Main.py

import numpy as np
from pyswarm import pso
# Group 14 - Logistical optomisation problem



# Step 1. Import delivery order list from CSV into an array





# Step 2: Define the objective function
# We will use distance as the main factor for lowering emission output
def weight(x, *args):
    H, d, t = x
    B, rho, E, P = args
    return rho*2*np.pi*d*t*np.sqrt((B/2)**2 + H**2)

# Step 3: Define contraints
# For our project the constraint functions will be volume of orders and weight of orders per truck
def yield_stress(x, *args):
    H, d, t = x
    B, rho, E, P = args
    return (P*np.sqrt((B/2)**2 + H**2))/(2*t*np.pi*d*H)

def buckling_stress(x, *args):
    H, d, t = x
    B, rho, E, P = args
    return (np.pi**2*E*(d**2 + t**2))/(8*((B/2)**2 + H**2))

def deflection(x, *args):
    H, d, t = x
    B, rho, E, P = args
    return (P*np.sqrt((B/2)**2 + H**2)**3)/(2*t*np.pi*d*H**2*E)

def constraints(x, *args):
    strs = yield_stress(x, *args)
    buck = buckling_stress(x, *args)
    defl = deflection(x, *args)
    return [100 - strs, buck - strs, 0.25 - defl]

# Define the other parameters
B = 60  # inches
rho = 0.3  # lb/in^3
E = 30000  # kpsi (1000-psi)
P = 66  # kip (1000-lbs, force)
args = (B, rho, E, P)

# Define the lower and upper bounds for H, d, t, respectively
lb = [10, 1, 0.01]
ub = [30, 3, 0.25]

xopt, fopt = pso(weight, lb, ub, f_ieqcons=constraints, args=args)
print(xopt)
print(fopt)