181 lines
7.7 KiB
Python
181 lines
7.7 KiB
Python
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 # Firm类
|
||
|
||
# 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_os_int_status: int
|
||
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
|
||
op_os_n_total_order: 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']) # 停止接单时间
|
||
# 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.op_os_n_total_order = 0
|
||
self.op_os_int_status = 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)
|
||
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() # 接收创建的订单
|
||
self.the_firm.the_os.accept_order(new_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")
|
||
print("当期延误时长为:{}".format(self.the_firm.the_os.ev_ave_delay_time))
|
||
|
||
# Record data after each simulation
|
||
def update(self): # ?
|
||
self.op_os_n_total_order = len(self.the_firm.the_os.a_lst_order) # 订单个数
|
||
# 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 # 当期及之前未满足需求总和
|
||
# 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_produce_number # 当期产品生产数量
|
||
self.op_ps_str_status = self.the_firm.the_os.ev_int_produce_type # 当期生产状态
|
||
|
||
# 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': 300, # 进行总时间数
|
||
# '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 变量代表的含义
|
||
# 'xv_int_lead_time_c': 3,
|
||
# 'xv_int_lead_time_d': 1,
|
||
'xv_ary_product_id': tuple(pd.read_excel("initial_product.xlsx").iloc[:, 0]), # 产成品id顺序
|
||
'xv_ary_material_id': tuple(pd.read_excel("initial_material.xlsx").iloc[:, 0]), # 原材料id顺序
|
||
'xv_product_num': len(pd.read_excel("initial_product.xlsx").to_numpy()), # 产成品个数
|
||
'xv_material_num': len(pd.read_excel("initial_material.xlsx").to_numpy()), # 原材料个数
|
||
'xv_ary_initial_product_num': tuple([tuple(x) for x in pd.read_excel("initial_product.xlsx").values]),
|
||
# 初始产成品库存 23x2
|
||
'xv_ary_initial_material_num': tuple([tuple(x) for x in pd.read_excel("initial_material.xlsx").values]),
|
||
# 初始原材料库存 115x2
|
||
'xv_ary_bom': tuple([tuple(x) for x in pd.read_excel("bom23.xlsx").values]), # bom表
|
||
'xv_ary_plan': tuple([tuple(x) for x in pd.read_excel("plan.xlsx").values]), # plan表
|
||
'xv_ary_s': tuple([tuple(x) for x in pd.read_excel("rawmaterial - s.xlsx").values]), # s
|
||
'xv_ary_S': tuple([tuple(x) for x in pd.read_excel("rawmaterialS.xlsx").values]), # S
|
||
# 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')
|