Python3实现单级库存仿真，single echelon inventory assessment

参考代码的来源：
https://github.com/anshul-musing/single-echelon-inventory-assessment/blob/master/src/simpy_3.0/simLostSales.py

src/simpy_3.0/simBackorder.py

这段代码主要模拟单级供应链，所考虑的库存参数为在途库存、库存水平、服务水平。
假设这个系统采用的是“一旦库存水平低于再订货点（固定），管理者立即下订单（固定）”的订货策略。
假设当前未被满足的订单允许被后期的补货满足，
基于订单有多晚被满足 ，计算服务水平。
假设需求服从正态分布、提前期服从均匀分布。

"""This module simulates a single-echelon supply chain
and calculates inventory profile (along with associated inventory
parameters such as on-hand, inventory position, service level, etc.)
across time

The system follows a reorder point-reorder quantity policy
If inventory position <= ROP, an order of a fixed reorder
quantity (ROQ) is placed by the facility

It is assumed that any unfulfilled order is backordered
and is fulfilled whenever the material is available in the
inventory.  The service level is estimated based on how
late the order was fulfilled

Demand is assumed to be Normally distributed
Lead time is assumed to follow a uniform distribution
"""

__author__ = 'Anshul Agarwal'


import simpy
import numpy as np

# Stocking facility class
class stockingFacility(object): ## ?? why we need to in herit 'object'?

    # initialize the new facility object
    def __init__(self, env, initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime):
        self.env = env
        self.on_hand_inventory = initialInv
        self.inventory_position = initialInv
        self.ROP = ROP # inventory position
        self.ROQ = ROQ # fixed order quantity
        self.meanDemand = meanDemand
        self.demandStdDev = demandStdDev
        self.minLeadTime = minLeadTime
        self.maxLeadTime = maxLeadTime
        self.totalDemand = 0.0
        self.totalBackOrder = 0.0
        self.totalLateSales = 0.0
        self.serviceLevel = 0.0
        env.process(self.runOperation())

    # main subroutine for facility operation
    # it records all stocking metrics for the facility
    def runOperation(self):
        while True:
            yield self.env.timeout(1.0)
            # demand newly generated
            demand = float(np.random.normal(self.meanDemand, self.demandStdDev, 1))
            self.totalDemand += demand
            # shipment 是该仓库送出的量，而self.ROQ是该仓库的补货量
            shipment = min(demand + self.totalBackOrder, self.on_hand_inventory) # the amount of goods available to send
            self.on_hand_inventory -= shipment # send the shipment to some retailer
            self.inventory_position -= shipment
            backorder = demand - shipment # the amount of demand unmet temporarily
            self.totalBackOrder += backorder
            self.totalLateSales += max(0.0, backorder)
            # if the current inventory position is less than ROP, then place an order
            if self.inventory_position <= 1.01 * self.ROP:  # multiply by 1.01 to avoid rounding issues
                self.env.process(self.ship(self.ROQ))
                # why we revise 'self.on_hand_inv' in the method 'ship', and revise 'self.inv_position' outside 'ship'
                self.inventory_position += self.ROQ

    # subroutine for a new order placed by the facility
    def ship(self, orderQty):
        # recall that we assume the lead time follows an uniform distribution
        leadTime = int(np.random.uniform(self.minLeadTime, self.maxLeadTime, 1))
        yield self.env.timeout(leadTime)  # wait for the lead time before delivering
        # now 'orderQty' goods is received
        self.on_hand_inventory += orderQty


# Simulation module
def simulateNetwork(seedinit, initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime):
    env = simpy.Environment()  # initialize SimPy simulation instance
    np.random.seed(seedinit)
    s = stockingFacility(env, initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime)
    env.run(until=365)  # simulate for 1 year
    s.serviceLevel = 1 - s.totalLateSales / s.totalDemand # 服务水平的定义：那些被及时满足的需求的占比
    return s


######## Main statements to call simulation ########
meanDemand = 500.0
demandStdDev = 100.0
minLeadTime = 7
maxLeadTime = 13
CS = 5000.0
ROQ = 6000.0
ROP = max(CS,ROQ)
initialInv = ROP + ROQ

