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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.