
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from Firm import Firm
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# import passive agents
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from Order import Order
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from ga_new import GeneticAlgorithm
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from fake_api import get_plan_by_pp_id, get_bom_by_prd_id
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class FMSEnv(ap.Model):
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# put all parameters here, not in any other places
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# xv_int_max_order: int
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# ev_n_order_created: int
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the_firm: Firm # Firm类
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# record data, define below
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# op_os_n_total_order: int
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# op_os_n_total_order_delayed: int
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op_os_all_delay_time: float
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# op_os_delay_ratio: float
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# op_is_flt_material_room_left: float
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# op_is_flt_product_room_left: float
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op_os_int_status: int
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op_os_to_dlv: np.ndarray
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op_is_current_product: np.ndarray
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op_is_current_material: np.ndarray
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op_is_trans_material: np.ndarray
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op_ps_back_trans_material: np.ndarray
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op_ps_produced_num: np.ndarray
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op_is_ip_mat_id: np.ndarray
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op_ip_prd_s: np.ndarray
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op_ip_prd_big_s: np.ndarray
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op_ip_prd_est_pfm: int
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op_os_n_total_order: int
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def __init__(self, dct_all_para, _run_id=None):
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super().__init__()
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# create agents here
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self.the_firm = Firm(env=self, dct_all_para=dct_all_para)
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# get the input parameter values from the dictionary
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self.int_stop_time = int(dct_all_para['time']) # 停止接单时间
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# self.xv_int_max_order = int(dct_all_para['xv_int_max_order'])
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# self.xv_dlv_product_para = np.asarray(dct_all_para['xv_dlv_product_para'])
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# self.xv_int_dlv_period_lam = int(dct_all_para['xv_int_dlv_period_lam'])
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# self.ev_n_order_created = 0
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self.op_os_n_total_order = 0
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self.op_os_int_status = 0
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self.op_os_all_delay_time = 0
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self.running = True
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self.t = 0
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# Creation of orders should be done in the environment
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def create_order(self):
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# Check if maximum number of orders has been reached
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xv_int_create_order_num = 1
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# xv_int_create_order_num = random.poisson(lam=xv_int_create_order_lam, size=None)
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# if self.ev_n_order_created < xv_int_max_order:
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# for i in range(xv_int_create_order_num):
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new_order = Order(model=self, time_created=self.t)
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return new_order
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# return None
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# Execute the interactions of each time step in the simulation.
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def step(self):
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# organize the interactions of agents in each time step here
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new_order = self.create_order() # 接收创建的订单
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self.the_firm.the_os.accept_order(new_order=new_order)
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self.the_firm.operating()
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self.update()
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if self.t >= self.int_stop_time:
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self.running = False
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self.stop()
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# else:
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#
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# # print(f"running the {self.t} step")
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# # print("当期延误时长为：{}".format(self.the_firm.the_os.ev_ave_delay_time))
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# Record data after each simulation
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def update(self): # ?
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self.op_os_n_total_order = len(self.the_firm.the_os.a_lst_order) # 订单个数
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# 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])
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self.op_os_to_dlv = self.the_firm.the_os.ev_ary_to_dlv # 当期及之前未满足需求总和
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# self.op_os_all_delay_time = self.the_firm.the_os.ev_lst_all_delay_time
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self.op_ps_produced_num = self.the_firm.the_ps.ev_ary_produce_number # 当期产品生产数量
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self.op_ps_str_status = self.the_firm.the_os.ev_int_produce_type # 当期生产状态
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# self.op_is_current_product = self.model.the_firm.the_is.ev_ary_current_product
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#
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# self.op_is_current_material = self.model.the_firm.the_is.ev_ary_current_material
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#
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# self.op_is_trans_material= self.model.the_firm.the_is.ev_lst_trans_material
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self.op_ps_back_trans_material = self.model.the_firm.the_ps.ev_lst_backtrans_material
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self.record([att for att in self.__dict__.keys() if att.startswith('op_')]) # ?
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self.op_os_all_delay_time += self.the_firm.the_os.ev_ave_delay_time
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# pass
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def GA_run(inventory_bound=None):
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material = tuple(pd.read_excel("initial_material.xlsx").iloc[:, 0])
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s = tuple(tuple([i, j]) for i, j in
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zip(material, inventory_bound[: len(pd.read_excel("initial_material.xlsx").to_numpy())]))
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S = tuple(tuple([i, j]) for i, j in zip(material, inventory_bound[
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len(pd.read_excel("initial_material.xlsx").to_numpy()): len(
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pd.read_excel("initial_material.xlsx").to_numpy()) * 2]))
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dct_para = {
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'time': 300, # 进行总时间数
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'xv_ary_product_id': tuple(pd.read_excel("initial_product.xlsx").iloc[:, 0]), # 产成品id顺序
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'xv_ary_material_id': tuple(pd.read_excel("initial_material.xlsx").iloc[:, 0]), # 原材料id顺序
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'xv_product_num': len(pd.read_excel("initial_product.xlsx").to_numpy()), # 产成品个数
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'xv_material_num': len(pd.read_excel("initial_material.xlsx").to_numpy()), # 原材料个数
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'xv_ary_initial_product_num': tuple([tuple(x) for x in pd.read_excel("initial_product.xlsx").values]),
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# 初始产成品库存 23x2
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'xv_ary_initial_material_num': tuple([tuple(x) for x in pd.read_excel("initial_material.xlsx").values]),
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# 初始原材料库存 115x2
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'xv_ary_bom': tuple([tuple(x) for x in pd.read_excel("bom23.xlsx").values]), # bom表
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'xv_ary_plan': tuple([tuple(x) for x in pd.read_excel("plan.xlsx").values]), # plan表
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'xv_ary_s': s, # s
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'xv_ary_S': S, # S
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# 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
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}
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sample = ap.Sample(dct_para)
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exp = ap.Experiment(FMSEnv, sample, iterations=1, record=True)
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results = exp.run()
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return results['variables']['FMSEnv']['op_os_all_delay_time'][dct_para['time']] / 2
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if __name__ == '__main__':
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# dct_para = {
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# 'time': 60, # 进行总时间数
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# # 'xv_int_max_order': random.randint(30, 50),
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# # 'xv_dlv_product_para': tuple([(30, 100), (30, 50)]),
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# # 'xv_dlv_product_para': tuple([30,40,30,20]), # 读取生产率 np.read.
