results.py

import pandas as pd
from parameters import Parameters

''' results.py '''

class Results:
    def __init__(self, model, data:Parameters):
        self.model = model

        self.T = data.T
        self.F = data.F
        self.S = data.S
        self.FT = data.FT
        self.MP = data.MP
        self.CT = data.CT
        self.L = data.L

        self.sales_t = self.create_sales_t()
        self.lost_sales_t = self.create_lost_sales_t()
        self.distressed_sales_t = self.create_distressed_sales_t()
        self.raw_material_losses_t = self.create_raw_material_losses_t()
        self.raw_material_purchase_t = self.create_raw_material_purchase_t()
        self.exports_t = self.create_exports_t()
        self.production_t = self.create_production_t()
        self.product_shipped_t = self.create_product_shipped_t()
        self.sales_income_mu = self.create_sales_income_mu()
        self.distressed_sales_mu = self.create_distressed_sales_mu()
        self.raw_material_losses_cost_mu = self.create_raw_material_losses_cost_mu()
        self.raw_material_purchase_cost_mu = self.create_raw_material_purchase_cost_mu()
        self.exports_income_mu = self.create_exports_income_mu()
        self.cost_shipped_dc_mu = self.create_cost_shipped_dc_mu()
        self.setups_cost_mu = self.create_setups_cost_mu()
        self.expected_net_benefits_mu = self.create_expected_net_benefits_mu()

        pd.set_option('display.width', 100)

        pass

    def paper_values_table6(self):
        data = {
            'Metric': ['Sales [t]', 'Lost Sales [t]', 'Distressed Sales of Products [t]', 'Raw Material Losses [t]', 'Raw Material Purchase [t]', 'Exports [t]', 'Production [t]', 'Product shipped to DC [t]', 'Sales Income [MU]', 'Distressed Sales of Products [MU]', 'Raw Material Losses Cost [MU]', 'Raw Material Purchase Cost [MU]', 'Exports Income [MU]', 'Cost for shipped to DC [MU]', 'Setups Cost [MU]', 'Expected Net Benefits [MU]'],
            'SP': [12815, 1715.2, 3694.1, 22951, 1078.4, 975, 16561, 15556, 62241, 3516, 22951, 2156.90, 4875, 10814, 40, 34670.1],
            'EMVP': [11149, 3381, 1410.2, 20334, 4471.9, 2500, 14460, 11607, 53541, 752, 20334, 8943.7, 12500, 8798.5, 80, 28636.8],
            'Deviation': ['+14.9%', '-49.3%', '+162.0%', '+12.9%', '-75.9%', '-61.0%', '+14.5%', '+34.0%', '+16.2%', '+367.5%', '+12.9%', '-75.9%', '-61.0%', '+22.9%', '-50.0%', '+21.1%']
        }
        df = pd.DataFrame(data)
        return df


    def paper_values_table8(self):
        data = {
            'Metric': ['Production [t]', 'Production [t]', 'Production [t]', 'Production [t]', 
                       'N° of PM Export Lots', 'Processed Raw Milk [t]', 
                       'Required trucks', 'Required trucks',  'Required trucks', 
                       'DCs Expected Stockout days', 'DCs Expected Stockout days', 'DCs Expected Stockout days', 'DCs Expected Stockout days', 
                       'DCs Expected Overstock days', 'DCs Expected Overstock days', 'DCs Expected Overstock days', 
                       'Expected number of days with raw milk Overstock', 
                       'Expected number of days with raw milk bought at premium price', 
                       'Expected Sales (MU)', 'Expected Raw Milk Costs (MU)', 'Expected Net Benefits (MU)'] ,
            'Sub-Metric': ['UHT', 'Milk', 'Yogurt', 'Cheese', 
                           'N° of PM Export Lots', 'Processed Raw Milk [t]', 
                           'Fresh', 'Dry', 'UHT', 
                           'Powdered Milk', 'Yogurt', 'Cheese', 'UHT', 
                           'Powdered Milk', 'Yogurt', 'Cheese', 
                           'Raw Milk', 'Raw Milk', 
                           'Sales', 'Raw Milk', 'Net Benefits'] ,
            'SP':   [9881.6, 1938, 4465.3, 276.4, 39,   33759, 598, 1348,  9.7,  5.5, 10,    6.7, 3.5, 3.8,  6.4, 2.3, 10.5, 2.2, 62241, 25107, 34670.1],
            'EMVP': [6600,   3360, 4499.8, None,  100, 397769, 554,  910, 20.2, 16.1, 13.1, 29.7, 7.9, 4.8, 18.1, 1.2, 12.1, 9,   53541, 29277, 28636.8]
        }
        
