HTSim/Environment.py

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(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': 60,  # 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')