166 lines
6.5 KiB
Python
166 lines
6.5 KiB
Python
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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 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
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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: list
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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_ps_str_status: str
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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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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'])
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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.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, xv_dlv_product_para=self.xv_dlv_product_para,
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leadtime=max(self.the_firm.xv_ary_lead_time))
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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()
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self.the_firm.the_os.accept_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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print(f"running the {self.t} step")
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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)
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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
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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_produced_num
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self.op_ps_str_status = self.the_firm.the_ps.ev_str_status
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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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pass
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if __name__ == '__main__':
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dct_para = {
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'time': 60,
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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
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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_initial_product_num': pd.read_excel("initial_product.xlsx").to_numpy(),
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'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
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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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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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# 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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