# Simulate
replications = 100
sL = []
for i in range(replications):
    nodes = simulateNetwork(i,initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime)
    sL.append(nodes.serviceLevel)

sLevel = np.array(sL)
print("Avg. service level: " + str(np.mean(sLevel)))
print("Service level standard deviation: " + str(np.std(sLevel)))

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src/simpy_3.0/simLostSales.py

不同于上一小节的地方在于，这里不允许回购，而是允许发生销售损失（lost sales）。
因此，在代码实现方面也会有微妙的差别，具体如下，

1. 在类stockingFacility中数据self.totalShipped用于记录从这个仓库发出了多少货；
2. 在类stockingFacility的方法runOperation中，当前从该仓库的送出量shipment的计算方式不再考虑backorder；
3. 在函数simulateNetwork中，计算服务水平（从该仓库的送出量占总需求量的比例）。

"""This module simulates a single-echelon supply chain
and calculates inventory profile (along with associated inventory
parameters such as on-hand, inventory position, service level, etc.)
across time

The system follows a reorder point-reorder quantity policy
If inventory position <= ROP, an order of a fixed reorder
quantity (ROQ) is placed by the facility

It is assumed that any unfulfilled order is lost
The service level is estimated based on how much
of the demand was fulfilled

Demand is assumed to be Normally distributed
Lead time is assumed to follow a uniform distribution
"""

__author__ = 'Anshul Agarwal'


import simpy
import numpy as np

# Stocking facility class
class stockingFacility(object):

    # initialize the new facility object
    def __init__(self, env, initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime):
        self.env = env
        self.on_hand_inventory = initialInv
        self.inventory_position = initialInv
        self.ROP = ROP
        self.ROQ = ROQ
        self.meanDemand = meanDemand
        self.demandStdDev = demandStdDev
        self.minLeadTime = minLeadTime
        self.maxLeadTime = maxLeadTime
        self.totalDemand = 0.0
        self.totalShipped = 0.0 # !!
        self.serviceLevel = 0.0
        env.process(self.runOperation())

    # main subroutine for facility operation
    # it records all stocking metrics for the facility
    def runOperation(self):
        while True:
            yield self.env.timeout(1.0)
            demand = float(np.random.normal(self.meanDemand, self.demandStdDev, 1))
            self.totalDemand += demand
            shipment = min(demand, self.on_hand_inventory) # !!
            self.totalShipped += shipment
            self.on_hand_inventory -= shipment
            self.inventory_position -= shipment
            if self.inventory_position <= 1.01 * self.ROP:  # multiply by 1.01 to avoid rounding issues
                self.env.process(self.ship(self.ROQ))
                self.inventory_position += self.ROQ

    # subroutine for a new order placed by the facility
    def ship(self, orderQty):
        leadTime = int(np.random.uniform(self.minLeadTime, self.maxLeadTime, 1))
        yield self.env.timeout(leadTime)  # wait for the lead time before delivering
        self.on_hand_inventory += orderQty


# Simulation module
def simulateNetwork(seedinit, initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime):
    env = simpy.Environment()  # initialize SimPy simulation instance
    np.random.seed(seedinit)
    s = stockingFacility(env, initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime)
    env.run(until=365)  # simulate for 1 year
    s.serviceLevel = s.totalShipped / s.totalDemand # !!
    return s


######## Main statements to call simulation ########
meanDemand = 500.0
demandStdDev = 100.0
minLeadTime = 7
maxLeadTime = 13
CS = 5000.0
ROQ = 6000.0
ROP = max(CS,ROQ)
initialInv = ROP + ROQ

# Simulate
replications = 100
sL = []
for i in range(replications):
    nodes = simulateNetwork(i,initialInv, ROP, ROQ, meanDemand, demandStdDev, minLeadTime, maxLeadTime)
    sL.append(nodes.serviceLevel)

sLevel = np.array(sL)
print("Avg. service level: " + str(np.mean(sLevel)))
print("Service level standard deviation: " + str(np.std(sLevel)))

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