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Improved
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@Ervin66
Ervin66 authored Apr 14, 2021
1 parent d2cb52a commit 5f3216c
Showing 1 changed file with 38 additions and 37 deletions.
75 changes: 38 additions & 37 deletions Model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ def __init__(self, data_path, product_col, time_col, loc_col, qty_col):
loc_col], dropna=False).sum() # aggregate the order per week, product group and location
print(self.data)
print(self.data.groupby(product_col).agg(["size", "mean", "std"]))
self.data.to_csv("demand_input.csv")
self.inv_model = pulp.LpProblem("Inventory_Optimization",
LpMinimize) # creates an object of an LpProblem (PuLP) to which we will assign variables, constraints and objective function

Expand Down Expand Up @@ -103,31 +104,40 @@ def define_variables(self):
self.factory_id,
self.time_id),
lowBound=0)
self.slack = pulp.LpVariable.dicts("slack",
(self.prod_id,
self.loc_id,
self.time_id),
lowBound=0)

def define_objective(self):
''' Defines the objective funciton
'''
holding_costs = lpSum((self.inv_level[i][w][t] * self.loc_data.loc[w, "holding costs"]
holding_costs = lpSum((self.inv_level[i][w][t] * self.loc_data.loc[w, "Hold. Costs"]
for i in self.prod_id
for w in self.loc_id
for t in self.time_id))
# ordering_costs = lpSum([self.order_dec[i][w][t] * self.order_cost
# for i in self.prod_id
# for w in self.loc_id
# for t in self.time_id])

trans_costs_echelon1 = lpSum([self.FTL[o][d][t] * self.factory_dist.loc[o, d]
for o in self.factory_id
for d in self.dc_id
for i in self.prod_id
for t in self.time_id])
trans_costs_echelon2 = lpSum((self.FTL[o][d][t] * self.dc_dist.loc[o, d]
for o in self.dc_id
for d in self.wh_id
for i in self.prod_id
for t in self.time_id))
production_costs = lpSum((self.production[i][f][t] * self.loc_data.loc[f, "Prod. Costs"])
for i in self.prod_id
for f in self.factory_id
for t in self.time_id)

# slack_costs = lpSum((self.slack[i][w][t] * 50000
# for i in self.prod_id
# for w in self.loc_id
# for t in self.time_id))

self.inv_model += holding_costs + \
trans_costs_echelon1 + trans_costs_echelon2
trans_costs_echelon1 + trans_costs_echelon2 # + production_costs # + slack_costs

def define_constraints(self):
'''Defines the constraints to be added to the model
Expand All @@ -141,37 +151,32 @@ def define_constraints(self):
"Pallets", "std")] * 2.33 * self.weighted_avg(prevt, w, i)

else:
self.inv_model += lpSum(self.safety_stock[i][w][t]) == self.demand_stats.loc[i, (
"Pallets", "std")] * 2.33 * self.weighted_avg(t, w, i)
try:
self.inv_model += lpSum(self.safety_stock[i][w][t]) == self.demand_stats.loc[i, (
"Pallets", "std")] * 2.33 * (1 / len(self.loc_id))

self.inv_model += lpSum(
self.data.loc[(t, i, w)] + self.safety_stock[i][w][t]) == self.inv_level[i][w][t]

except KeyError:
continue

# self.inv_model += lpSum((self.inv_level[i][w][t] + self.safety_stock[i][w][t]
# for i in self.prod_id)) <= self.loc_data.loc[w, "Capacity"]
self.inv_model += lpSum((self.inv_level[i][w][t] + self.safety_stock[i][w][t]
for i in self.prod_id)) <= self.loc_data.loc[w, "Hold. Cap."]

for f in self.factory_id:
for ind, t in enumerate(self.time_id):
for i in self.prod_id:
self.inv_model += lpSum((self.production[i][f][t]
for i in self.prod_id)) <= self.loc_data.loc[f, "Prod. Cap."]
try:
if ind != 0:
prevt = self.time_id[ind - 1]
self.inv_model += lpSum(self.inv_level[i][f][prevt] -
self.data.loc[(t, i, f)]
- self.shipment[f][dc][i][t]
+ self.production[i][f][t]
+ self.safety_stock[i][f][t]
for dc in self.dc_id) == self.inv_level[i][f][t]
# else:
# self.inv_model += lpSum(self.data.loc[(t, i, f)]
# - self.shipment[f][dc][i][t]
# + self.production[i][f][t]
# + self.safety_stock[i][f][t]
# for dc in self.dc_id) == self.inv_level[i][f][t]
for dc in self.dc_id) >= self.inv_level[i][f][t] + self.safety_stock[i][f][t]

except KeyError:
continue

Expand All @@ -185,18 +190,12 @@ def define_constraints(self):
self.inv_level[i][d][prevt] -
self.data.loc[(t, i, d)]
- self.shipment[d][wh][i][t]
+ self.safety_stock[i][d][t]
for o in self.factory_id
for wh in self.wh_id) == self.inv_level[i][d][t]
# else:
# self.inv_model += lpSum(self.shipment[o][d][i][t] +
# self.data.loc[(t, i, d)]
# - self.shipment[d][wh][i][t]
# + self.safety_stock[i][d][t]
# for o in self.factory_id
# for wh in self.wh_id) == self.inv_level[i][d][t]
for wh in self.wh_id) >= self.inv_level[i][d][t] + self.safety_stock[i][d][t]

except KeyError:
continue

for d in self.wh_id:
for ind, t in enumerate(self.time_id):
for i in self.prod_id:
Expand All @@ -206,13 +205,8 @@ def define_constraints(self):
self.inv_model += lpSum(self.shipment[o][d][i][prevt] +
self.inv_level[i][d][prevt] -
self.data.loc[(t, i, d)]
+ self.safety_stock[i][d][t]
for o in self.dc_id) == self.inv_level[i][d][t]
# else:
# self.inv_model += lpSum(self.shipment[o][d][i][t] +
# self.data.loc[(t, i, d)]
# + self.safety_stock[i][d][t]
# for o in self.dc_id) == self.inv_level[i][d][t]
for o in self.dc_id) >= self.inv_level[i][d][t] + self.safety_stock[i][d][t]

except KeyError:
continue

Expand Down Expand Up @@ -286,6 +280,11 @@ def build_model(self):
prod_ind=self.prod_id,
variable=self.production,
filename="Production")
# self.export_vars_3d(time_ind=self.time_id,
# wh_ind=self.loc_id,
# prod_ind=self.prod_id,
# variable=self.slack,
# filename="Slack")

def export_vars_3d(self, time_ind, wh_ind, prod_ind, variable, filename):
''' Formats the solution and returns it embedded in a pandas dataframe
Expand Down Expand Up @@ -343,4 +342,6 @@ def export_vars_4d(self, time_ind, origin_ind, destination_ind, prod_ind, variab
- automate time index
- automate removal in distance matrix
- improve initial SS allocation
- improve export functions (make indices argmuent list like)
-
'''

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