Full 2SP Server Farm Code

2SP_ServerFarm.py

import numpy as np
import gurobipy as gp
from gurobipy import GRB

## First Stage Constants
i = np.array([6,14,27])
s = np.array([1,5,3])
B = 1000
S = 100
## Second Stage Constants
K = 50 # Number of scenarios
d_xi = np.array([
    np.random.normal(70,10,K),
    np.random.normal(8,2,K),
    np.random.normal(15,4,K)
])
c = np.array([7,18,30])
p = np.array([1.0/K]*K) # Probability of a scenario happening

m = gp.Model("2SP")
m.ModelSense = GRB.MINIMIZE
m.setParam('OutputFlag', 0)  # Telling gurobi to not be verbose
m.params.logtoconsole=0
# VARIABLES 
## First Stage Decision variable
x = m.addMVar((3,), vtype = GRB.INTEGER, name = "x")

## 2nd stage decision for each potential scenario
y = m.addMVar((3,K), vtype = GRB.INTEGER, name = "y")
### Gurobi automatically sets bounds to (0, inf)

# OBJECTIVE 
m.setObjective(i @ x + gp.quicksum(c @ y[:,k] * p[k] for k in range(K)))

# CONSTRAINTS 
## Installation Budget
m.addConstr(i @ x <= B)

## Space
m.addConstr(s @ x <= S)

## Meet demand in each scenario
m.addConstrs((
    x + y[:,k] >= d_xi[:,k] for k in range(K)
))

# SOLVING
m.optimize()
result = x.x

print(f"Our Server farm will cost ${np.round(m.ObjVal,2)}. We should buy {result[0]} CPUs, {result[1]} GPUs, and {result[2]} TPUs ")

I found it impressive