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# # 'xv_int_dlv_period_lam': 8.5,
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# # 'xv_int_create_order_lam': 2,
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# # 'xv_ary_price_product': tuple([0.3,0.2,0.5,1]),
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# # 'xv_ary_cost_material_per': tuple([0.1,0.1,0.2,0.4]),
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# # 'xv_ary_volume_material': tuple([1.0, 1.5]),
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# # 'xv_ary_volume_product': tuple([3.0, 5.0]),
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# # 'xv_array_lead_time': 2, # 读取原材料表格 np.read, 暂时不读 变量代表的含义
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# # 'xv_int_lead_time_c': 3,
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# # 'xv_int_lead_time_d': 1,
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# 'xv_ary_product_id': tuple(pd.read_excel("initial_product.xlsx").iloc[:, 0]), # 产成品id顺序
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# 'xv_ary_material_id': tuple(pd.read_excel("initial_material.xlsx").iloc[:, 0]), # 原材料id顺序
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# 'xv_product_num': len(pd.read_excel("initial_product.xlsx").to_numpy()), # 产成品个数
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# 'xv_material_num': len(pd.read_excel("initial_material.xlsx").to_numpy()), # 原材料个数
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# 'xv_ary_initial_product_num': tuple([tuple(x) for x in pd.read_excel("initial_product.xlsx").values]),
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# # 初始产成品库存 23x2
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# 'xv_ary_initial_material_num': tuple([tuple(x) for x in pd.read_excel("initial_material.xlsx").values]),
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# # 初始原材料库存 115x2
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# 'xv_ary_bom': tuple([tuple(x) for x in pd.read_excel("bom23.xlsx").values]), # bom表
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# 'xv_ary_plan': tuple([tuple(x) for x in pd.read_excel("plan.xlsx").values]), # plan表
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# 'xv_ary_s': tuple([tuple(x) for x in pd.read_excel("rawmaterial - s.xlsx").values]), # s
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# 'xv_ary_S': tuple([tuple(x) for x in pd.read_excel("rawmaterialS.xlsx").values]), # S
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# # 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
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# # 'xv_flt_initial_cash': 50000.0,
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# # 'dct_status_info': json.dumps({ #需要引入生产状态表
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# # "0": {"xv_flt_produce_rate": tuple([0.0, 0.0]),
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# # "xv_ary_mat_material": tuple([0.0, 0.0]),
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# # "xv_flt_broken_rate": 0,
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# # "xv_flt_run_cost": 0.0,
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# # "name": "wait"
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# # },
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# # "1": {"xv_flt_produce_rate": tuple([90.0, 0.0]),
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# # "xv_ary_mat_material": tuple([4.0, 1.0]),
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# # "xv_flt_broken_rate": 0.03,
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# # "xv_flt_run_cost": 40.0,
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# # "name": "produceA"
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# # },
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# # "2": {"xv_flt_produce_rate": tuple([0.0, 60.0]),
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# # "xv_ary_mat_material": tuple([1.5, 5.0]),
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# # "xv_flt_broken_rate": 0.05,
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# # "xv_flt_run_cost": 50.0,
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# # "name": "produceB"
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# # },
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# # "3": {"xv_flt_produce_rate": tuple([55.0, 30.0]),
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# # "xv_ary_mat_material": tuple([2.0, 1.5]),
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# # "xv_flt_broken_rate": 0.07,
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# # "xv_flt_run_cost": 60.0,
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# # "name": "produceAB"
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# # },
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# # "-1": {"xv_flt_produce_rate": 0.0,
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# # "xv_ary_mat_material": tuple([0.0, 0.0]),
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# # "xv_flt_broken_rate": 0.1,
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# # "xv_flt_run_cost": 100.0,
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# # "name": "failed"
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# # }
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# # })
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#
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# }
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# sample = ap.Sample(dct_para)
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#
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# exp = ap.Experiment(FMSEnv, sample, iterations=1, record=True)
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# results = exp.run()
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# print(results['variables']['FMSEnv']['op_os_all_delay_time'])
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# print(results['variables']['FMSEnv']['op_os_all_delay_time'][dct_para['time']])
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# results['variables']['FMSEnv'].to_excel(f"simulation-results-{datetime.today().strftime('%Y-%m-%d-%H-%M-%S')}.xlsx",
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# engine='openpyxl')
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material_num = len(pd.read_excel("initial_material.xlsx").to_numpy()) # 原材料个数
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GA = GeneticAlgorithm(function=GA_run, dim=material_num * 2, lb=[10 for i in range(material_num * 2)],
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ub=[100 for i in range(material_num * 2)], int_var=[i for i in range(material_num * 2)])
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GA.optimize()
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# print(result1, result2)
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