A compilation of programs for Supply Chain Analytics
- Clone this repo
- Install requirements
- Run the script
- Done! 🎉
MPS.py is a compilation of algorithms for Master Production Schedule:
- One Time run
- Chase (Lot for Lot)
- Fixed Order Quantity
- Periodic Order Quantity
- Silver-Meal Heuristic
- Wagner-Whitin Optimization
$ git clone https://github.com/cydessole/Supply-Chain-Management.git- Python 3
- NumPy
- Pandas
$ pip install -r requirements.txtYou can either run the script as a standalone program or as a module
Master Production Schedule
positional arguments:
fcost The fixed cost
hcost The holding cost
demands Demands during each period
optional arguments:
-h, --help show this help message and exit
-v, --verbose Print information during process
-e, --excel Write Final DataFrame to excel
$ python MPS.py 500 1.5 150 50 150 200 50 250 50 200 50 100 50 300Master Production Schedule :
1: One time Run
2: Chase
3: Fixed Order Quantity
4: Periodic Order Quantity
5: Silver-Meal
6: Wagner-Whitin
Choose the method number you want to run : 3
Quantity to order: 300
Start inventory: 0
========================Input========================
setup cost: 500.0
holding cost: 1.5
demands: [150, 50, 150, 200, 50, 250, 50, 200, 50, 100, 50, 300]
Start inventory: 0
Quantity: 300.0
========================Final Cost========================
total_holding_cost: 1800.0
total_setup: 3000.0
total_cost: 4800.0
$ python MPS.py 500 1.5 150 50 150 200 50 250 50 200 50 100 50 300 -v -eMaster Production Schedule :
1: One time Run
2: Chase
3: Fixed Order Quantity
4: Periodic Order Quantity
5: Silver-Meal
6: Wagner-Whitin
Choose the method number you want to run : 5
Start inventory: 0
========================Input========================
setup cost: 500.0
holding cost: 1.5
Start inventory: 0
demands: [150, 50, 150, 200, 50, 250, 50, 200, 50, 100, 50, 300]
========================Step 1========================
period 1
Number of period to produce : 2
cost [500.0, 287.5]
Number of period to produce : 3
cost : [500.0, 287.5, 341.67]
production [200, 0]
inventory [50, 0]
Best number of period to produce : 2
Minimum cost: 287.5
========================Step 2========================
period 3
Number of period to produce : 2
cost [500.0, 400.0]
Number of period to produce : 3
cost : [500.0, 400.0, 316.67]
Number of period to produce : 4
cost : [500.0, 400.0, 316.67, 518.75]
production [200, 0, 400, 0, 0]
inventory [50, 0, 250, 50, 0]
Best number of period to produce : 3
Minimum cost: 316.6666666666667
========================Step 3========================
period 6
Number of period to produce : 2
cost [500.0, 287.5]
Number of period to produce : 3
cost : [500.0, 287.5, 391.67]
production [200, 0, 400, 0, 0, 300, 0]
inventory [50, 0, 250, 50, 0, 50, 0]
Best number of period to produce : 2
Minimum cost: 287.5
========================Step 4========================
period 8
Number of period to produce : 2
cost [500.0, 287.5]
Number of period to produce : 3
cost : [500.0, 287.5, 291.67]
production [200, 0, 400, 0, 0, 300, 0, 250, 0]
inventory [50, 0, 250, 50, 0, 50, 0, 50, 0]
Best number of period to produce : 2
Minimum cost: 287.5
========================Step 5========================
period 10
Number of period to produce : 2
cost [500.0, 287.5]
Number of period to produce : 3
cost : [500.0, 287.5, 491.67]
production [200, 0, 400, 0, 0, 300, 0, 250, 0, 150, 0]
inventory [50, 0, 250, 50, 0, 50, 0, 50, 0, 50, 0]
Best number of period to produce : 2
Minimum cost: 287.5
========================Step 6========================
cost [500.0]
period 12
production [200, 0, 400, 0, 0, 300, 0, 250, 0, 150, 0, 300]
inventory [50, 0, 250, 50, 0, 50, 0, 50, 0, 50, 0, 0]
========================Final Cost========================
total_holding_cost: 750.0
total_setup: 3000.0
total_cost: 3750.0
demand production IOH Setup_cost Holding_cost total_cost
period
1 150 200 50 500.0 75.0 575.0
2 50 0 0 0.0 0.0 0.0
3 150 400 250 500.0 375.0 875.0
4 200 0 50 0.0 75.0 75.0
5 50 0 0 0.0 0.0 0.0
6 250 300 50 500.0 75.0 575.0
7 50 0 0 0.0 0.0 0.0
8 200 250 50 500.0 75.0 575.0
9 50 0 0 0.0 0.0 0.0
10 100 150 50 500.0 75.0 575.0
11 50 0 0 0.0 0.0 0.0
12 300 300 0 500.0 0.0 500.0
Excel saved
from MPS import *
import numpy as np
import pandas as pd
setup_cost=500.0
holding_cost= 1.5
demands= [150, 50, 150, 200, 50, 250, 50, 200, 50, 100, 50, 300]
inventory,production,total_cost=wagnerWhitin(setup_cost,holding_cost,demands,verbose=False,excel=False)
print()
strategy=pd.DataFrame(data={'period':range(1,len(demands)+1),
'demand':demands,
'production':production,
'IOH':inventory})
strategy=strategy.set_index('period')
strategy['Setup_cost']=np.where(strategy['production']>0,setup_cost,0)
strategy['Holding_cost']=strategy['IOH']*holding_cost
strategy['total_cost']=strategy['Setup_cost']+strategy['Holding_cost']
print(strategy)
print('The total cost: ',total_cost)========================Input========================
setup cost: 500.0
holding cost: 1.5
demands: [150, 50, 150, 200, 50, 250, 50, 200, 50, 100, 50, 300]
========================Final Cost========================
total_holding_cost: 1200.0
total_setup: 2500.0
total_cost: 3700.0
demand production IOH Setup_cost Holding_cost total_cost
period
1 150 200 50 500.0 75.0 575.0
2 50 0 0 0.0 0.0 0.0
3 150 400 250 500.0 375.0 875.0
4 200 0 50 0.0 75.0 75.0
5 50 0 0 0.0 0.0 0.0
6 250 300 50 500.0 75.0 575.0
7 50 0 0 0.0 0.0 0.0
8 200 400 200 500.0 300.0 800.0
9 50 0 150 0.0 225.0 225.0
10 100 0 50 0.0 75.0 75.0
11 50 0 0 0.0 0.0 0.0
12 300 300 0 500.0 0.0 500.0
The total cost: 3700.0Other algorithms will be added to this repository for SCM.
For example, Dijkstra, Clarke-Wright for Supply Chain Design.