Solid waste collection Optimization

Requirements

• asttokens==2.0.5
• backcall==0.2.0
• cycler==0.11.0
• debugpy==1.6.0
• decorator==5.1.1
• entrypoints==0.4
• executing==0.8.3
• fonttools==4.33.3
• gurobipy==9.5.1
• ipykernel==6.13.0
• ipython==8.3.0
• jedi==0.18.1
• jupyter-client==7.3.1
• jupyter-core==4.10.0
• kiwisolver==1.4.2
• matplotlib==3.5.2
• matplotlib-inline==0.1.3
• nest-asyncio==1.5.5
• numpy==1.22.3
• opencv-python==4.5.5.64
• packaging==21.3
• pandas==1.4.2
• parso==0.8.3
• pexpect==4.8.0
• pickleshare==0.7.5
• Pillow==9.1.0
• prompt-toolkit==3.0.29
• psutil==5.9.0
• ptyprocess==0.7.0
• pure-eval==0.2.2
• Pygments==2.12.0
• pyparsing==3.0.9
• python-dateutil==2.8.2
• pytz==2022.1
• pyzmq==22.3.0
• six==1.16.0
• stack-data==0.2.0
• tornado==6.1
• traitlets==5.2.0
• wcwidth==0.2.5

General Idea

• We are using linear programming optimization in gurobipy to solve for optimal garbage collection.
• As distances matrix is in meters (huge number) whereas fill ratio is between 0 and 1, it gives us weight of distance as 0 as removing weight will minimize the function most. To counter this bug we normalize all distances by dividing distance in each row with that rows maximum value.
• Time between each dynamic computation is taken as 15 minutes (900s).
• Speed of each truck is taken as 50 km/hour (13.88 m/s).
• For 2 trucks in each ward total coverage is in range 35 to 45
• For 3 trucks in each ward total coverage is in range 53 to 66
• For 4 trucks in each ward total coverage is in range 78 to 85
• For 5 trucks in each ward we have total coverage above 90 for all wards

Folder structure and files

• Chandigarh QGIS : It contains all files related to QGIS we have used.
• Data : It contains all data which we are using for our computation.
• Bin Locations.csv : They are randomly generated points in QGIS and clustered using K-Means. The depot is assigned ward -1.
• distances.csv : This contains the distance matrix of all points in same ward or from depot.
• Static Data : Contains data regarding Static cases
• Dynamic Data : Contains Data regarding Dynamic cases
• Visited Truck # : Visited truck list with fill ratio
• Truck # Data : Each truck data used for computation.
• Statistics.csv : Stats for the respective case.

Python Files

• get_distance.py : Run this to get distance matrix
• static_function.py : Static Optimization code
• dynamic_function.py : Dynamic Optimization code
• multi_truck_function.py : Multiple trucks dynamic optimization function. It works only till 3 trucks in each ward.
• four_plus_truck_function.py : Function to perform dynamic optimzation on 4+ trucks
• four_plus_truck_worst_case_function.py : Function to perform dynamic optimzation on 4+ trucks in worst case
• static_unweighted.py : Static unweighted optimization code
• static_find_weights.py : Finding the wieghts for static case
• dynamic_unweighted.py : Dynamic unweighted optimization code
• dynamic_weight_finding.py : Finding weights for dynamic case.
• dynamic_worst_case.py : Computing dynamic worst case optimization
• dynamic_weighted_multiple_trucks.py : Dynamic weighted truck optimization where there are 2 trucks in each ward.
• dynamic_weighted_three_trucks.py : Performing Dynamic optimization of 3 trucks in each ward.
• dynamic_weighted_four_trucks.py : Performing Dynamic optimization of 4 trucks in each ward.
• dynamic_weighted_multi_best_case.py : Best collection ratio for dynamic case (5 Trucks)
• dynamic_weighted_multi_worst_case.py : Worst collection ratio for dynamic case (5 Trucks)
• show_routes.py : For visualizing routes.