        df = pd.DataFrame(data)
        df.set_index(['Metric', 'Sub-Metric'], inplace=True, drop=True)

        return df
        #df = pd.DataFrame(data)
        #return df

    def create_sales_t(self):
        # Compute the value for 'Sales [t]'

        sales_t = 0


        for s in self.S:
            for f in self.F:
                for l in self.L:
                    for t in self.T:
                        if self.model.getVarByName(f'SAs_f_l_t[{s},{f},{l},{t}]') is not None:
                            sales_t += self.model.getVarByName(f'SAs_f_l_t[{s},{f},{l},{t}]').Obj


        return sales_t

    def create_lost_sales_t(self):
        # Compute the value for 'Lost Sales [t]'

        lost_sales_t = 0


        for s in self.S:
            for f in self.F:
                for l in self.L:
                    for t in self.T:
                        if self.model.getVarByName(f'SOs_f_l_t[{s},{f},{l},{t}]') is not None:
                            lost_sales_t += self.model.getVarByName(f'SOs_f_l_t[{s},{f},{l},{t}]').Obj

        print(lost_sales_t)

        return lost_sales_t

    def create_distressed_sales_t(self):
        # Compute the value for 'Distressed Sales of Products [t]'

        distressed_sales_t = 0

        for s in self.S:
            for f in self.F:
                for l in self.L:
                    for t in self.T:
                        if self.model.getVarByName(f'OSs_f_l_t[{s},{f},{l},{t}]') is not None:
                            distressed_sales_t += self.model.getVarByName(f'OSs_f_l_t[{s},{f},{l},{t}]').Obj

        return distressed_sales_t

    def create_raw_material_losses_t(self):
        # Compute the value for 'Raw Material Losses [t]'

        raw_material_losses_t = 0

        for s in self.S:
            for t in self.T:
                if self.model.getVarByName(f'ROs_t[{s},{t}]') is not None:
                    raw_material_losses_t += self.model.getVarByName(f'ROs_t[{s},{t}]').Obj
        return raw_material_losses_t

    def create_raw_material_purchase_t(self):
        # Compute the value for 'Raw Material Purchase [t]'

        raw_material_purchase_t = 0

        for s in self.S:
            for t in self.T:
                if self.model.getVarByName(f'RSs_t[{s},{t}]') is not None:
                    raw_material_purchase_t += self.model.getVarByName(f'RSs_t[{s},{t}]').Obj

        return raw_material_purchase_t

    def create_exports_t(self):
        # Compute the value for 'Exports [t]'

        exports_t = 0

        for f in self.F:
            for t in self.T:
                if self.model.getVarByName(f'Ef_t[{f},{t}]') is not None:
                    exports_t += self.model.getVarByName(f'Ef_t[{f},{t}]').Obj

        return exports_t

    def create_production_t(self):
        # Compute the value for 'Production [t]'

        production_t = 0

        for f in self.F:
            for t in self.T:
                if self.model.getVarByName(f'FPf_t[{f},{t}]') is not None:
                    production_t += self.model.getVarByName(f'FPf_t[{f},{t}]').Obj

        return production_t

    def create_product_shipped_t(self):
        # Compute the value for 'Product shipped to DC [t]'

