104 lines
5.4 KiB
Python
104 lines
5.4 KiB
Python
import agentpy as ap
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import numpy as np
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from Order import Order
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import pandas as pd
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# 输出一个生产状态指令，一个交付情况，一个延期时间计算
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class OrderSystem(ap.Agent):
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a_lst_order: ap.AgentList[Order]
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ev_int_produce_type: int
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ev_ary_to_dlv: np.ndarray # 当前order list内所有产品的综合
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ev_ary_product_to_produce: np.ndarray # 用于计算 production gap
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ev_lst_all_delay_time: list
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ev_ave_delay_time: float
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xv_plan_excel: np.ndarray
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ev_lst_flag: list
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ev_changed_product: np.ndarray
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# ev_ary_dlv_product: np.ndarray??????
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def setup(self):
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# Create a list of order
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self.a_lst_order = ap.AgentList(self, [])
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self.ev_ary_to_dlv = np.zeros((23, 1))
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self.ev_int_produce_type = 0
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self.ev_ary_product_to_produce = np.zeros((23,))
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self.ev_lst_all_delay_time = []
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self.ev_ave_delay_time = 0
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# self.ev_ary_dlv_product = np.zeros((23,)) ????
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self.ev_changed_product = np.zeros((23, ))
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self.xv_plan_excel = pd.read_excel("plan.xlsx").to_numpy()
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pass
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def accept_order(self, new_order):
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# Determine whether the order is received and all are currently received
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if new_order is not None:
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new_order.ev_is_accepted = True
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self.a_lst_order.append(new_order)
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def rank_order(self):
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# Sort order
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self.a_lst_order = self.a_lst_order.sort('xv_dlv_t', reverse=False)
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def produce_status(self):
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# 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
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self.ev_ary_to_dlv = np.zeros((23,))
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for order in self.model.the_firm.the_os.a_lst_order:
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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
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self.ev_ary_product_to_produce[:, 1] = self.ev_ary_to_dlv - self.model.the_firm.the_is.ev_ary_current_product
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# self.ev_ary_product_to_produce = self.model.the_firm.the_is.ev_ary_current_product - self.ev_ary_to_dlv
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# self.ev_ary_product_to_produce > 0:
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# 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
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# 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
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sorted_indices = np.argsort(self.ev_ary_product_to_produce[:, 1])[::-1]
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sorted_data = self.ev_ary_product_to_produce[sorted_indices]
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if sorted_data[0, 1] > 0: # 判断是否存在库存不足
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pass
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else:
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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
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sorted_data = self.model.the_firm.the_is.ev_ary_current_product[sorted_indices]
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a = self.xv_plan_excel[self.xv_plan_excel[:, 1] == sorted_data[0, 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
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sorted_indices = np.argsort(a[:, 3])[::-1]
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sorted_data = a[sorted_indices]
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# return sorted_data[0, 0]
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self.ev_int_produce_type = sorted_data[0, 0]
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return self.ev_int_produce_type
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def do_shipment(self,ev_ary_current_product):
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# Make shipments based on ranked order list
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# 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
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# 就继续在列表中存在
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# 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
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# 需要更新 order.ev_ary_dlv_product
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# Make shipments based on ranked order list
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self.ev_lst_all_delay_time = []
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self.ev_ave_delay_time = 0
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self.ev_changed_product = ev_ary_current_product # 23x1 ndarray 存储本次库存的改变
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for order in self.a_lst_order:
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if order.xv_dlv_t == self.model.t: # 第一次交付
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# Check and make shipment
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order.ev_is_delivered = True
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for i in range(len(order.ev_ary_dlv_product)):
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if order.ev_ary_dlv_product[i] <= self.ev_changed_product[i]:
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order.ev_ary_dlv_product[i] = 0
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self.ev_changed_product[i] -= order.ev_ary_dlv_product[i]
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else:
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order.ev_is_delivered = False
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elif order.xv_dlv_t < self.model.t and order.ev_is_delivered == False: # 第二次交付
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order.ev_is_delivered = True
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for i in range(len(order.ev_ary_dlv_product)):
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if order.ev_ary_dlv_product[i] > self.ev_changed_product[i]: # 先判断能不能一次性交付
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order.ev_is_delivered = False
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if order.ev_is_delivered: # 如果一次性交付
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delay_num = np.sum(order.ev_ary_dlv_product)
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order.ev_ary_dlv_product = np.zeros((23,))
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self.ev_changed_product = self.ev_changed_product - order.ev_ary_dlv_product
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order.ev_actual_dlv_t = self.model.t
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order.ev_int_delay_time = order.ev_actual_dlv_t - order.xv_dlv_t
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self.ev_ave_delay_time += order.ev_int_delay_time * delay_num / 10000
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return self.ev_changed_product, self.ev_ave_delay_time
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