HTSim/OrderSystem.py

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_ave_delay_time: float
    xv_ary_plan: np.ndarray
    ev_changed_product: np.ndarray


    xv_product_num: int
    xv_ary_product: np.ndarray
    # ev_ary_dlv_product: np.ndarray??????

    def setup(self, xv_product_num,  xv_ary_plan, xv_ary_product_id):
        # Create a list of order
        self.a_lst_order = ap.AgentList(self, [])  #
        self.ev_ary_to_dlv = np.zeros((xv_product_num,))
        self.ev_int_produce_type = 0  # 生产状态（方案）
        self.ev_ary_product_to_produce = np.zeros((xv_product_num,))
        self.ev_ave_delay_time = 0
        # self.ev_ary_dlv_product = np.zeros((23,))  ????
        self.ev_changed_product = np.zeros((xv_product_num,))
        self.xv_ary_plan = xv_ary_plan
        self.xv_product_num = xv_product_num
        self.xv_ary_product = xv_ary_product_id

    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)  # 按预期交付时间排序

    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((self.xv_product_num,))  # 产品需求总和
        for order in self.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 = self.ev_ary_to_dlv - np.array([float(x) for x in self.model.the_firm.the_is.ev_ary_current_product[:, 1]])
        # 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]
        sorted_data = self.xv_ary_product[sorted_indices]  # gap从大到小的产品id
        gap_sorted = self.ev_ary_product_to_produce[sorted_indices]
        # print(gap_sorted)
        if gap_sorted[0] > 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][:, 0]  # 库存从小到大的产品id

        option = self.xv_ary_plan[self.xv_ary_plan[:, 1] == sorted_data[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(option[:, 3])[::-1]
        sorted_data = option[sorted_indices]
        self.ev_int_produce_type = sorted_data[0, 0]
        # print(option)
        # 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_ave_delay_time = 0
        self.ev_changed_product = np.array([float(x) for x in ev_ary_current_product[:, 1]])  # 23x1 ndarray 存储本次库存的改变
        # print(ev_ary_current_product)
        # print(self.a_lst_order[0].ev_ary_dlv_product)
        # if len(self.a_lst_order) > 1:
        #     print(self.a_lst_order[1].ev_ary_dlv_product)
        for order in self.a_lst_order:
            # print(order.xv_dlv_t[0], self.model.t)
            if order.xv_dlv_t[0] == self.model.t:  # 第一次交付
                # Check and make shipment
                order.ev_is_delivered = True
                for i in range(self.xv_product_num):
                    if order.ev_ary_dlv_product[i] <= self.ev_changed_product[i]:
                        self.ev_changed_product[i] -= order.ev_ary_dlv_product[i]
                        order.ev_ary_dlv_product[i] = 0
                    else:
                        order.ev_is_delivered = False
            elif order.xv_dlv_t[0] < self.model.t and order.ev_is_delivered == False:  # 第二次交付
                order.ev_is_delivered = True
                for i in range(self.xv_product_num):
                    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)
                    self.ev_changed_product = self.ev_changed_product - order.ev_ary_dlv_product
                    order.ev_ary_dlv_product = np.zeros((self.xv_product_num,))

                    order.ev_actual_dlv_t = self.model.t
                    order.ev_int_delay_time = order.ev_actual_dlv_t - order.xv_dlv_t[0]

                    self.ev_ave_delay_time += order.ev_int_delay_time * delay_num / 10000

        # return self.ev_changed_product, self.ev_ave_delay_time