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more details command line - C:\Users\Tech\AppData\Local\Programs\Python\Python311\Lib\site-packages\pulp\solverdir\cbc\win\64\cbc.exe C:\Users\Tech\AppData\Local\Temp\a542aeb3f5d6408398d37a281507c36e-pulp.mps timeMode elapsed branch printingOptions all solution C:\Users\Tech\AppData\Local\Temp\a542aeb3f5d6408398d37a281507c36e-pulp.sol (default strategy 1) Traceback (most recent call last): |
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looks like you have a divide by zero in your model code, which is putting NAN in the generated lp file. I suggest removing constraints until you find the one causing the problem then fixing it. |
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this is the optimization part Define linear problemlp_problem = LpProblem('CFLP', sense=LpMinimize) Variable: y_j (constraint: it is binary)created_warehouse = LpVariable.dicts( Variable: x_ijserved_customer = LpVariable.dicts( Objective functionobjective = lpSum(annual_cost_dict[j] * created_warehouse[j] for j in warehouse_df['warehouse_id']) + lp_problem += objective Constraint: the demand must be metfor i in customer_df['customer_id']: Constraint: a warehouse cannot deliver more than its capacity limitfor j in warehouse_df['warehouse_id']: Constraint: a warehouse cannot give a customer more than its demandfor i in customer_df['customer_id']: |
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this is full code import pandas as pd import matplotlib.pyplot as plt import pandas as pd cities_df = pd.read_csv("C:/Users/Tech/Desktop/New folder/raw_data/konum_il.csv", usecols=["plaka", "il_adi", "lat", "lon"]) cities_df.rename(columns = {'plaka':'city_code', 'il_adi':'name'}, inplace = True) cities_df districts_df def my_capitalize(text): Capitalize city and district namescities_df['name'] = cities_df['name'].apply(lambda x: my_capitalize(x)) districts_df = districts_df.loc[~districts_df['name'].isin(cities_df['name'])].reset_index(drop=True) temp_list = [] districts_df district_populations = pd.read_csv("C:/Users/Tech/Desktop/New folder/raw_data/nüfus_ilçe.csv", usecols=["İlçe", "Nüfus"]) district_populations = district_populations.loc[~district_populations['name'].str.contains("Merkez")].reset_index(drop=True) split the string at the first occurrence of the left parenthesisdistrict_populations['name'] = district_populations['name'].apply(lambda x: x.split('(', 1)[0] + '_' + x.split('(', 1)[1].split(')')[0] if '(' in x else x) print the updated dataframedistricts_df = districts_df.merge(district_populations[['name', 'population']], how='left') city_populations = pd.read_csv("C:/Users/Tech/Desktop/New folder/raw_data/nüfus_il.csv", usecols=["İl", "Nüfus"], dtype={'Nüfus':'int'}) split the string at the first occurrence of the left parenthesiscity_populations['name'] = city_populations['name'].apply(lambda x: x.split('-',1)[0]) print the updated dataframecities_df = cities_df.merge(city_populations[['name', 'population']], how='left') dataset_df = pd.concat([cities_df, districts_df]).reset_index(drop=True) region_dict = { city_centers = {} if "area" not in dataset_df.columns: radius_values = np.sqrt(dataset_df["area"]) / 4 dataset_df = dataset_df.iloc[selected_rows].reset_index(drop=True) Fraction of citizens of a city that may order a productFRACTION_DEMAND = 0.02 Demand is composed of:1. A fraction of the population2. An error term of uniform distributionNote: demand is approximated to the closest int as its physical meaning denies decimalsdataset_df['demand'] = np.floor(FRACTION_DEMAND * dataset_df.population + np.random.uniform(-10, 10, size=(dataset_df.shape[0],))) dataset_df = dataset_df[["city_code","name","region","lat","lon","population","demand"]] dataset_df.to_csv("C:/Users/Tech/Desktop/New folder/dataset/dataset.csv", index=False) dataset_df = pd.read_csv("C:/Users/Tech/Desktop/New folder/dataset/dataset.csv") RANDOM_STATE = 0 # For reproducibility Create the warehouses dataframe:1. Sample a fraction of the original citieswarehouse_df = dataset_df.sample(frac=FRACTION_WAREHOUSES, random_state=RANDOM_STATE, ignore_index=True) Create the customers dataframe:1. Sample a fraction of the original citiescustomer_df = dataset_df.sample(frac=FRACTION_CUSTOMERS, random_state=RANDOM_STATE+1, ignore_index=True) Customers IDs listcustomer_df['customer_id'] = range(1, 1 + customer_df.shape[0]) def add_geocoordinates(df, lat='lat', lon='lon'): customer_df = add_geocoordinates(customer_df) Prepare country plotworld = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) Extract and plot the shape of Italyturkey = world[world.name == 'Turkey'] Plot customers as pointscustomer_df. Plot potential warehouse locations as pointswarehouse_df. Add legendplt.legend(facecolor='white', title='Konum') Add titleplt.title('***********************') Remove ticks from axisplt.xticks([]) Show plotplt.show() Prepare region dataframe:1. Group by region2. Calculate:- Mean regional latitude- Mean regional longitude- Sum of regional demandregion_df = dataset_df. Add geo-coordinatesregion_df = add_geocoordinates(region_df) Plot the shape of Italyax = turkey.plot(color='white', edgecolor='black', figsize=(16, 16)) Plot region area colored based on demandregion_df. Add