112 lines
6.0 KiB
Python
112 lines
6.0 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_ave_delay_time: float
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xv_ary_plan: np.ndarray
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ev_changed_product: np.ndarray
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xv_product_num: int
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xv_ary_product: np.ndarray
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# ev_ary_dlv_product: np.ndarray??????
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def setup(self, xv_product_num, xv_ary_plan, xv_ary_product_id):
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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((xv_product_num,))
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self.ev_int_produce_type = 0 # 生产状态（方案）
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self.ev_ary_product_to_produce = np.zeros((xv_product_num,))
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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((xv_product_num,))
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self.xv_ary_plan = xv_ary_plan
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self.xv_product_num = xv_product_num
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self.xv_ary_product = xv_ary_product_id
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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) # 按预期交付时间排序
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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((self.xv_product_num,)) # 产品需求总和
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for order in self.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 = self.ev_ary_to_dlv - np.array([float(x) for x in self.model.the_firm.the_is.ev_ary_current_product[:, 1]])
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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]
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sorted_data = self.xv_ary_product[sorted_indices] # gap从大到小的产品id
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gap_sorted = self.ev_ary_product_to_produce[sorted_indices]
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# print(gap_sorted)
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if gap_sorted[0] > 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][:, 0] # 库存从小到大的产品id
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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
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sorted_indices = np.argsort(option[:, 3])[::-1]
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sorted_data = option[sorted_indices]
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self.ev_int_produce_type = sorted_data[0, 0]
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# print(option)
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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_ave_delay_time = 0
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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
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# print(ev_ary_current_product)
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# print(self.a_lst_order[0].ev_ary_dlv_product)
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# if len(self.a_lst_order) > 1:
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# print(self.a_lst_order[1].ev_ary_dlv_product)
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for order in self.a_lst_order:
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# print(order.xv_dlv_t[0], self.model.t)
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if order.xv_dlv_t[0] == 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(self.xv_product_num):
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if order.ev_ary_dlv_product[i] <= self.ev_changed_product[i]:
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self.ev_changed_product[i] -= order.ev_ary_dlv_product[i]
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order.ev_ary_dlv_product[i] = 0
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else:
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order.ev_is_delivered = False
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elif order.xv_dlv_t[0] < 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(self.xv_product_num):
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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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self.ev_changed_product = self.ev_changed_product - order.ev_ary_dlv_product
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order.ev_ary_dlv_product = np.zeros((self.xv_product_num,))
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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[0]
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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 |