        product_shipped_t = 0

        for f in self.F:
            for l in self.L:
                for t in self.T:
                    if self.model.getVarByName(f'DVf_l_t[{f},{l},{t}]') is not None:
                        product_shipped_t += self.model.getVarByName(f'DVf_l_t[{f},{l},{t}]').Obj

        return product_shipped_t

    def create_sales_income_mu(self):
        # Compute the value for 'Sales Income [MU]'

        sales_income_mu = 0

        sales_income_mu = self.model.getVarByName('EXI').Obj

        return sales_income_mu

    def create_distressed_sales_mu(self):
        # Compute the value for 'Distressed Sales of Products [MU]'

        distressed_sales_mu = 0

        return distressed_sales_mu

    def create_raw_material_losses_cost_mu(self):
        # Compute the value for 'Raw Material Losses Cost [MU]'
        # Replace the following line with your computation
        raw_material_losses_cost_mu = 1
        return raw_material_losses_cost_mu

    def create_raw_material_purchase_cost_mu(self):
        # Compute the value for 'Raw Material Purchase Cost [MU]'
        # Replace the following line with your computation
        raw_material_purchase_cost_mu = 1
        return raw_material_purchase_cost_mu

    def create_exports_income_mu(self):
        # Compute the value for 'Exports Income [MU]'
        # Replace the following line with your computation
        exports_income_mu = 1
        return exports_income_mu

    def create_cost_shipped_dc_mu(self):
        # Compute the value for 'Cost for shipped to DC [MU]'
        # Replace the following line with your computation
        cost_shipped_dc_mu = 1
        return cost_shipped_dc_mu

    def create_setups_cost_mu(self):
        # Compute the value for 'Setups Cost [MU]'
        # Replace the following line with your computation
        setups_cost_mu = 1
        return setups_cost_mu

    def create_expected_net_benefits_mu(self):
        # Compute the value for 'Expected Net Benefits [MU]'
        # Replace the following line with your computation
        expected_net_benefits_mu = 1
        return expected_net_benefits_mu
    
    def calculate_deviation(self, value, comparison):
        print(comparison)
        if (comparison == int(0)) or comparison == float(0):
            return 'N/A'
        return f'+{round((value - comparison) / comparison * 100, 1)}%'

    def PrintResults(self, table6, table8):
        print('=========================================')
        print('Results:')
        print('Objective value: %g' % self.model.objVal)
        print('table 6:')
        print(table6)

        print('table 8:')
        print(table8)

    

    def ComputeResultsOfTable6(self):

        data = {'SP':  [self.sales_t, 
                        self.lost_sales_t,
                        self.distressed_sales_t,
                        self.raw_material_losses_t,
                        self.raw_material_purchase_t,
                        self.exports_t,
                        self.production_t,
                        self.product_shipped_t,
                        self.sales_income_mu,
                        self.distressed_sales_mu,
                        self.raw_material_losses_cost_mu,
                        self.raw_material_purchase_cost_mu,
                        self.exports_income_mu,
                        self.cost_shipped_dc_mu,
                        self.setups_cost_mu,
                        self.expected_net_benefits_mu]}
                        
        listOfResults = pd.DataFrame()
        listOfResults['SP'] = data['SP']