region 'center' as red dotsregion_df. Add region name above the centerfor i, row in region_df.iterrows(): Add color bar with demand scaleplt.colorbar(ax.get_children()[1], ax=ax, label='Yıllık Talep', fraction=0.02, pad=0.02) Add titleplt.title('************') Show plotplt.show() Dictionary of cutomer id (id) and demand (value)demand_dict = { customer : customer_df['demand'][i] for i, customer in enumerate(customer_df['customer_id']) } Assumptions:1. Each warehouse has an annual rental cost of 500.000,00 TL1. Each warehouse has an annual personel cost of 700.000,00 TL2. Each warehouse can meet 3 times the regional average annual demandRENTAL_COST_PER_WAREHOUSE = 500_000 Warehouses listwarehouse_df['warehouse_id'] = [f'Ambar {i}' for i in range(1, 1 + warehouse_df.shape[0])] Dictionary of warehouse id (id) and max supply (value)annual_supply_dict = { warehouse : SUPPLY_PER_WAREHOUSE for warehouse in warehouse_df['warehouse_id'] } Dictionary of warehouse id (id) and fixed costs (value)annual_cost_dict = {} For each warehouse locationfor i in range(0, warehouse_df.shape[0]): Calculate the distance in km between two locations given latitude and longitude.def haversine(lat1, lon1, lat2, lon2): Dict to store the distances between all warehouses and customerstransport_costs_dict = {} For each warehouse locationfor i in range(0, warehouse_df.shape[0]): Define linear problemlp_problem = LpProblem('CFLP', LpMinimize) Variable: y_j (constraint: it is binary)created_warehouse = LpVariable.dicts( Variable: x_ijserved_customer = LpVariable.dicts( Objective functionlp_problem += lpSum(annual_cost_dict[j]*created_warehouse[j] for j in warehouse_df['warehouse_id']) + lp_problem += objectiveCostraint: the demand must be metfor i in customer_df['customer_id']: Constraint: a warehouse cannot deliver more than its capacity limitfor j in warehouse_df['warehouse_id']: Constraint: a warehouse cannot give a customer more than its demandfor i in customer_df['customer_id']: lp_problempulp_solver = pulp.CBC_CMD(path="C:\Users\Tech\AppData\Local\Programs\Python\Python311\Lib\site-packages\pulp\solverdir\cbc\win\64\cbc.exe") print('Solution: ', LpStatus[lp_problem.status]) List of the values assumed by the binary variable created_warehousewarehouse_values = [i.varValue for i in created_warehouse.values()] Count of each distinct value of the list[[i, warehouse_values.count(i)] for i in set(warehouse_values)] Create dataframe column to store whether to build the warehouse or notwarehouse_df['build_warehouse'] = '' Assign Yes/No to the dataframe column based on the optimization binary variablefor i in warehouse_df['warehouse_id']: warehouse_df.build_warehouse.value_counts().sort_index(axis=0, ascending=False).plot.barh( for i, v in enumerate(warehouse_df.build_warehouse.value_counts().sort_index(axis=0, ascending=False)): function to add value labelsdef addlabels(x,y): colors = ['#19376D', '#576CBC'] x = ["Şehir İçine Kurulan Ambar Sayısı", "İlçelere Kurulan Ambar Sayısı"] plt.bar(x,y, color = colors) Plot the shape of Italyax = turkey.plot(color='white', edgecolor='black', figsize=(10, 10)) Plot sites to establishwarehouse_df. Plot sites to discardwarehouse_df. Add titleplt.title('Optimize Edilmiş Ambar Alanları') Add legendplt.legend(title='Ambar Alanı', facecolor='white') plt.xticks([]) Show plotplt.show() #Find customer ids that are served by the input warehouse. Warehouses to establishestablish = warehouse_df.loc[warehouse_df.build_warehouse =='Evet'] Plot the shape of Italyax = turkey.plot(color='white', edgecolor='black', figsize=(30, 30)) Plot sites to establishestablish. Plot customerscustomer_df. For each warehouse to buildfor _, row in establish.iterrows(): Add titleplt.title('Optimize Edilmiş Müşteri Tedariği', fontsize = 35) Add legendplt.legend(facecolor='white', fontsize=30) plt.xticks([]) Show plotplt.show() |
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in this bart in my code
lp_problem.solve()
the error
PulpSolverError Traceback (most recent call last)
Cell In[28], line 1
----> 1 lp_problem.solve()
File c:\Users\Tech\AppData\Local\Programs\Python\Python311\Lib\site-packages\pulp\pulp.py:1913, in LpProblem.solve(self, solver, **kwargs)
1911 # time it
1912 self.startClock()
-> 1913 status = solver.actualSolve(self, **kwargs)
1914 self.stopClock()
1915 self.restoreObjective(wasNone, dummyVar)
File c:\Users\Tech\AppData\Local\Programs\Python\Python311\Lib\site-packages\pulp\apis\coin_api.py:137, in COIN_CMD.actualSolve(self, lp, **kwargs)
135 def actualSolve(self, lp, **kwargs):
136 """Solve a well formulated lp problem"""
--> 137 return self.solve_CBC(lp, **kwargs)
File c:\Users\Tech\AppData\Local\Programs\Python\Python311\Lib\site-packages\pulp\apis\coin_api.py:213, in COIN_CMD.solve_CBC(self, lp, use_mps)
211 pipe.close()
212 if not os.path.exists(tmpSol):
--> 213 raise PulpSolverError("Pulp: Error while executing " + self.path)
214 (
215 status,
216 values,
(...)
220 sol_status,
221 ) = self.readsol_MPS(tmpSol, lp, vs, variablesNames, constraintsNames)
222 lp.assignVarsVals(values)
PulpSolverError: Pulp: Error while executing c:\Users\Tech\AppData\Local\Programs\Python\Python311\Lib\site-packages\pulp\solverdir\cbc\win\64\cbc.exe
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