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# Display the result if Optimal solution is found
if status == cp_model.OPTIMAL:
    # Create one list of assigned tasks per machine.
    assigned_jobs = collections.defaultdict(list)
    for job_id, job in enumerate(jobs_data):
        for task_id, task in enumerate(job):
            machine = task[0]
            assigned_jobs[machine].append(
                assigned_task_type(start=solver.Value(
                    all_tasks[job_id, task_id].start),

samirsaci

import collections

# Import Python wrapper for or-tools CP-SAT solver.
from ortools.sat.python import cp_model

# Create the model.
model = cp_model.CpModel()

# Machine id
dict_machine = {0:'Machine 1', 1:'Machine 2', 2:'Machine 3'}

samirsaci

import pandas as pd
import numpy as np

# Import Dataframe
df = pd.read_csv('sales_data.csv', sep=";")
print("{:,} transactions in your raw data".format(len(df)))

# Format Date
print("Start Processing ...")
df['date'] = pd.to_datetime(df['date'])

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import pandas as pd
import numpy as np
from rectpack import newPacker
import rectpack.packer as packer
import matplotlib.pyplot as plt

# Initialize Model Parameters

#-- Pallet Dimensions: 80 x 120 cm
bx = 5 # Buffer x

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# Function Solver
def solver(n_812, n_1012, bins):
    # Pallets to load
    rectangles = [pal_812 for i in range(n_812)] + [pal_1012 for i in range(n_1012)]
    
    # Build the Packer
    pack = newPacker()

    # Add the rectangles to packing queue
    for r in rectangles:

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def plot_solution(all_rects, pal_812, pal_1012):
    # Plot
    plt.figure(figsize=(10,10))
    # Loop all rect
    for rect in all_rects:
        b, x, y, w, h, rid = rect
        x1, x2, x3, x4, x5 = x, x+w, x+w, x, x
        y1, y2, y3, y4, y5 = y, y, y+h, y+h,y

        # Pallet type

samirsaci

import pandas as pd

# Dictionnary of filenames 
list_files = ['DC-JAN-2017.xlsx', 'DC-FEB-2017.xlsx', 'DC-MAR-2017.xlsx','DC-APR-2017.xlsx', 'DC-MAY-2017.xlsx', 'DC-JUN-2017.xlsx',
             'DC-JUL-2017.xlsx', 'DC-AUG-2017.xlsx', 'DC-SEP-2017.xlsx','DC-OCT-2017.xlsx', 'DC-NOV-2017.xlsx', 'DC-DEC-2017.xlsx']
zip_loop = zip(list_files, [i[3:6] for i in list_files])

# Final report DataFrame
df_report = pd.DataFrame()

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# Function to open and clean
def clean(file_raw, month):
    # Open the file and start from line 5
    df_raw = pd.read_excel(file_raw, header = 5)
    df_raw.head()

    # Remove First Lines
    df_clean = df_raw.copy()
    df_clean = df_clean.iloc[4:]

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def process_month(df_clean, month):

    # Type
    df_clean['Type'] = df_clean[['Renting', 'Investment']].apply(
        lambda t: 'Rent.' if t['Renting']=='X' else 'Invest.' if t['Investment']=='X' else 'Purch.', axis = 1)

    # Quantity 
    dict_qty = dict(zip(['Rent.', 'Purch.', 'Invest.'], ['Rental Units', 'Purchasing Units', 'Invests. Units']))
    df_clean['Qty'] = df_clean.apply(lambda t: t[dict_qty[t['Type']]], axis = 1)

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from pulp import *

# Optimize your Bakery Model 

# Parameters 
items = ["A", "B", "C", "D", "E", "F"]
profit = {"A":6, "B":4.4, "C":7.5, "D":0.9, "E":1.2, "F":2.2}
baker = {"A":50, "B":0, "C":45, "D":35, "E":25, "F":0}
oven = {"A":45, "B":0, "C":90, "D":20, "E":45, "F":0}
display = {"A":4, "B":1.5, "C":3, "D":1, "E":1, "F":1}