        data = {
            'Metric': ['Sales [t]', 'Lost Sales [t]', 'Distressed Sales of Products [t]', 'Raw Material Losses [t]', 'Raw Material Purchase [t]', 'Exports [t]', 'Production [t]', 'Product shipped to DC [t]', 'Sales Income [MU]', 'Distressed Sales of Products [MU]', 'Raw Material Losses Cost [MU]', 'Raw Material Purchase Cost [MU]', 'Exports Income [MU]', 'Cost for shipped to DC [MU]', 'Setups Cost [MU]', 'Expected Net Benefits [MU]'],
            'SP': [self.sales_t, self.lost_sales_t, self.distressed_sales_t, self.raw_material_losses_t, self.raw_material_purchase_t, self.exports_t, self.production_t, self.product_shipped_t, self.sales_income_mu, self.distressed_sales_mu, self.raw_material_losses_cost_mu, self.raw_material_purchase_cost_mu, self.exports_income_mu, self.cost_shipped_dc_mu, self.setups_cost_mu, self.expected_net_benefits_mu],
            'EMVP': [self.sales_t, self.lost_sales_t, self.distressed_sales_t, self.raw_material_losses_t, self.raw_material_purchase_t, self.exports_t, self.production_t, self.product_shipped_t, self.sales_income_mu, self.distressed_sales_mu, self.raw_material_losses_cost_mu, self.raw_material_purchase_cost_mu, self.exports_income_mu, self.cost_shipped_dc_mu, self.setups_cost_mu, self.expected_net_benefits_mu],
            'Deviation': [  self.calculate_deviation(self.sales_t, self.sales_t),
                            self.calculate_deviation(self.lost_sales_t, self.lost_sales_t),
                            self.calculate_deviation(self.distressed_sales_t, self.distressed_sales_t),
                            self.calculate_deviation(self.raw_material_losses_t, self.raw_material_losses_t),
                            self.calculate_deviation(self.raw_material_purchase_t, self.raw_material_purchase_t),
                            self.calculate_deviation(self.exports_t, self.exports_t),
                            self.calculate_deviation(self.production_t, self.production_t),
                            self.calculate_deviation(self.product_shipped_t, self.product_shipped_t),
                            self.calculate_deviation(self.sales_income_mu, self.sales_income_mu),
                            self.calculate_deviation(self.distressed_sales_mu, self.distressed_sales_mu),
                            self.calculate_deviation(self.raw_material_losses_cost_mu, self.raw_material_losses_cost_mu),
                            self.calculate_deviation(self.raw_material_purchase_cost_mu, self.raw_material_purchase_cost_mu),
                            self.calculate_deviation(self.exports_income_mu, self.exports_income_mu),
                            self.calculate_deviation(self.cost_shipped_dc_mu, self.cost_shipped_dc_mu),
                            self.calculate_deviation(self.setups_cost_mu, self.setups_cost_mu),
                            self.calculate_deviation(self.expected_net_benefits_mu, self.expected_net_benefits_mu)],
            'SP-paper': self.paper_values_table6()['SP'],
            'EMVP-paper': self.paper_values_table6()['EMVP'],
            'Deviation-paper': self.paper_values_table6()['Deviation'],
            #'diff SP paper-model': self.paper_values_table6()['SP'] - listOfResults,
            #'diff EMVP paper-model': self.paper_values_table6()['EMVP'] - listOfResults,
            #'deviation SP paper-model': self.calculate_deviation(listOfResults['SP'], self.paper_values_table6()['SP']),
        }
        df = pd.DataFrame(data)
        return df

    def ComputeResultsOfTable8(self):    
        data = {'SP':  [self.sales_t, 
                        self.lost_sales_t,
                        self.distressed_sales_t,
                        self.raw_material_losses_t,
                        self.raw_material_purchase_t,
                        self.exports_t,
                        self.production_t,
                        self.product_shipped_t,
                        self.sales_income_mu,
                        self.distressed_sales_mu,
                        self.raw_material_losses_cost_mu,
                        self.raw_material_purchase_cost_mu,
                        self.exports_income_mu,
                        self.cost_shipped_dc_mu,
                        self.setups_cost_mu,
                        self.expected_net_benefits_mu]}
                        
        listOfResults = pd.DataFrame()
        listOfResults['SP'] = data['SP']

        data = {
            'SP-paper': self.paper_values_table8()['SP'],
            'EMVP-paper': self.paper_values_table8()['EMVP'],
        }

        df = pd.DataFrame(data, index = self.paper_values_table8().index)
        return df


    def Evaluate_results(self):
        table6 = self.ComputeResultsOfTable6()
        table8 = self.ComputeResultsOfTable8()
        self.PrintResults(table6, table8)
        pass